**Study of State Demographic, Economic, and Programmatic Variables and
Their Impact on the Performance-Based Child Support Incentive System**

Final Report

Prepared for:\
Department of Health and Human Services\
Office of Child Support Enforcement

Prepared by:\
John Tapogna, ECONorthwest

Karen Gardiner, The Lewin Group

Burt Barnow, Johns Hopkins University

Michael Fishman, The Lewin Group

Plamen Nikolov, The Lewin Group

August 2003

Table of Contents

**Executive Summary [1](#executive-summary)**

A. Background [1](#a.-background)

B. Purpose of the Study [2](#b.-purpose-of-the-study)

C. Findings [3](#c.-findings)

I. Introduction [6](#i.-introduction)

A. Background on Incentive Payment Methods
[6](#a.-background-on-incentive-payment-methods)

B. Purpose of the Study [7](#b.-purpose-of-the-study-1)

C. Organization of the Report [8](#c.-organization-of-the-report)

II\. Study method and limitations [9](#ii.-study-method-and-limitations)

A. Method [9](#a.-method)

B. Selection of Study Variables [10](#b.-selection-of-study-variables)

1\. Literature Review [10](#literature-review)

2\. Expert Discussions [12](#expert-discussions)

3\. Data Assembly and Model Development
[13](#data-assembly-and-model-development)

C. Limitations [15](#c.-limitations)

III\. Description of the study variables
[17](#iii.-description-of-the-study-variables)

A. Performance Measures (Dependent Variables)
[17](#a.-performance-measures-dependent-variables)

1\. Paternity Establishment Percentage (PEP)
[17](#paternity-establishment-percentage-pep)

2\. Percentage of IV-D Cases with Orders for Support
[18](#percentage-of-iv-d-cases-with-orders-for-support)

3\. IV-D Collection Rate for Current Support
[19](#iv-d-collection-rate-for-current-support)

4\. Percentage of IV-D Cases with Collections on Arrears
[19](#percentage-of-iv-d-cases-with-collections-on-arrears)

B. Factors Associated with Performance (Explanatory Variables)
[20](#b.-factors-associated-with-performance-explanatory-variables)

1\. Economic Variables [21](#economic-variables)

2\. Demographic Variables [22](#demographic-variables)

3\. Programmatic Variables [23](#programmatic-variables)

IV\. study results [26](#iv.-study-results)

A. Simple Correlations [26](#a.-simple-correlations)

1\. Correlations Among Dependent Variables
[26](#correlations-among-dependent-variables)

2\. Correlations Between Explanatory Variables and Dependent Variables
[27](#correlations-between-explanatory-variables-and-dependent-variables)

3\. Correlations among Explanatory Variables
[28](#correlations-among-explanatory-variables)

B. Multivariate Regression Analysis
[28](#b.-multivariate-regression-analysis)

1\. Factors Associated with Cases with Orders
[30](#factors-associated-with-cases-with-orders)

2\. Factors Associated with Percentage of Current Support Collected
[31](#factors-associated-with-percentage-of-current-support-collected)

3\. Factors Associated with Cost-Effectiveness
[32](#factors-associated-with-cost-effectiveness)

4\. Factors Associated with Percent of Cases Paying Toward Arrears
[33](#factors-associated-with-percent-of-cases-paying-toward-arrears)

5\. Factors Associated with Paternity Establishment
[34](#factors-associated-with-paternity-establishment)

6\. Conclusions [35](#conclusions)

V. Applying study results to incentive policy
[38](#v.-applying-study-results-to-incentive-policy)

A. Rationale for Adjustments [38](#a.-rationale-for-adjustments)

B. Approaches for Making Adjustments
[39](#b.-approaches-for-making-adjustments)

C. lllustrative Adjustments [41](#c.-lllustrative-adjustments)

1\. Cases with Orders [41](#cases-with-orders)

2\. Percent of Current Support Paid
[44](#percent-of-current-support-paid)

3\. Percent of Cases Paying Toward Arrears
[48](#percent-of-cases-paying-toward-arrears)

4\. Cost-effectiveness [51](#cost-effectiveness-1)

D. Conclusions [54](#d.-conclusions)

Vi. Summary of major findings [56](#vi.-summary-of-major-findings)

A. Summary [56](#a.-summary)

B. Key Findings [57](#b.-key-findings)

# Executive Summary

## A. Background

Since 1975, the federal government has paid incentives to state child
support enforcement programs to encourage improvement in collections
through efficient establishment and enforcement techniques.[^1] The
method used to determine incentive payments has changed dramatically
since 1998. Between 1984 and 1998, the federal government based a
state's incentives payment on a percentage of their TANF and non-TANF
collections. The percentage of incentives paid was determined by
measurement of state program cost-effectiveness---defined as a state's
total collections divided by its total administrative costs. States
received a payment equal to at least six percent of collections.[^2]
High-performing states (i.e., those with a collection-to-cost ratio of
at least 2.8 to 1) could receive a payment equal to 10 percent of
collections.

In 1998, Congress enacted the Child Support Performance and Incentive
Act (CSPIA) to revise the incentive structure and reward states for
performance on a larger number of their establishment and enforcement
practices. Specifically, Congress linked incentive payments to a state's
performance in five areas:

-   Paternity establishment (states can choose between one of two
    measures: paternity establishment statewide or specific to the IV-D
    caseload);

-   Establishment of child support orders;

-   Collections on current support due;

-   Cases with collections on arrears (past support due);

-   Cost-effectiveness (i.e., total collections divided by total
    administrative costs).

Other key elements of the new incentive system include:

-   **Capped pool of incentives**. The overall payment pool is capped.
    The incentive pool was \$422 million in fiscal year (FY) 2000. The
    capped system creates an interactive effect because an increase in
    payments to one state must be matched by a decrease in others.

-   **State incentive potential related to collection levels.**
    Incentive payments are a function of the state collection base,
    which is child support collected for current and former TANF cases
    multiplied by two plus the collection amount for cases never on
    TANF.

-   **Performance corresponds to an incentive percentage.** To calculate
    the incentive payment, state performance on each measure corresponds
    to an incentive percentage (e.g., if a state has established orders
    for 80 percent or more of the cases in its system, it would receive
    100 percent of the cases with orders incentive payment).

-   **Audited data underlie system**. Data used to calculate incentives
    must be complete and reliable, as determined by an audit. If an
    audit finds that data is not complete and reliable for a given
    measure, the state receives zero payments for that measure.

The federal Office of Child Support Enforcement (OCSE) has implemented
the new incentive formula gradually over the fiscal year (FY) 2000-2002
period. Policymakers called for the gradual phase-in, in part, so state
officials would have time to perfect their measurement of performance
and data reliability, and identify factors that affect both.

A 1999 study by the Lewin Group and ECONorthwest documented the
importance of incentive payments to the state financing of child support
enforcement. While state practices regarding the use of incentives were
largely unknown before 1997, Fishman et. al.[^3] showed that the
majority of states earmark incentive payments to the child support
enforcement (CSE) program. Indeed, CSPIA mandates such earmarking for
the few remaining states that did not do so historically. The study also
noted that incentive funds take on added importance because, when they
are used for child support expenditures, they are matched two for one by
Federal Financial Participation (FFP) funding. Therefore, a loss of one
incentive-dollar translates to a three-dollar loss of total program
funding.

The effectiveness of the new incentive system will hinge, in part, on
whether states perceive it to be fair; that is, whether they perceive a
clear tie between an improvement in performance and the amount of
incentive payments they receive. It will also rest on the perception
that states are not being "penalized" for factors beyond their control.
If state economic and demographic characteristics affect performance on
any of these measures, the new incentive system would reward states for
performance improvements inequitably as well as jeopardize state
acceptance of the system.

## B. Purpose of the Study

In passing CSPIA, Congress mandated a study of the economic and
demographic characteristics of states and how they affect performance,
calling on the Secretary of the Department of Health and Human Services
(DHHS) to recommend adjustments to ensure that the relative performance
of the states is measured from a baseline that takes account of such
variables. This study provides the underlying data for the Secretary's
report. Specifically, the study seeks to answer two questions:

1.  What economic, demographic, and programmatic factors are associated
    with the performance of state child support enforcement programs?

2.  If empirical work identifies factors that affect performance and are
    outside the control of child support agencies, how could DHHS amend
    the incentive system to account for the factors with the goal of
    improving the system's equity?

To address these questions, we assembled state-level data on over 50
economic, demographic, and programmatic variables that have theoretical
relationships with child support performance. Economic characteristics,
such as personal income per capita and employment rates, gauge the
relative ease that non-custodial and custodial parents will encounter in
securing and keeping jobs to support their children. Demographic
characteristics, including migration rates and urbanicity, indicate
populations that IV-D officials have identified as inherently easy or
difficult to serve. Finally, programmatic characteristics, like staffing
levels or degree of program universality, measure aspects of programs
determined, in large part, by state policy and funding decisions. With
these state-level data in hand, we then developed a number of
statistical models to explore and estimate the independent effects, if
any, of each of these theorized determinants of child support
performance. We developed a distinct statistical model for each of
CSPIA's five measures. We estimated the models using 1999 data, 2000
data, and pooled 1999 and 2000 data.

## C. Findings

Our final models relied on 12 economic, demographic and programmatic
variables. We employed different combinations of these variables to
predict each performance indicator; no model used all 12. The stability
and reliability of the models varied across the performance indicators.
For example, the model for cases with orders explained more than 73
percent of the variation in the performance scores reported by states.
On the other hand, our model for the statewide paternity establishment
indicator explained only 33 percent of the variation in state scores.

Below, we summarize the key findings that emerged from our analyses of
the pooled 1999 and 2000 data.

**A robust economy is associated with better performance.** We
ultimately settled on four economic indicators in our final models:
poverty rate, personal income per capita, job growth, and the employment
rate of working-age males. We included at least one of these indicators
in each of our final regression models, and they were statistically
significant in most cases. However, no single indicator performed well
across all models. Specifically:

-   A higher *poverty rate* was associated with weaker child support
    program performance on the cases with orders and arrearage measures.

-   *Per capita personal income* did a better job in predicting rates of
    current collections, with higher personal incomes linked to better
    performance.

-   A higher rate of *males not working* depressed performance on
    current collections and cost-effectiveness.

-   A higher rate of *job growth* was associated with better performance
    on the arrearage measure.

**Demographic factors play a role in state performance**. We explored
stability of the local population, percent of the population living in
urban areas, and the percent of TANF heads under age 30. We found:

-   A higher share of *urban dwellers* is associated with weaker
    performance in the four models in which it was used: cases with
    orders, current collections, cost-effectiveness and arrears. We
    speculate that the urban variable is serving as a proxy for a host
    of more specific characteristics that more directly influence child
    support outcomes (e.g., non-marriage births, crime, and
    incarceration rates).

-   *Population stability* exhibits strong relationships with the
    statewide paternity measure, cases with orders, current collections,
    and cost-effectiveness. We found that the more stable a state's
    population---as evidenced by the share who remain in the same house
    from one year to the next---the better is the state performance.

-   States with *younger TANF case heads* exhibited weaker performance
    for paternity establishment (IV-D measure), current collections, and
    cost-effectiveness.

**Several programmatic factors---determined by state and agency
policies---appear to be related to child support enforcement.** We
explored program universality, cases per full-time equivalent (FTE)
staff, child support expenditures per case, and the process for
establishing orders.

-   Our findings are consistent with the hypothesis that states that
    serve a large number of *non-TANF clients* should report better
    performance than programs that primarily serve current recipients of
    cash assistance. Specifically, we find that states with a higher
    share of IV-D cases receiving TANF exhibit weaker performance on the
    paternity (statewide), case with orders, current collections,
    arrearages, and cost-effectiveness measures.

-   We found that staff resources devoted to enforcement---expressed in
    terms of *cases per FTE* are also related to performance.
    Specifically, the lower is the ratio of cases to total program
    staff, the better is performance in the cases with orders and
    current collections measure.

-   Another measure of resources---*average IV-D expenditures per
    case*---is related to better performance on the paternity measure
    (statewide) but weakens the cost-effectiveness ratio.

-   *The process by which states establish child support orders* appears
    related to their performance on case with orders. Specifically,
    having an administrative processes is associated with better
    performance in order establishment.

**States will likely face tradeoffs in attempting to maximize overall
performance.** Officials will likely discover an inherent tradeoff
between cost-effectiveness and the other performance measures. For
example, if states increased staffing levels in an attempt to boost case
establishment or current collection rates, they would likely increase
spending per case, which could decrease their cost-effectiveness ratios.

**Adjustments to state performance scores would be feasible at this time
for four of the five indicators.** Using the findings from the models,
OCSE could adjust state performance scores for all but the paternity
establishment measure so as to hold states harmless for economic and
demographic factors that appear to be associated with child support
performance but over which program directors have no control. For
example, states with characteristics that are linked with weaker child
support enforcement performance (e.g., higher-than-average state poverty
rates, lower-than average per capita personal incomes, and high levels
of in- or out-migration) would see upward adjustments, while states with
strong economies and stable populations would receive downward
adjustments. The U.S. Department of Labor employed a similar type of
adjustment process in its allocation of funds under the Job Training
Partnership Act program. Advantages and disadvantages would be inherent
in an adjustment process.

-   Advantages include an increased perception of equity in the
    incentive funding system, particularly among states that perceive
    themselves as penalized by factors beyond their control (e.g., weak
    economies).

-   Disadvantages would stem from mistrust of the regression models, and
    their underlying data, employed to make the adjustments. Moreover,
    the process for determining state incentive payments is already long
    and complex. Adjusting state scores based on economic and
    demographic factors could lengthen the time of the process, thus
    delaying the payment of incentives. This is due to the interactive
    nature of the incentive system. A capped incentive pool means that
    an upward adjustment to one state would have to be matched by a
    same-size downward adjustment in other states.

**Further research will be necessary**. This study is based on two years
of data. The original modeling used FY 1999 data. We re-ran the
regressions using FY 2000 data and found that most, but not all, of the
relationships remained stable. Our strongest results were produced when
we increased the sample size by pooling FY 1999 and 2000 data. Further
studies should aim to replicate our findings. By using individual-year
data, researchers can explore whether the variables we identified as
significant factors in child support performance remain stable over
time. Combining the data for additional years would increase the sample
size further. At some point, it may be possible to model adjustments for
the paternity establishment measures.

# I. Introduction

## A. Background on Incentive Payment Methods

Since 1975, the federal government has paid incentives to state child
support enforcement programs to encourage improvement in collections
through efficient establishment and enforcement techniques.[^4] The
method used to determine incentive payments has changed dramatically
since 1998. Between 1984 and 1998, the federal government based a
state's incentives payment on a percentage of their TANF and non-TANF
collections. The percentage of incentives paid was determined by
measurement of the state program cost-effectiveness---defined as the
states total collections divided by its total administrative costs.
States received a payment equal to at least six percent of
collections.[^5] High-performing states (i.e., those with a collection
to cost ratio of at least 2.8 to 1) could receive a payment equal to 10
percent of collections.

In 1998, Congress enacted the Child Support Performance and Incentive
Act (CSPIA), to revise the incentive structure and reward states for
performance on a larger number of their establishment and enforcement
practices. Specifically, Congress linked incentive payments to a state's
performance in five areas:

-   Paternity establishment;

-   Establishment of child support orders;

-   Collections on current support due;

-   Cases with collections on arrears (past support due);

-   Cost effectiveness (i.e., total collections divided by total
    administrative costs).

Other key elements of the new incentive system include:

-   **Capped pool of incentives**. The overall payment pool is capped.
    The incentive pool is set at \$422 million for FY 2000, \$429
    million for FY 2001, \$450 million for FY 2002, \$461 million for FY
    2003, \$454 million for FY 2004, and increases to \$483 million by
    FY 2008. The capped system creates an interactive effect because an
    increase in payments to one state must be matched by a decrease in
    others.

-   **State incentive potential related to collection levels.**
    Incentive payments are a function of the state collection base,
    which is child support collected for current and former TANF cases
    multiplied by two plus the collection amount for cases never on
    TANF.

-   **Performance corresponds to an incentive percentage.** To calculate
    the incentive payment, state performance on each measure corresponds
    to an incentive percentage. For instance, if a state's support order
    performance level is 57 percent, it would receive 66 percent of the
    cases with orders incentive payment, assuming it passed the audit.

-   **Audited data underlie system.** Data used to calculate incentives
    must be complete and reliable, as determined by an audit. If an
    audit finds that data is not complete and reliable for a given
    measure, the state receives zero payments for that measure.

The federal Office of Child Support Enforcement (OCSE) has implemented
the new incentive formula gradually over the fiscal year (FY) 2000-2002
period. Policymakers called for the gradual phase-in, in part, so state
officials would have time to perfect their measurement of performance
and data reliability, and identify factors that affect both.

A 1999 study by the Lewin Group and ECONorthwest documented the
importance of incentive payments to the state financing of child support
enforcement. While state practices regarding the use of incentives were
largely unknown before 1997, Fishman et. al.[^6] showed that the
majority of states earmark incentive payments to the child support
enforcement (CSE) program. Indeed, CSPIA mandates such earmarking for
the few remaining states that did not do so historically. The study also
noted that incentive funds take on added importance because, when they
are used for child support expenditures, they are matched two for one by
Federal Financial Participation (FFP) funding. Therefore, a loss of one
incentive dollar translates to a three-dollar loss of total program
funding.

The effectiveness of the new incentive system will hinge, in part, on
whether states perceive it to be fair; that is, whether they perceive a
clear tie between an improvement in performance and the amount of
incentive payments they receive. It will also rest on the perception
that states are not being "penalized" for factors beyond their control.
If state economic and demographic characteristics affect performance on
any of these measures, the new incentive system would reward states for
performance improvements inequitably as well as jeopardize state
acceptance of the system.

## B. Purpose of the Study

In passing CSPIA, Congress mandated a study of the economic and
demographic characteristics of states and how they affect performance,
calling on the Secretary of the Department of Health and Human Services
(DHHS) to recommend adjustments to ensure that the relative performance
of the states is measured from a baseline that takes account of such
variables. This study provides the underlying data for the Secretary's
report. Specifically, the study seeks to answer two questions:

-   What economic, demographic, and programmatic factors are associated
    with the performance of state child support enforcement programs?

-   If empirical work identifies factors that affect performance *and*
    are outside the control of child support agencies, how could DHHS
    amend the incentive system to account for the factors with the goal
    of improving the system's equity?

In answering the first question, we expanded the scope of the project
beyond the original Congressional request and included an analysis of
programmatic factors---such as staffing levels and award establishment
processes. This was necessary because we needed to consider the
associations of *all* potential determinants of performance in order to
generate unbiased estimates of the effects of economic and demographic
factors. Underlying the study are the performance data reported by
states in FY 1999 and 2000. OCSE used the FY 1999 data as a baseline.
The FY 2000 incentive payments were based on a combination of the old
incentive formula (2/3 of the incentive payment) and the new formula
(1/3 of the payment).

We assembled state-level data on over 50 economic, demographic, and
programmatic variables that have theoretical relationships with child
support performance. The variables include state rates of poverty,
unemployment, non-marital births, migration and incarceration. We also
considered IV-D program spending and staff levels and other program
features that experts believe affect performance. We then developed a
number of statistical models to explore and estimate the independent
effects, if any, of each of these theorized determinants of child
support performance. We developed a distinct statistical model for each
of CSPIA's five measures. We then applied the models results to the
incentive policy. Specifically, we show how adjustments could be made to
state scores for each performance measure.

## C. Organization of the Report

In the remainder of this report, we describe the study's methodology and
limitations (***Section II***); describe the study variables (***Section
III***); report our results from our statistical analyses (***Section
IV***); apply our statistical findings to OCSE policy on incentive
payments (***Section V***); and summarize our key findings (***Section
VI***). Finally, within several appendices to this report, we provide
sources of existing information related to this topic as well as
state-specific performance data for the reader's reference.

# II. Study method and limitations

## A. Method

The study consisted of estimating the direct relationships between a
selection of *dependent* variables (that is, variables that measure
child support performance) and a host of *explanatory* variables that,
taken together, help explain variations in the dependent variables. As
discussed previously, OCSE directed us to use the five performance
measures enacted through CSPIA as the dependent variables. Below we
describe the key steps involved and discuss the list of variables that
program experts have recommended for inclusion.

Through a related Lewin/ECONorthwest study on this topic, *Preliminary
Assessment of the Association between State Child Support Enforcement
Performance and Financing Structure,* (Fishman, et al, 2000)[^7] we
identified multivariate regression analysis as the technique best suited
for this type of study. This statistical technique generates estimates
of the independent effect of a variety of factors on performance---while
holding other characteristics constant. For instance, a researcher might
ask:

> *"How would an increase in the state's poverty rate affect its
> collection of current support if the state was typical in every other
> way?"*

If designed properly with reliable data, a regression analysis provides
the estimated relationship between an explanatory variable and a given
performance indicator.

As with our previous study, we rely on secondary or existing data
sources for this analysis. Specifically, we draw data from the U.S.
Bureau of the Census Current Population Survey (CPS), OCSE
administrative data, the U.S. Department of Commerce's Bureau of
Economic Analysis, and the U.S. Department of Labor's Bureau of Labor
Statistics.

With the performance and explanatory variables in hand, we specify the
multivariate regression models. In their most general form, the models
take the following form:

*CSE Performance = ƒ(demographic, economic, programmatic factors)*[^8]

Because the effect of a given explanatory variable may differ across
performance measures, we construct a unique regression model for each
performance measure. In the case of paternity establishment, we develop
two separate models because states have the option of measuring
paternity establishment for the entire state ("statewide paternity") or
for the IV-D caseload ("IV-D paternity").

## B. Selection of Study Variables

Language in CSPIA explicitly defined the dependent variables as the five
Congressionally specified performance measures. The dependent variables
were readily available from OCSE. Moreover, the agency's audit division
assessed the accuracy of each state's data submission. In our final
models, we pooled the performance data for FY 1999 and FY 2000, so each
state essentially had two data points for each measure.

We developed a roster of explanatory variables through two processes.
First, we reviewed the academic literature on the determinants of
performance in child support enforcement. With the findings from the
literature in mind, we then conferred with a number of researchers and
program experts to identify other variables with hypothesized
associations with performance.

### 1. Literature Review

The professional literature on the determinants of child support
performance is limited to a number of articles published within the last
several years. Sorensen and Halpern (1999)[^9] conducted a time-series
analysis (1976-1997) with the goal of assessing the effect of the IV-D
system, and particular enforcement tools, on collections. They concluded
state-level policies---the \$50 pass-through, presumptive guidelines,
and tax offset tools---had significant and positive effects on
collection rates. In measuring the effects of those policies, they
controlled for and measured the independent effects of a number of
economic and demographic variables.

Garfinkel, Heintze, and Huang (2000)[^10] considered the effects of
stronger child support enforcement on the incomes of custodial mothers
and their children. The study found that more stringent child support
enforcement has increased child support collections and decreased
welfare caseloads. Moreover, the researchers concluded that improved
enforcement increased the labor supply of mothers who otherwise would
have been on welfare and slightly increased the labor supply of
non-custodial parents.

Fishman et al (2000) looked directly at the OCSE performance indicators
and considered the effects of a state's financing structure on
performance. While they did not find strong associations between
performance and methods of state finance, they did measure statistically
significant relationships between performance and other economic,
demographic, and programmatic factors.

In the following sections, we discuss the key findings of these studies
with respect to individual economic, demographic, and programmatic
measures.

#### a. Economic variables

Each of the studies discussed above hypothesized that economic
conditions faced by the non-custodial and custodial parents would have
an effect on the performance of a child support enforcement program. The
studies used earnings levels and rates of employment or unemployment as
measures of economic conditions.

**Income and Earnings Measures.** Sorensen and Halpern (1999) found that
increases in average earnings of single men were positively correlated
with rates of child support receipt. Specifically, the study concluded
that the modest increases in average earnings for single men during
1976-1997 were responsible for a 0.6 percentage point increase in the
rate of child support receipt for never-married women. For previously
married women, the estimated impact of earnings was 0.3 percentage
points. Similarly, Garfinkel, Heintze, and Huang (2000) measured a
positive effect of the non-custodial parent's income on child support
payments.

**Employment Measures.** Findings on the association between performance
and rates of unemployment or non-employment have been mixed. Fishman et
al (2000) found a negative association between the proportion of males
ages 20-64 not employed and the state's share of IV-D cases with orders.
Specifically, the study found that a one percentage point increase in
the ratio of males not working was associated with 1.3 percentage point
decrease in the percent of IV-D cases with orders for support. The study
did not show statistically significant relationships between the ratio
of men not working and four other measures of performance.[^11] Sorensen
and Halpern (1999) and Garfinkel, Heintze, and Huang (2000) found no
relationship between state unemployment rates and their respective
measures of CSE performance.

#### b. Demographic variables

In addition to considering economic factors, each of the three studies
controlled for demographic changes observed over time or across states.

**Race and Ethnicity Measures**. Garfinkel, Heintze, and Huang (2000)
estimated that, holding other factors constant, eligible
African-American and Hispanic custodial parents are less likely to
receive child support than their white counterparts. By contrast,
Sorensen and Halpern (1999) found that African Americans were more
likely to receive support than whites, while Hispanics were less likely
to receive support than whites---again holding other characteristics
constant.

**Other Demographic Measures**. Sorensen and Halpern (1999) and
Garfinkel, Heintze, and Huang (2000) estimated that the receipt of child
support increases with the custodial parent's age and educational
attainment. Also, Sorensen and Halpern found that the more children in a
family who are potentially eligible for support, the less likely the
family is to receive support.

Past research also suggests that a state's urban and rural mix is
associated with CSE performance. Fishman et al (2000) found strong,
negative associations between the percent of population living in urban
areas and four OCSE performance indicators.[^12] Garfinkel, Heintze, and
Huang (2000) found that custodial parents living in central cities
receive less child support than similarly situated custodial parents who
do not.

#### c. Programmatic variables

Our review of the literature indicates that a number of child support
programmatic variables appear to affect performance.

**IV-D Staffing and Spending**. Fishman et al (2000) found the number of
full-time equivalent (FTE) staff per IV-D case was associated with
higher rates of paternity and order establishment. The study also found
IV-D expenditures per case were negatively associated with OCSE's
cost-effectiveness measure. Garfinkel, Heintze, and Huang (2000) found
that increased expenditures could improve enforcement, but only if a
state had a requisite number of CSE laws in its statutes. If the state
has the requisite laws in place, each additional \$100 per capita spent
on child support enforcement translates into a four percent increase in
income for custodial parents. Additionally, Sorensen and Halpern (1999)
estimated that IV-D spending per single mother is associated with higher
rates of support receipt for never-married mothers and lower rates of
support receipt for previously married mothers. The study also found
statistically significant, positive effects of specific program
policies, including the \$50 pass-through,[^13] in-hospital paternity
establishment programs, presumptive child support guidelines, and
automatic wage withholding.

**Structure and Organization of IV-D**. Fishman et al (2000) found no
relationship between the degree of program centralization and
performance. The study also examined the effects of universality on IV-D
agency performance. A program that serves every custodial parent in the
state that is potentially eligible to receive child support---regardless
of his or her eligibility for TANF---would be considered fully
universal. The study found that universality is positively associated
with the paternity establishment and cost-effectiveness performance
measures.

### 2. Expert Discussions

After reviewing the academic literature, we had conversations with a
range of federal, state, and local governmental and private-sector
experts on child support enforcement (see ***Appendix A*** for a list of
interviewees). Based on these interviews, we assembled a candidate list
of additional economic and demographic variables to use in our analysis
of state-level performance.

> **Poverty and Welfare Status.** Several respondents suggested
> including poverty measures in the study, hypothesizing that
> child-support performance is *inversely* correlated with a state's
> poverty rate. The poverty rate is one measure of the state's economic
> position and may serve as an indication of the relative difficulty
> that non-custodial parents have in securing jobs and paying support.
> Poverty rates are also directly related to TANF participation, and
> therefore, are likely correlated with IV-D participation rates.
> Candidate measures include the percent of the total population in
> poverty, the percent of children in poverty, and the percent of
> population/children receiving TANF or Food Stamps.
>
> **Fertility and Marital Status**. Experts also pointed to a number of
> demographic factors that likely affect performance---most notably the
> martial status of the custodial parent. Consistent with previous
> research, respondents contend that states with a higher share of
> never-married mothers on their IV-D caseload will show weaker
> performance. Specific measures include non-marital birth rates among
> women aged 15-44, percent of children born to unmarried mothers during
> the previous 18 years, and percent of children born to teen mothers
> during the previous 18 years.

### Data Assembly and Model Development 

Following our review of the academic literature and expert discussions,
we assembled data for the majority of the recommended variables. In a
limited number of instances, we departed from the suggestions of experts
if, upon further consideration, we did not agree that a theoretical
relationship existed between the variable and child support performance.
Examples include general state tax collections per capita, state
religious profiles, and state's proximity to the US-Mexican border.
Moreover, experts recommended several factors for which data were not
readily available: proportion of single parents divorced versus never
married, percent of non-custodial parents who are remarried, and average
educational attainment of non-custodial parents. After culling the list,
we selected 55 variables that would be tested in the multivariate
regressions (see ***Appendix B*** for a comprehensive list of
explanatory variables and a list of variables not included in the
study).

##### Economic variables included 19 measures of state poverty and welfare, earnings and income levels, rates of job growth, and unemployment.

-   Demographic variables consisted of 22 measures of migration,
    fertility, race, ethnicity, household composition, and age profiles.

-   Programmatic variables included 36 measures that tracked staffing
    and expenditure levels, program universality, state and county
    supervision, and enforcement and establishment processes.

We developed our models systematically by first selecting a list of
explanatory variables that we believed best explained each performance
indicators, based on our reading of the academic literature and
conversations with experts. As discussed above, we developed six
distinct models---two for paternity establishment and one for each of
the other performance indicators:

-   IV-D paternity establishment;

-   Statewide paternity establishment;

-   Percent of cases with orders;

-   Percent of current support due that is paid;

-   Percent of cases paying toward arrears;

-   Cost-effectiveness.

At the outset of this project, audited data was available for only FY
1999, so we essentially had 51 possible observations for each
performance factor. We had fewer observations for the paternity
establishment measure because states can define the measure in two
different ways, and they are evenly split between those reporting
paternity establishment statewide and establishment within the IV-D
system. Additionally, six states failed to provide audit trails for any
of their performance measures in FY 1999, so we elected not to use their
data.

When OCSE released the audited FY 2000 performance data, we re-ran the
models using updated explanatory variables using the 2000 data.
Ultimately, to develop our final models, we pooled the performance data
for FY 1999 and FY 2000, so most states had two observations for each
performance score. Pooling the data across the two years more than
doubled the number of observations and yielded more stable and
statistically robust models.

During our initial modeling efforts, we included at least one
explanatory (or dependent) variable from each of the following major
categories: poverty/welfare; earnings/income; unemployment/job growth;
fertility; race/ethnicity; household composition/population age
profiles; staffing/expenditures; program universality; and program
structure/supervision. After reviewing the results of these initial
efforts, we substituted explanatory variables---within major
categories---to see to if the new combination improved our prediction of
state performance. For example, we would substitute rates of child
poverty for overall poverty or overall unemployment rates of
non-employment specific to males.

Through this method, we discovered that certain variables performed well
on their own but not in combination with related variables with which
they were correlated. For example, indicators of poverty are correlated
with rates of employment or unemployment. In such instances when we used
both measures, the models had difficulty determining relationships
between the variables, and consequently rendered both statistically
insignificant. We ultimately selected the explanatory variable that
yielded the best prediction of performance and dropped the related
variable.

In addition, we found that many of the candidate variables did not
perform well under any of our specifications. For example, the variables
for race and ethnicity typically were not statistically significant and
were unstable. Similarly, the number of self-employed workers, percent
of population incarcerated, and average TANF household size failed to
show stable associations with performance across models.

Our final six models relied on different combinations of 13 explanatory
variables:

-   Personal income per capita;

-   Poverty rate;

-   Percent of males aged 20-64 not working;

-   Rate of job growth;

-   Percent of population living in urban areas;

-   Percent of TANF case heads under age 30;

-   Percent of IV-D cases currently participating in TANF;

-   Percent of IV-D cases that have never participated in TANF;

-   Number of IV-D cases per full-time equivalent (FTE) staff;

-   IV-D expenditures per case;

-   Population stability (percent of people living in same house 1999
    and 2000);

-   Judicial or administrative order establishment process;

-   Audit pass/failure indicator.

Each of the six models had at least one economic, one demographic, and
one programmatic variable. None of the six models used all 12 economic,
demographic and programmatic variables; all used the audit pass/fail
indicator. We provide details on the performance indicators and
explanatory variables in ***Section III***.

## C. Limitations

Our analysis has several limitations. First, with respect to data
quality, the program performance data series are, in some cases,
relatively new, and states are in the process of refining their data
collection and reporting methods. OCSE has audited the performance data
for both FY 1999 and 2000, and while states are showing signs of
improvement in their reporting, they still generally struggle with three
variables in particular: paternity establishment percentage, cases
paying toward arrears, and current collections. ***Exhibit II.1***
reports the number of states that failed the data reliability audit for
each indicator during FYs 1999 and 2000.

Exhibit II.1

Performance Measure Audit Failure Rates: 50 States and Washington, D.C.

  ------------------------------------ ----------------- -----------------
  Performance Indicator                States Failed in  States Failed in
                                       1999              2000

  Cases with Orders                    6                 2

  Collections on Current Support       12                7

  Collections on Arrears Due           12                7

  Cost Effectiveness                   1                 1

  Paternity Establishment, IV-D        7                 12

  Paternity Establishment, Statewide   9                 1
  ------------------------------------ ----------------- -----------------

Source: Office of Child Support Enforcement

To correct for the most serious instances of missing or miscalculated
data, we dropped certain states from our analyses. The ongoing problems
with data quality should be expected at this early stage of
implementation. Congress chose to phase-in the new incentive system over
time in part because of concerns about states' abilities to report data
for the performance measures. When more reliable data become available
for those states, we encourage researchers to replicate this analysis.
We expect that in doing, so researchers might draw somewhat different
conclusions about the associations between state CSE performance and
economic, demographic, and programmatic variables.

In addition to data quality, we were also concerned about omission of
potential determinants of CSE performance for which we had no measures.
For example, we know that in addition to the number of CSE enforcement
staff in each state, the quality of the staff and management also affect
performance. Likewise, the functionality of a state's computer system
should affect performance, but we were unable to rank or score the
relative quality of state systems. In short, we can point to a number of
factors that may affect performance that we have knowingly omitted from
the analysis. To the extent that those omitted variables are important
in explaining CSE performance, our findings may be biased, as our models
will assign the effects of these omitted variables to the variables that
we did include. We did not attempt to correct for this bias and urge
readers to consider it when interpreting our results.

Our results may also be influenced by "pre-test bias." We used simple
correlations between a candidate roster of explanatory variables and our
dependent variables to inform our selection of explanatory variables for
our regression model. Specifically, we included those explanatory
variables that were highly correlated with our dependent variables and
avoided using pairs of variables that were highly correlated with each
other. Once we had determined our base regression model, we also
conducted a series of sensitivity analyses, adding and subtracting
individual explanatory variables to determine the importance of those
variables. Both of these selection procedures may contribute to pre-test
bias in our findings. Pre-test bias means that we are more likely to
find statistically significant associations between our explanatory and
dependent variables than we would otherwise.

Finally, our statistical models rely on a relatively limited number of
observations. We mitigated this liability by pooling data across FY 1999
and FY 2000, which generated up to 96 observations for each performance
measure. In the future, researchers will benefit from additional years
of data, which will allow time-series analyses and pooling over larger
numbers of years.

# III. Description of the study variables 

## A. Performance Measures (Dependent Variables)

The primary goal of the study is to determine if variations in CSE
performance across states are based on differences in economic,
demographic, and programmatic factors.[^14] To date, few studies have
attempted to analyze the performance of CSE programs due, in part, to
the absence of appropriate measures of performance.[^15] For the
purposes of this study, we define program performance by five measures
that states reported to OCSE in FY 1999 and FY 2000. OCSE used the FY
2000 measures to calculate incentive payments under CSPIA.

Below we provide the definition of the five CSE performance measures. It
is important to note that OCSE has taken steps to standardize the
caseload data among states. For example, OCSE excluded cases for which a
state had no legal jurisdiction (e.g., international and tribal cases).

### 1. Paternity Establishment Percentage (PEP)

The first performance measure is based on the Paternity Establishment
Percentage as defined in the Personal Responsibility and Work
Opportunity Reconciliation Act (PRWORA). Under the new incentive
formula, states use one of two measures: (1) a IV-D (or "caseload")
paternity establishment measure (IV-D), or (2) a statewide paternity
establishment measure (statewide). It is defined as follows:

+-------------------+---+----------------------------------------------+
| Paternity         |   | Total number of children in IV-D caseload in |
|                   |   | the Fiscal Year or, at the option of the     |
| Establishment     |   | State, as of the end of the Fiscal Year who  |
|                   |   | were born out-of-wedlock with paternity      |
| Percentage- IV-D  |   | established or acknowledged                  |
+-------------------+---+----------------------------------------------+
|                   | = | []{.mark}                                    |
+-------------------+---+----------------------------------------------+
|                   |   | []{.mark}                                    |
+-------------------+---+----------------------------------------------+
|                   |   | Total number of children in the IV-D         |
|                   |   | caseload who were born out-of-wedlock as of  |
|                   |   | the end of the prior Fiscal Year             |
+-------------------+---+----------------------------------------------+

+-------------------+---+----------------------------------------------+
| Paternity         |   | Total number of minor children in the state  |
|                   |   | born out-of-wedlock and paternity            |
| Establishment     |   | established or acknowledged during the       |
|                   |   | Fiscal Year                                  |
| Percentage-       |   |                                              |
| Statewide         |   |                                              |
+-------------------+---+----------------------------------------------+
|                   | = | []{.mark}                                    |
+-------------------+---+----------------------------------------------+
|                   |   | []{.mark}                                    |
+-------------------+---+----------------------------------------------+
|                   |   | Total number of children in the state born   |
|                   |   | out-of-wedlock in the previous Fiscal Year   |
+-------------------+---+----------------------------------------------+

During FYs 1999-2000, the median IV-D paternity establishment score was
67.4 percent, and the statistic ranged from a low of 1.4 percent to a
high of 180.9 percent. OCSE deemed the scores at low and high ends of
the range unreliable. By contrast, the median score for states
submitting the statewide paternity establishment version was 92.1 during
1999-2000 and range from 52.9 to 251.7. Unlike other performance
indicators, a paternity establishment score in excess of 100 percent is
feasible if a state establishes a large number of backlog cases from
prior years. From an analytical perspective, this performance variable
was the most problematic for two reasons.

First, as just noted, the federal government gives states the option to
report the statistic for the IV-D caseload or for the state as a whole.
In FY 1999 and 2000, states were split almost evenly between submitting
the IV-D and statewide measures.[^16] Second, the measure had the
highest rate of audit failures because of poor data quality, with 16
states submitting data that OCSE deemed unreliable in FY 1999 and 13
states in 2000. Because it is inappropriate to combine scores from
statewide and IV-D states, few quality data points were available for
each of the paternity establishment (IV-D and statewide) analyses.

### 2. Percentage of IV-D Cases with Orders for Support

The second indicator measures the percentage of cases in the IV-D
caseload that have orders for support. OCSE defines the measure as
follows:

+-------------------+---+----------------------------------------------+
| Percentage of     |   | Number of IV-D cases with orders for support |
| IV-D              |   | during the Fiscal Year                       |
|                   |   |                                              |
| Cases with Orders |   |                                              |
|                   |   |                                              |
| for Support       |   |                                              |
+-------------------+---+----------------------------------------------+
|                   | = |                                              |
+-------------------+---+----------------------------------------------+
|                   |   |                                              |
+-------------------+---+----------------------------------------------+
|                   |   | Number of IV-D cases during the Fiscal Year  |
+-------------------+---+----------------------------------------------+

Note that the IV-D caseload---which is the denominator in this indicator
as well as a component of the following two CSE performance
indicators---is not as straightforward as it may seem. For example,
certain types of cases, such as interstate cases, will be counted in two
or more states' caseloads.

During 1999-2000, the median cases with orders score was 66 percent and
scores ranged from a low of 26 percent to a high of 93 percent. In FY
1999, 6 states failed the audit on the measure. In FY 2000, two states
failed the audit.

### 3. IV-D Collection Rate for Current Support

The third performance indicator measures the proportion of current
support due that is collected on IV-D cases. The proportion is expressed
by the following formula:

+-------------------+---+----------------------------------------------+
| IV-D Collection   |   | Dollars collected for current support in     |
|                   |   | IV-D cases                                   |
| Rate for Current  |   |                                              |
|                   |   |                                              |
| Support           |   |                                              |
+-------------------+---+----------------------------------------------+
|                   | = |                                              |
+-------------------+---+----------------------------------------------+
|                   |   |                                              |
+-------------------+---+----------------------------------------------+
|                   |   | Dollars owed for current support in IV-D     |
|                   |   | cases                                        |
+-------------------+---+----------------------------------------------+

During 1999-2000, the median score on the indicator was 53 percent and
ranged from 15 percent to 77 percent. In FY 1999, 12 states failed the
audit on this measure. In FY 2000, seven states failed the audit.

### 4. Percentage of IV-D Cases with Collections on Arrears

The fourth indicator measures state efforts to collect money from cases
with an arrearage. The measure specifically counts paying cases---and
not total arrears dollars collected---because states have different
methods of handling certain aspects of arrears cases. The measure is
calculated as follows:

+-------------------+---+----------------------------------------------+
| Percentage of     |   | Number of IV-D cases with at least one       |
| IV-D              |   | payment toward arrears                       |
|                   |   |                                              |
| Cases with        |   |                                              |
| Collections       |   |                                              |
|                   |   |                                              |
| on Arrears        |   |                                              |
+-------------------+---+----------------------------------------------+
|                   | = |                                              |
+-------------------+---+----------------------------------------------+
|                   |   |                                              |
+-------------------+---+----------------------------------------------+
|                   |   | Number of IV-D cases with arrears due        |
+-------------------+---+----------------------------------------------+

OCSE audit reports suggest that states continue to struggle with its
measurement. During 1999-2000, the median score on the indicator was 57
percent. Scores ranged from 30 percent to an infeasible 145 percent. In
FY 1999, 12 states failed the audit on the measure. FY 2000 saw an
improvement, with only seven states failing the audit.

### 5. Cost-Effectiveness {#cost-effectiveness .ES-Heading-3 .unnumbered}

The fifth measure assesses the total dollars collected in the CSE
program for each dollar spent. The equation for cost-effectiveness is
the following:

  -------------------- ---- ----------------------------------------------
  Cost-Effectiveness        IV-D dollars collected

                       =    

                            

                            IV-D dollars expended (federal and state
                            shares)
  -------------------- ---- ----------------------------------------------

The ratio has long governed state incentive payments, although the
definition has changed somewhat, and consequently data quality is good.
During FY 1999 and 2000, only one state failed the audit each year.
States ranged from cost-effectiveness ratios of \$1.21 to \$8.41.

## B. Factors Associated with Performance (Explanatory Variables)

The models' explanatory variables fall into three key categories:
economic, demographic, and programmatic. As indicated in the previous
section, we tested each of the 55 variables listed in ***Appendix B***.
In this section, we discuss only the 12 we used in our final model for
each performance indicator.

The variables used in our analyses, and the years for which the data are
available, are listed in ***Exhibit III.1***. For the economic and
demographic variables, we relied on secondary data sources, such as
tables published by the Census Bureau, the Department of Labor's Bureau
of Labor Statistics, and the Department of Commerce's Bureau of Economic
Analysis. The programmatic variables were available from OCSE data.
***Appendix C*** lists the state-level data used in the analysis.

Ideally, we would use 1999 data in our 1999 performance models and 2000
data in our 2000 models. Data for 1999 and 2000 was available for the
child support program variables. However, because we relied on disparate
sources for the economic and demographic variables, we were limited in
the timeliness of the data. Additionally, our need for state-level data,
as opposed to aggregate national data, further limited our data options.
For instance, while data on poverty rates is available for the nation
for our study years, state-level data is reported in three-year
averages, due to the Current Population Survey's small sample size for a
number of states.

**Exhibit III.1**

Economic, Demographic, and Programmatic Variables

  ----------------------------------- -----------------------------------
  **Variable**                        **Most Recent Data Available;
                                      Source**

  Poverty Rate                        1998-2000; U.S. Bureau of the
                                      Census, Current Population Survey

  Personal Income Per Capita          2000: U.S. Department of Commerce,
                                      Bureau of Economic Analysis

  Percent Males 20-64 not Employed    2000; U.S. Department of Labor,
                                      Bureau of Labor Statistics; Census
                                      Bureau Current Population Survey

  Job Growth                          1999-2000, U.S. Department of
                                      Labor, Bureau of Labor Statistics,

  Percent Population Living in Urban  1990; Census Bureau, Decennial
  Areas                               Census

  Population Stability                1999-2000; Census Bureau 2000
                                      Supplemental Survey

  TANF Heads under Age 30             1999; Administration for Children
                                      and Families, U.S. Department of
                                      Health and Human Services

  Number of Cases per FTE             2000; OCSE

  IV-D Expenditures per Case          2000; OCSE

  Program Universality                2000; OCSE

  Current TANF Recipient              2000; OCSE

  Judicial or Administrative Order    1997 survey, updated 2001; Center
  Establishment                       for Law and Social Policy
  ----------------------------------- -----------------------------------

### 1. Economic Variables

#### a. Poverty Rate

The state poverty rate variable is a well-known indicator that divides
the number of people living in households with income below the federal
poverty threshold by the total number of people living in the state. We
used the U.S. Census Current Population Survey three-year average
estimates for poverty.[^17] State poverty rates ranged from 7.3 percent
(Maryland) to 22.7 percent (District of Columbia). The median score was
11.2 percent. We hypothesize that a higher level of poverty is
associated with weaker child support performance.

#### b. Personal Income Per Capita

This variable divides personal income received by state residents by the
state's total non-institutionalized population. During 1999-2000, the
measure varied from a low of \$20,013 (Mississippi) to a high of
\$40,870 (Connecticut). Median per capita personal income for the period
was \$26,840. We hypothesize that lower personal income per
capita---evidence of a less robust economy---will translate into weaker
child support performance.

#### c. Percent of Males Aged 20-64 Not Employed

Our third economic variable is related to a male unemployment rate but
also captures men who are out of the labor force. Specifically, the
measure divides the non-institutionalized population of males between
the ages of 20 and 64 who are not working by the total population of
non-institutionalized males living in the state. We designed the measure
to capture economic conditions facing non-custodial parents, the
majority of whom are male and between the ages of 20 and 64.[^18] During
1999-2000, the statistic varies from a low 10.3 percent (Nebraska) to a
high of 26.2 percent (West Virginia). The median stood at 15.1 percent.
We hypothesize that a larger proportion of males not working will be
associated with weaker child support performance. Because the factor is
a proxy for the non-custodial parent's ability to pay child support, it
should have a stronger relationship with the payment-related measures:
collection ratio, percent of cases paying on arrears, and
cost-effectiveness.

#### d. Job Growth

Our final economic variable measures the rate of job growth in the state
during in the previous year, using labor force data from the Bureau of
Labor Statistics. The median state experienced a job growth rate of 1.8
percent. The statistic varies from average declines of --0.9 percent
(Nebraska) to growth of 5.5 percent (New Hampshire). We hypothesize that
higher job growth rates will be associated with stronger child support
performance. Higher growth rates are correlated with more job starts and
lower unemployment, which give enforcement workers additional
opportunities to locate absent parents and withhold wages.

### 2. Demographic Variables

#### a. Percent of Population Living in Urban Areas

This variable measures the number of persons residing in urbanized areas
in 1990 divided by number of residents in state in 1990.[^19] An
urbanized area is defined by the Census Bureau as a "central place" and
the adjacent densely settled surrounding "urban fringe" that together
have a minimum of 50,000 people. The variable ranges from 15 percent in
Vermont---our least urbanized state---to 100 percent in the District of
Columbia. The median state has 50 percent of its population living in
urban areas. Based on findings from Fishman (2000) and Garfinkel (2000),
we anticipate that a higher share of the population living in urban
areas will be associated with weaker program performance.

#### b. Population Stability

This demographic measure, drawn from the Census 2000 Supplemental
Survey, reports the share of a state's population that lived in the same
home one year before the survey was taken.[^20] States with high in- or
out-migration show lower percentages for this measure. In 2000,
population stability ranged from a low of 76 percent in Nevada to a high
of 89 percent in New York. Montana and North Dakota, the median states,
registered 84 percent. We hypothesize that states with less stability
have a more difficult time locating non-custodial parents as they move
from house to house within the state or across state borders. Moreover,
states with unstable populations are likely serving a higher share of
custodial families who have recently moved and who are associated with
non-custodial parents who live in a different county or state. Given
these dynamics, we expect the population stability variable to be
positively correlated with performance. That is, a higher stability
measure should be associated with better child support
performance---holding other factors constant. The factor has a
theoretical relationship with all five performance measures.

#### 3. TANF Heads under Age 30

This measure is drawn from Temporary Assistance for Needy Family (TANF)
administrative data. It is defined as the proportion of all case heads
who are under age 30. In FY 1999, it ranged from a low of 34.5 percent
in California to a high of 63.8 percent in Alabama. We hypothesized that
a larger proportion of young TANF heads would be associated with weaker
CSE performance.

### 3. Programmatic Variables

#### a. Number of Cases per Full-Time-Equivalent Staff (FTEs)

Taken from OCSE administrative data, we define this variable as the
cases at the end of the Fiscal Year divided by the number of full-time
equivalent IV-D staff in the state. The number of cases per FTE does not
reflect the caseload of frontline workers, but the caseload relative to
all IV-D staff combined. As noted above, the denominator might include
double counting of certain types of cases (e.g., interstate). In
addition, the numerator does not include those child support staff,
particularly those in the judicial system, who work on child support
issues but are not paid by the IV-D program (e.g., judges). During
1999-2000, cases per FTE ranged from 145 in Utah to 798 in South
Carolina. The median state had 287 cases per FTE.

Program observers have speculated that program performance is associated
with the relationship between the number of CSE staff employed by a
state and the number of cases in the state. A number of policymakers and
commissions, including the U.S. Commission on Interstate Child Support,
have called for studies on the issue.[^21]

#### b. IV-D Expenditures per Case

In addition to theories about staffing outlined above, some observers
believe the amount of total resources devoted to the IV-D agency on a
per case basis is associated with performance. The key difference
between this measure and the previous one is that expenditures per case
captures information on a state's spending on automated systems and
staff salaries, as well as other expenditures (e.g., lab costs). During
1999-2000, Indiana spent the least per case (\$98) and Minnesota spent
the most (\$526). The median state spent \$249 per case. We anticipate
expenditures per case have a positive relationship with all but one
performance indicator: the cost-effectiveness ratio.

#### c. Program Universality (Percent of IV-D caseload on TANF now; Percent of IV-D caseload never on TANF)

IV-D program officials have hypothesized that the composition of a
state's caseload affects a state's program performance. Specifically,
some suggest that performance improves as the program serves a greater
share of the population that never received cash assistance. These
never-on-welfare families report higher collection rates of current
support.[^22] By contrast, caseloads comprised of current or former
welfare recipients tend to be more difficult to serve---perhaps in part
because a larger share of the parents in these families have lower
incomes, have less education, and are never married. Our analysis
includes two indicators that measure the relative difficulty of a
state's IV-D caseload.

The first indicator reports the share of a state's IV-D caseload that is
*currently* enrolled in the TANF program. Idaho reported the lowest
share of its IV-D caseload currently on TANF (5 percent). Rhode Island
had the highest share (45 percent). In the median state, 17 percent of
IV-D cases were actively receiving TANF benefits.

A second indicator, related to the first, measures the share of a
state's IV-D caseload that has *never* received TANF benefits. States
began reporting their "never assistance" caseloads to OCSE beginning in
FY 1999. During 1999 to 2000, the statistic ranged from a low of 15
percent in Rhode Island to a high of 68 percent in Indiana. Given these
statistics, we would consider Indiana's program to be significantly more
"universal" than Rhode Island's.

#### d. Judicial or Administrative Process for Order Establishment

Program observers have hypothesized that the method by which a state
establishes child support orders may be associated with one or more of
the performance measures. Relative to other aspects of the program,
states and localities have flexibility in selecting the forum and
participants of the establishment process. A highly judicial process
involves a formal court setting with a judge presiding and an attorney
representing the IV-D agency. In a highly administrative process, the
state establishes orders in a IV-D office, generally without an attorney
involved. Between these two extremes are a number of variations. As part
of a concurrent study for OCSE, the project team developed a taxonomy,
ranging from 4 (highly administrative) to 16 (highly judicial) that
characterized each state's establishment process. We detail the method
and individual state scores in ***Appendix D***. We anticipate that
administrative processes, which observers believe are faster, may have a
positive association with cases with orders. On the other hand,
observers note that establishing an order through the court and in the
presence of a judge may lead to higher compliance, which could result in
judicial processes showing better performance on the collection and
arrearage indicators.

# IV. study results

## A. Simple Correlations

A key step in designing a regression model is gaining a better
understanding of how the data that underlie the analysis interrelate. To
do so, we estimated simple correlation coefficients, which measure the
strength and direction of the relationship between two variables. In
doing so, we focused on the following questions:

-   Are the dependent variables (i.e., performance indicators)
    correlated with one another? We examined this issue to determine
    whether improvement in one performance area may be related to
    improvement in another area.

-   Are the explanatory variables correlated with the dependent
    variables? If an explanatory variable is correlated with the
    dependent variable, there is an increased likelihood that the
    variable will prove to be important in the regression model.
    However, it is also the case that variables that appear promising
    based on a correlation statistic may show no relationship with the
    dependent variable in a regression model.

-   Are the explanatory variables correlated with one another? While
    regression analysis is designed to isolate the effects of each
    variable, the method suffers if two explanatory variables are highly
    correlated. That is, if two variables move in concert, the model has
    difficulty determining their independent effects on the dependent
    variable.

We used combined 1999 and 2000 data. We present the findings of the
correlation analyses in ***Appendix E***. Below, we briefly summarize
correlations among the dependent variables and the independent variables
ultimately used in the regression analyses.

### 1. Correlations Among Dependent Variables

As ***Exhibit IV.1*** depicts, we found little positive or negative
correlation between the dependent variables, which suggests that the
level of performance on one measure does not appear to be associated
with the level of performance on another. There are two exceptions to
this general finding. First, states that have a higher percentage of
IV-D cases with orders for support also had a higher collection rate for
current support (correlation coefficient of 0.49). Second,
cost-effectiveness appears to be correlated with cases with orders
(0.43) and current collections (0.40).

Exhibit IV.1

Correlations among Dependent Variables

  --------------- --------- ------------- ---------- --------------- --------------- ------------------------
                  **PEP     **PEP         **Cases    **Current       **Collections   **Cost-Effectiveness**
                  IV-D**    Statewide**   with       Collections**   on Arrears**    
                                          Orders**                                   

  PEP IV-D        1.0       .044          (.002)     .018            (.189)          (.150)

  PEP Statewide   .044      1.0           .236       .213            .054            .185

  Cases with      (.002)    .236          1.0        **.494**        .273            **.427**
  Orders                                                                             

  Current         .018      .213          **.494**   1.0             .178            **.402**
  Collections                                                                        

  Collections on  (.189)    .054          .273       .178            1.0             .064
  Arrears                                                                            

  Cost            (.150)    .185          **.427**   **.402**        .064            1.0
  Effectiveness                                                                      
  --------------- --------- ------------- ---------- --------------- --------------- ------------------------

### 2. Correlations Between Explanatory Variables and Dependent Variables

Correlations among all explanatory variables we considered for this
analysis and our dependent variables suggested that certain variables
were likely to perform well in the regression analysis (i.e.,
correlation of greater than 0.4). Some variables were economic. For
example, the state poverty rate and the proportion of men ages 20 to 64
not working were highly and negatively correlated with the proportion of
cases with orders. (See ***Exhibit IV.2***.)

A number of the relationships between the variables were
program-related. Specifically, we found that states with higher numbers
of cases per FTE (thus lower overall staffing levels) had a lower
percentage of IV-D cases with orders for support, as well as a lower
percentage of current collections on orders. Conversely, states with
higher expenditures per case had a higher percentage of cases with
orders. Moreover, the process for establishing orders appears correlated
with order establishment: states with more judicial processes had a
lower percentage of cases with orders. One economic measure---the
percentage of males aged 20-64 who were not employed---was negatively
correlated with the percentage of IV-D cases with orders for support.
These correlations are all in the direction we would expect. (See
***Appendix E*** for all correlations***.***)

Exhibit IV.2

Key Explanatory and Dependent Variable Correlations

+------------------------------+---------------------------------------+
| **Explanatory variable...**  | **...is correlated with**             |
+------------------------------+---------------------------------------+
| Higher state poverty         | Lower percent of cases with orders:   |
|                              | (.504)                                |
+------------------------------+---------------------------------------+
| More men 20-64 not working   | Lower percent of cases with orders:   |
|                              | (.402)                                |
+------------------------------+---------------------------------------+
| Higher cases per FTE         | Lower percent of cases with orders:   |
|                              | (.602)                                |
|                              |                                       |
|                              | Lower percent of current collections: |
|                              | (.413)                                |
+------------------------------+---------------------------------------+
| Higher expenditures per case | Higher percent of cases with orders:  |
|                              | .548                                  |
+------------------------------+---------------------------------------+
| A more judicial process      | Lower percent of cases with orders:   |
|                              | (.501)                                |
+------------------------------+---------------------------------------+

###  3. Correlations among Explanatory Variables

Finally, we explored correlations between our independent variables.
(See ***Appendix E*** for all correlations.) We tried to avoid using
highly correlated independent variables (i.e., correlations greater than
0.4) in the same regression model. Generally, the independent variables
were not highly correlated. There were, however, a few exceptions (see
***Exhibit IV.3***). Men ages 20 to 64 not working was highly
(positively) correlated with the state poverty rate. Expenditure per
case average was highly (negatively) correlated with the state poverty
rate. The state poverty rate was highly (negatively) correlated with per
capita personal income. The percentage of the population in urban areas
was highly (positively) correlated with per capita personal income. And,
cases per FTE and expenditures per case were highly (negatively)
correlated. These highly correlated pairs were not used together in
models, with one exception; percent urban and per capita personal income
were used in the collections model. Because these variables were
frequently mentioned by experts as important factors in child support
enforcement performance, we opted to retain them.

Exhibit IV.3

Correlations among Explanatory Variables

+-----------------------------------+-----------------------------------+
| **Explanatory variable**          | **Correlation**                   |
+-----------------------------------+-----------------------------------+
| State poverty rate                | Positively with men 20-64 not     |
|                                   | working: .657                     |
|                                   |                                   |
|                                   | Negatively with per capita        |
|                                   | personal income: (.401)           |
|                                   |                                   |
|                                   | Negatively with expenditures per  |
|                                   | case average: (.453)              |
+-----------------------------------+-----------------------------------+
| Percent of population in urban    | Positively with per capita        |
| areas                             | personal income: .684             |
+-----------------------------------+-----------------------------------+
| Cases per FTE                     | Negatively with expenditures per  |
|                                   | case average: (.751)              |
+-----------------------------------+-----------------------------------+

## 

## B. Multivariate Regression Analysis

To capture the relationship of each explanatory variable to the
performance of CSE programs, we developed six multivariate regression
models:

-   Cases with orders

-   Current collections

-   Cases with collections on arrears

-   Cost-effectiveness

-   Statewide paternity establishment

-   IV-D paternity establishment

Unlike the simple correlations described previously, the output from
multivariate regression analysis reports the association between the
dependent variable (e.g., cases with orders) and an explanatory variable
(e.g., percent of males 20 to 64 not employed) *holding all other
explanatory variables constant.* For example, one might ask, "How would
the proportion of men not employed in a state affect its cases with
orders ratio if the state was typical in every other way?" If designed
properly with reliable data, a regression analysis should report an
estimated relationship between the explanatory variable and given
performance indicator.

> As described in detail in ***Section II***, we grouped explanatory
> variables into categories and subcategories. We systematically tested
> a number of combinations of explanatory variables. We noted which
> variables (e.g., percent of population in an urban area) appeared to
> perform well across models. We then tested this "short list" for each
> dependent variable. In the end, we had 12 economic, demographic, and
> programmatic variables across six models.

In addition to selection of independent variables, we had to determine
how to treat states that had inconsistent data across performance
indicators. There were a number of scenarios:

-   A state had no audit trail, thus failed audits on all performance
    indicators. In 1999, six states fell into this category.[^23] We
    dropped these states from all the regression models for both years,
    even through in 2000, no state failed all audits.

-   States failed some audits. In 1999, for example, the number of
    states failing an audit ranged from 1 for cost-effectiveness to 16
    for paternity establishment. States that provided an audit trail but
    failed an audit were included in the regressions. To capture a
    state's pass or fail on a particular performance measure, we
    assigned each state a "dummy variable." If the state passed the
    audit, it received a "1", and if it failed, it received a "0". Use
    of a dummy variable enabled us to observe whether states that fail
    their audits on a particular indicator as a group systematically had
    higher or lower rates on each performance indicator than states that
    passed.

We ran single year regressions using 1999 and 2000 data. Then we pooled
the data for both years to increase the number of observations. We
report on the findings from our panel data analyses here; the findings
for all of the regression models are reported in ***Appendix F***. For
each model, we report a coefficient for each of the explanatory
variables used in that model. The sign of the coefficient indicates the
variable's positive or negative association with the dependent variable.
The actual value of the coefficient is difficult to interpret without
reviewing the data that underlie the analysis, which we will do below.
We also report the coefficient's statistical significance. A coefficient
that is statistically significant at the one-percent level implies
that---with 99 percent certainty---the association between the
explanatory variable and dependent variable is not equal to zero. We
have more confidence in the reported relationships of variables that are
statistically significant at the one-percent level than those that are
significant at the five- or ten-percent levels. In those cases where the
coefficient is *insignificant*, we can not be certain---in a statistical
sense---that an association between the explanatory and dependent
variables actually exists.

In addition to the coefficients on the individual explanatory variables,
we report a statistic called the "R^2^" for each of the models. The
statistic is an overall measure of a model's explanatory power.
Specifically, it measures the percentage of variation in the dependent
variable that can be explained by the explanatory variables. The
statistic varies from 0.73 in the cases with orders model to 0.33 in the
paternity establishment (statewide) model. In other words, our
explanatory variables explain about 73 percent of the variation in the
cases with orders performance indicator and 33 percent of the variation
in the paternity establishment (statewide) indicator.

In the following sections, we describe in more detail the estimated
coefficients and their implications.

### 1. Factors Associated with Cases with Orders

The cases with orders indicator measures the ratio of IV-D cases with
orders for support to the number of IV-D cases. Our model consists of
six independent variables and a dummy variable that indicates whether
the state passed the audit for the measure. The independent variables
included were state poverty rate, percent urban, cases per FTE, score on
the judicial/administrative taxonomy, population stability, and the
proportion of the caseload on TANF. As indicated above, the R^2^ was
0.73; thus, our model explains 73 percent of the variation across states
for the cases with orders indicator.

Seven of the eight independent variables were significant. State
poverty, percent urban, cases per FTE, and judicial/administrative
process were significant at the 1 percent level. Two other variables
(caseload on TANF and population stability) were significant at the 5
percent level. The coefficient for the dummy on the audit findings was
not statistically significant, which implies that states failing the
audit did not report performance that was systematically higher or lower
than states that passed the audit.

The signs of the coefficients conform with expectations. State poverty,
percent urban, cases per FTE, administrative/judicial process and
percent of cases currently on TANF are negative. Thus, an increase in
these indicators is associated with weaker performance on the cases with
orders measure. We describe findings for each of the variables in more
detail below.

-   *State poverty*. Higher levels of state poverty are associated with
    lower performance on the cases with orders measure. For each
    percentage point increase in the poverty rate, the proportion of
    cases with orders declines about two percentage points. We predict
    that an increase in the poverty rate from 10 percent to 15 percent
    would be associated with a 9 percentage point decrease in the cases
    with orders measure.

-   *Percent urban*. A percentage point increase in the proportion of
    the state's population in an urban area is associated with a 0.22
    percentage point reduction in cases with orders. Thus, we predict
    that an increase in the urbanicity rate from 40 percent to 50
    percent would be associated with a 2.2 percentage point reduction in
    a state's cases with orders score.

-   *Cases per FTE*. Our modeling indicates that the number of cases per
    FTE is associated with weaker child support performance. For
    example, if a state's cases per FTE increased by 100, from 400 to
    500, the model predicts that the state's cases with orders score
    would fall by 4.4 percentage points.

-   *Administrative/judicial process*. An increase of one point on the
    taxonomy scale---which implies moving incrementally toward a more
    judicial process---is associated with a 0.97 percentage point
    decline in cases with orders. Thus, if a state moved from a taxonomy
    score of 5 to a taxonomy score of 10 (from a very administrative
    process to a quasi-judicial process), we predict that the percentage
    of cases with orders would decline by 4.8 percentage points.

-   *Caseload currently on TANF*. A percentage point increase in the
    proportion of the IV-D caseload currently receiving TANF is
    associated with a 0.25 percentage point decline in cases with
    orders. Thus, we predict that an increase in the proportion of the
    caseload on TANF from 25 percent to 35 percent would be associated
    with a 2.5 percentage point drop in the cases with orders measure.

-   *Population stability*. A percentage point increase in population
    stability is associated with a 0.87 percentage point increase in
    cases with orders. Therefore, if a state's stability score increased
    from 85 percent to 90 percent, we would expect the cases with orders
    score to increase by 4.3 percentage points.

### 2. Factors Associated with Percentage of Current Support Collected

The collections on current support indicator measures the ratio of
dollars collected for current support to dollars owed for current
support. Our model consists of eight independent variables, including a
dummy variable that indicates whether or not the state passed the audit
on the measure. The independent variables were per capita personal
income, percent of males 20 to 64 not working, percent urban, proportion
of TANF caseload less than age 30, cases per FTE, population stability,
and the proportion of the caseload on TANF. The R^2^ was 0.61; thus, our
model explains more than 60 percent of the variation observed in the
measure across states.

All eight independent variables were significant at the five percent
level or better. The signs on the coefficients make sense intuitively.
Percent of males 20-64 not employed, percent urban, percent of TANF
caseload less than 30, cases per FTE, and percent of cases currently on
TANF had negative coefficients. Thus, an increase in these variables is
associated with a decrease in current collections. The coefficient on
the dummy variable also was negative. This suggests that states that
passed the audit reported lower collection rates on average than those
that did not. The coefficient on the population stability variable was
positive. Each is described in more detail below.

-   *Males 20-64 not employed*. A percentage point increase in the
    proportion of males not employed is associated with almost a one
    percentage point decrease in current collections. Put another way,
    we predict that an increase in the proportion of men not employed
    from 10 percent to 15 percent would be associated with a 5.0
    percentage point drop in current collections.

-   *Percent urban*. A percentage point increase in a state's urbanicity
    is associated with a 0.1 percentage point drop in current
    collections. So, for example, if the proportion of the state
    population residing in urban areas increased from 50 percent to 60
    percent, we predict that current collections would decline by almost
    one percentage point.

-   *TANF less than 30*. A percentage point increase in the proportion
    of the TANF heads under age 30 is associated with almost a
    half-point decline in current collections. Thus, if the proportion
    of TANF heads under age 30 increased from 50 percent to 60 percent,
    current collections would decline by 4.5 percentage points.

-   *Cases per FTE*. The model's coefficient is --0.02. This implies
    that an increase of 100 in a state's average caseload per FTE would
    yield a 2 percentage point reduction in the share of current support
    due that is collected.

-   *Caseload currently on TANF*. A one percentage point increase in the
    proportion of the IV-D caseload on TANF is associated with a 0.21
    point drop in current collections. Thus, if the proportion of the
    caseload on TANF increased from 40 percent to 50 percent, current
    collections would decline by about 2 percentage points.

-   *Per capita personal income*. The model finds that an increase in
    per capita income is associated with an increase in collections.
    Specifically, our findings suggest if a state's per capita income
    increased by \$5,000, the state's performance on current collections
    would improve by 2.9 percentage points.

-   *Population stability*. A one percentage point increase in the
    stability measure is associated with a 1.1 percentage point
    improvement in the current collections statistic. Thus, an increase
    from 80 percent to 85 percent of state residents who remained in
    their homes from one year to the next would be associated with a 5.5
    percentage point increase in current collections.

### 3. Factors Associated with Cost-Effectiveness

Cost-effectiveness is a measurement of the state's total distributed
collections divided by total state administrative costs. Our explanatory
model consists of seven independent variables: percent of males aged
20-64 not employed, percent urban, percent of IV-D caseload currently
receiving TANF, percent of TANF heads under age 30, average expenditures
per IV-D case, population stability, and a dummy variable that indicates
whether the state passed the audit for the measure. All of the
independent variables were statistically significant at the five percent
level or better. In fact, with the exception of the audit variable, all
were significant at the one percent level. The model reported an R^2^
statistic of 0.55, which indicates that the independent variables
explained more than one-half of the variation in the cost-effectiveness
ratios reported by states during 1999-2000. We detail our findings for
each of the independent variables below.

-   *Males 20-64 not employed*. A one percentage point increase in the
    share of males aged 20-64 who do not work is associated with a
    \$0.13 decline in the cost-effectiveness ratio. Therefore, we
    predict that moving from 10 to 15 percent on this variable would be
    associated with a \$0.66 decline in cost-effectiveness.

-   *Percent urban*. A percentage point increase in the share of a
    state's population living in urban areas is associated with a modest
    \$0.01 decline in cost-effectiveness. Thus, if the state's
    urbanicity rate increased from 40 percent to 50 percent, we predict
    the cost-effectiveness ratio would decline by \$0.10.

-   *TANF less than 30*. A higher share of TANF cases headed by people
    under age 30 is associated with lower cost-effectiveness. A
    coefficient of --.10 suggests that an increase in the proportion of
    young heads from 40 percent to 50 percent is associated with a
    \$1.00 decline in the cost-effectiveness ratio.

-   *Caseload currently on TANF*. A higher share of IV-D cases currently
    receiving TANF is associated with lower cost-effectiveness. For each
    percentage point increase in the share of child support cases that
    receive TANF, the model predicts a \$0.05 reduction in
    cost-effectiveness. Thus, if the percentage of IV-D cases currently
    on TANF increased from 30 percent to 40 percent, the
    cost-effectiveness ratio would decline by \$0.50.

-   *Expenditure per case*. Higher expenditures per case are associated
    with lower cost-effectiveness. The coefficient is 0.0047; thus, a
    \$100 increase in spending per case would decrease the
    cost-effectiveness ratio by \$0.47.

-   *Population stability*. A more stable population is associated with
    a higher cost-effectiveness ratio. Specifically, each percentage
    point increase in the stability variable is associated with a \$0.15
    improvement in cost-effectiveness.

-   *Audit pass*. The dummy indicating a state passed the audit on
    cost-effectiveness suggests that---holding other factors
    constant---passing states reported cost-effectiveness ratios that
    were about \$1.71 higher than states that did not pass the audit.

### 4. Factors Associated with Percent of Cases Paying Toward Arrears

Relative to other performance indicators discussed above, we had
difficulty developing reliable explanations of the variations between
states in the percent of cases paying toward arrears. This could be
due---in large part---to the states' on-going difficulty in measuring
it. Two variables that showed significant relationships in other models
proved important here as well: poverty rates and the percent of the
state's population living in urban areas. In addition, the rate of job
growth in a state proved a significant predictor of a state's
performance on arrears. The share of IV-D cases on TANF and passing the
federal audit were included in the final model, but the findings were
not statistically significant. Together, five variables explained about
34 percent of the variation in arrearage score submitted by states.
Below, we outline our specific findings for the significant variables.

-   *State poverty*. Higher state poverty is associated with lower
    performance on cases paying towards arrears. A one percentage point
    increase in the state's poverty rate is associated with a 1.1
    percentage point decrease in the share of cases paying toward
    arrears. Thus, we predict that an increase in the poverty rate from
    15 percent to 20 percent would result in a 5.5 percentage point
    decline in cases paying towards arrears.

-   *Job growth*. High job growth is positively associated with cases
    paying toward arrears. A one percentage point increase in the rate
    of job growth in a state is associated with a 2.9 percentage point
    increase in the arrears performance measure.

-   *Percent urban*. A one percentage point increase in the proportion
    of the state's population in an urban area is associated with a 0.15
    percentage point reduction in the arrears measure. Thus, an increase
    in the urbanicity rate from 40 percent to 50 percent would be
    associated with a 1.5 percentage point reduction in a state's
    arrears score.

### 5. Factors Associated with Paternity Establishment

We had limited success in modeling performance on paternity
establishment. Problems arose because of multiple definitions of the
indicator, as well as the ongoing difficulty with data quality. First,
with respect to multiple definitions, federal law permits states to
calculate the measure in one of two ways, statewide or IV-D specific.
During 1999-2000, states were split almost evenly between statewide and
IV-D measures. For modeling purposes, we considered each measure
separately; therefore, we have roughly one-half the observations for
each of the paternity measures that we have for other performance
indicators.[^24] Compounding the problem of limited observations is the
fact that federal audits show the data quality is weak for these
paternity measures. In 2000, 13 states failed the audit on the paternity
measures.

Despite these limitations, we did develop explanatory models and found
some informative relationships. The model of the **IV-D paternity
measure** consisted of the state poverty rate, share of TANF heads under
age 30, share of IV-D cases that has never received TANF, and a dummy
variable indicating that the state passed the audit. The final model
explained 48 percent of the variation in the scores reported by states.
Two variables were statistically significant at the 5 percent or higher
level; the audit pass/fail variable was significant at the 10 percent
level.

-   *TANF less than 30*. A higher proportion of young case heads is
    associated with lower performance on this measure. Specifically, the
    model predicts that a one percentage point increase in the share of
    TANF heads under 30 would result in a 4.3 percentage point decline
    in the IV-D paternity score.

-   *Caseload never on TANF*. The model predicts that a state that
    increases the share of IV-D cases that *never* received TANF by one
    percentage point would experience a 0.9 percentage improvement in
    its IV-D paternity score.

-   *Audit pass*. The dummy indicating a state passed the audit on its
    cost-effectiveness suggests that---holding other factors
    constant---passing states reported paternity establishment
    percentages that were 16 percentage points higher than states that
    did not pass the audit.

For the **statewide paternity** **measure**, the final model consisted
of five explanatory variables: per capita personal income, percent of
IV-D cases currently receiving TANF, expenditures per case, population
stability, and an audit pass/fail dummy. The model explained about 33
percent of the variance. All the variables exhibited the signs that we
expected, and three were statistically significant at the 5 percent
level.

-   *Caseload currently on TANF*. A higher share of IV-D cases currently
    on TANF is associated with weaker performance. A one percentage
    point increase in cases on TANF is related to a 0.7 point decline in
    the statewide paternity score. Thus, we predict that an increase in
    cases currently on TANF from 20 percent to 25 percent of the
    caseload would be associated with a 3.5 percentage point drop in
    paternity establishment.

-   *Population stability*. A more stable population is associated with
    higher statewide paternity establishment. We predict that a one
    percentage point increase in our stability measure yields a 2
    percentage point increase in the statewide paternity score.

-   *Expenditure per case average*. Higher spending per case appears to
    be associated with better paternity outcomes. For example, an extra
    \$100 spent per case, on average, boosts the statewide paternity
    score by 5 percentage points.

### 6. Conclusions

Despite a relatively limited number of observations and evolving data
quality, we found a number of important associations that were
consistent with previous findings and expectations of program experts.
The coefficients of the explanatory variables are generally reasonable
in their magnitudes and are stable when we tested them in models with
alternate variables and years.

As noted above, we originally ran our models using only 1999 data. In
other words, the models were fitted to this data. When 2000 data became
available, we re-ran the models and found that they were generally
robust. The sign of the coefficients in each model remained stable.
Moreover, many of the variables remained significant at the 10 percent
level or higher. For example, in the cases with order model, five
variables were significant in 1999; three remained so in 2000. In the
collections on current support model, seven variables were significant
in 1999, as were five in 2000. In 1999 six of the seven variables in the
cost-effectiveness model were significant; in 2000, the same number were
significant, but the mix was different. The arrears model had four
significant variables in 1999 and two in 2000. The paternity models were
not as robust. Although the signs on the coefficients were stable, only
one two variables in the IV-D paternity model remained stable in 2000
while none of the four significant variables in the statewide model did
so.

We then combined the 1999 and 2000 data, assuming that more observations
would strengthen our models. We found significant associations within
each of our major variable categories: economic, demographic, and
programmatic (see ***Exhibit IV.4***).

Reviewing the economic variables, we found that a higher poverty rate is
associated with weaker performance on the cases with orders and
arrearage indicators. While poverty rates were not associated with
collection rates, a state's personal income per capita was, with higher
incomes related to increased shares of current support collected. The
rate of working age males not employed was associated with lower
collection rates and cost-effectiveness ratios. Finally, the higher the
rate of job growth in the state, the better states did on arrearage
performance.

Several demographic variables also showed significant and stable
relationships with performance. The higher is the share of the state's
population that is living in urban areas, the weaker is performance on
cases with orders, collection rates, cases paying towards arrears, and
cost-effectiveness. A stable population is associated with better
performance on the statewide paternity measure, cases with orders,
current collections, and cost-effectiveness. And, a larger proportion of
TANF case heads under age 30 appears to be associated with poorer
performance in current collections, cost-effectiveness, and paternity
establishment (IV-D).

With respect to programmatic variables, the models suggest that, as
caseload per worker falls, performance on cases with orders and current
collections improves. However, the more a state spends per case, the
worse it will fare on the cost-effectiveness measure. Measures of
relative caseload difficulty also showed significant associations with
performance. The higher is the share of IV-D families that currently
receive TANF, the weaker is performance on the statewide paternity,
cases with orders, current collections, arrearage, and
cost-effectiveness measures. Our measure of judicial and administrative
establishment processes proved significant for only one performance
indicator---suggesting that states that have administrative processes
systematically have a higher percentage of cases with orders.

Not surprisingly, the higher the quality of the performance data, the
more reliable and robust were the estimated relationships with
explanatory variables. Federal audit data show that the paternity and
arrearage measures have proven the most difficult for states to
calculate. Given the ongoing reporting problems, our models were able to
explain a small fraction of the variation among states for those
measures. By contrast, we were able to explain more than 70 percent of
the variation among states for the cases with orders measure with the
seven explanatory variables selected---a robust result for a cross
sectional model.

  ---------------- ------------ ----------------- ------------------- ----------------- ------------------- -------------------
  Exhibit IV.4:                                                                                             
  Summary of                                                                                                
  Independent                                                                                               
  Variables,                                                                                                
  Combined                                                                                                  
  1999-2000                                                                                                 
  Regressions                                                                                               

  **Independent    **Cases      **Current**       **Cost**            **Collections**   **IV-D Paternity**  **State Paternity**
  Variable**       with**                                                                                   

                   **Orders**   **Collections**   **Effectiveness**   **on Arrears**    **Establishment**   **Establishment**

  Personal Income               +++                                                                         O

  State Poverty    \-\--                                              \-\--             O                    

  Males 20-64 not               \-\--             \-\--                                                      
  Working                                                                                                   

  Job Growth                                                          +++                                    
  1998-1999                                                                                                 

  Percent Urban    \-\--        \--               \-\--               \-\--                                  

  \% TANF Heads                 \-\--             \-\--                                 \-\--                
  under 30                                                                                                  

  \% IV-D Caseload \--          \--               \-\--               \-                                    \--
  on TANF                                                                                                   

  \% IV-D Caseload                                                                      ++                   
  never on TANF                                                                                             

  Cases per FTE    \-\--        \-\--                                                                        

  Expenditures per                                \-\--                                                     ++
  Case 1999                                                                                                 

  \% Pop. in same  ++           +++               +++                                                       ++
  home 1999-2000                                                                                            

  Judicial Process \-\--                                                                                     

  Dummy            O            \-\--             ++                  O                 \-                  O

  R^2^             0.7320       0.6085            0.5547              0.3395            0.4797              0.3311

  Note: 6 states                                                                                            
  with no audit                                                                                             
  trails in 1999                                                                                            
  dropped from                                                                                              
  regressions.                                                                                              

                   **Key**                                                                                  

                   \+           Positive and                          \--               Negative and        
                                significant at                                          significant at 10%  
                                10% level                                               level               

                   ++           Positive and                          \--               Negative and        
                                significant at 5%                                       significant at 5%   
                                level                                                   level               

                   +++          Positive and                          \-\--             Negative and        
                                significant at 1%                                       significant at 1%   
                                level                                                   level               

                   O            In model, not                                           Not in model        
                                significant                                                                 
  ---------------- ------------ ----------------- ------------------- ----------------- ------------------- -------------------

# V. Applying study results to incentive policy

The regression models described in the previous section show how various
state economic, demographic, and programmatic factors affect states'
performance on the five measures specified by legislation to measure
performance. In this section, we discuss the rationales for making
adjustments to raw outcome measures, suggest one option for making
adjustments, and provide illustrations for how an adjustment procedure
might work in practice and what the effects of such systems might be.
The goal of this chapter is to illustrate one method for making
adjustments, but we do not make a policy recommendation for or against
making those adjustments. OCSE would need to carefully weight the costs
and benefits of any change to the incentive system. On the one hand,
state IV-D officials may perceive an amended system as more equitable if
they understand the adjustment method and trust the data that underlie
it. On the other hand, adjustments would add several complex estimation
steps to a process already complicated by a payment cap.

## A. Rationale for Adjustments

As might be expected, there is a great deal of variation in the levels
of the performance measures across states. For FY 2000, for example, the
proportion of cases with orders ranged from 26 percent to 93 percent,
and the proportion of current support collected ranged from 35 percent
to 76 percent. If we knew for certain that these large disparities all
resulted from variation in state effort or effectiveness, then it would
be appropriate to use unadjusted outcome measures to assess performance.
In reality, however, we know that many factors can affect the outcomes
of interest, and it may be desirable to adjust the expectation of
satisfactory performance based on these factors.

In describing why adjustments were made to performance standards for
local areas under the Job Training Partnership act (JTPA), the U.S.
Department of Labor explained the intent of the adjustment practice as
follows:[^25]

> Performance standards are adjusted to "level the playing field" by
> making the standards neutral with respect to who is served and to
> local economic conditions. For example, a \[service delivery area\]
> SDA serving a hard-to-serve population would be given a lower standard
> than an SDA serving a less hard-to-serve population. Although set at
> different levels, meeting these two standards would require the same
> level of SDA effort. Similarly, an SDA facing difficult local economic
> conditions might be given a lower standard than an SDA in a booming
> economy.[^26]^,^ [^27]

## B. Approaches for Making Adjustments

If the goal is to establish a level playing field, the adjustments
should be set to hold the states harmless for factors beyond their
control. The regression analyses we have estimated are intended to serve
this purpose, although, as we noted earlier in the report, the
regression analyses may be biased to the extent that relevant variables
are missing or measured with error.

There is one complication to the rules for using the regression analyses
to calculate adjusted performance. The regression models include
relevant demographic, economic, and programmatic variables, but
adjustments to performance should not take into account program
decisions that are under the control of the states. If the program
variables were omitted from the regression models, the estimates of the
impacts of the demographic and economic variables would generally be
biased, so it is important to include the programmatic variables (e.g.,
proportion of caseload on TANF, expenditures per case) in the
regressions. In making adjustments to performance, however, no
adjustment is made for the programmatic variables. In effect, the
regression coefficients of the programmatic variables are set to zero.
The programmatic variables omitted from the calculations are cases per
FTE (for cases with orders and collections), the variable measuring the
state's position on the judicial/administrative scale (for cases with
orders), the proportion of the caseload currently on TANF (for cases
with orders, collections, arrears, and cost-effectiveness), and
expenditures per case (for cost-effectiveness). In addition, the
variable in each regression indicating if a state passed the audit is
not used in computing adjusted performance.

For each outcome measure, the procedure for calculating adjusted
performance is the same. First, the mean national value of each
explanatory variable is subtracted from the state's value on that
variable. The resulting figure is then multiplied by the regression
coefficient for the model except for variables that are considered
management variables under the control of the state. For the variables
under the states' control, no adjustment is made. Next, these values are
summed for all the explanatory variables. Finally, the sum of the
adjustments is added to the state's actual value for the variable to
obtain the state's adjusted value for the performance measure. For
illustrative purposes, ***Exhibit V.1*** depicts a simplified version of
this process for the cases with order measure.

Exhibit V.1

Hypothetical Adjustment

We compare unadjusted and adjusted performance in several ways for each
outcome measure (except paternity establishment):

-   We present detailed adjustment calculations for two states for each
    measure to indicate how the adjustments change the scores and which
    explanatory factors are most important. *The adjustments are made to
    the states' FY 2000 scores*.

-   We show the raw scores and adjusted scores for all 45 states
    included in the regression analysis. Again, we use FY 2000 scores
    for each states.[^28]

-   We present a scatter diagram showing how closely the adjusted and
    unadjusted measures correspond. Less scatter is indicative of
    smaller adjustments being made.

-   We compute the Spearman rank-order correlation of the adjusted and
    unadjusted measures. The Spearman correlation coefficient provides a
    measure of how much the regression adjustments change the rankings
    of states; a coefficient of 1.0 indicates that there is no change,
    and a coefficient of --1.0 indicates that the rankings are
    completely reversed.

As we noted earlier, the results for the paternity establishment measure
are not conducive to this type of adjustment procedure because states
are permitted to use two different measures of paternity establishment.

## C. lllustrative Adjustments 

### 1. Cases with Orders

***Exhibit V.2*** shows how the adjusted levels of performance on the
cases with orders measure for two states, Arizona and Massachusetts. In
this model, there are three variables for which adjustments are made.
Arizona provides a good example where state conditions vary from the
national average in a manner that leads to an upward adjustment in their
score with the approach described here. First, Arizona has an
above-average poverty rate (13.53 percent compared to the national
average of 11.68 percent). The cases with orders regression model
indicates that Arizona's score should be adjusted up by 3.43 points
because of this deviation from the national average (--1.85) x
(--1.85119) = 3.43. Arizona has a higher than average percentage of its
population living in urban areas, 72.5 percent compared to a national
average of 51.9 percent, and this also leads to a positive adjustment in
their score for this factor of (--20.57) x (--0.22225) = 4.57 points.
Finally, Arizona scores below the national average on population
stability, and this factor has a positive coefficient in the cases with
orders model, so the adjustment is 4.88 x 0.8755 = 4.27 points. Thus,
for Arizona, all three factors would tend to depress the state's
performance, and using the model would lead to an adjustment of 3.43 +
4.57 + 4.27 = 12.28 points. Arizona's adjusted score would be 69.65
compared to its raw score of 57.37.

Exhibit V.2

Adjusted Levels of Performance: Cases with Orders, FY 2000

  ---------------------- -------------- ----------- ---------------- ----------------- -------------
  *FY 2000 National                                                                    
  Average: 65.20*                                                                      

                                                                                       

  **State: Arizona**                                                                   

  **FY 2000 Score:                                                                     
  57.37**                                                                              

                                                                                       

  **Econ/Demographic**   **National**                                                  

  **Variable**           **Average**    **State**   **Difference**   **Coefficient**   **Product**

  State Poverty Rate\*   11.68          13.53       --1.85           --1.85            3.43

  Percent Urban\*        51.90          72.48       --20.57          --0.22            4.57

  Pop. Stability 99-00\* 83.58          78.70       4.88             0.88              4.27

  \% IV-D cases on TANF  18.50          24.41       --5.91           0.00              0.00

  Cases per FTE\*        317.84         236.32      81.52            0.00              0.00

  Judicial/Admin\*       12.04          16.00       -3.96            0.00              0.00

  *Estimated Adjustment*                                                               *12.28*

  *Actual Score*                                                                       57.37

  *Adjusted Score*                                                                     69.65
  ---------------------- -------------- ----------- ---------------- ----------------- -------------

Exhibit V.2 (continued)

Adjusted Levels of Performance: Cases with Orders

  ---------------------- -------------- ----------- ---------------- ----------------- -------------
  **State:                                                                             
  Massachusetts**                                                                      

  **FY 2000 Score:                                                                     
  67.09**                                                                              

                                                                                       

  **Econ/Demographic**   **National**                                                  

  **Variable**           **Average**    **State**   **Difference**   **Coefficient**   **Product**

  State Poverty Rate\*   11.68          10.17       1.51             --1.85            --2.80

  Percent Urban\*        51.90          78.61       --26.71          --0.22            5.94

  Pop. Stability 99-00\* 83.58          86.90       --3.32           0.88              --2.90

  \% IV-D cases on TANF  18.50          23.56       --5.06           0.00              0.00

  Cases per FTE\*        317.84         303.18      14.66            0.00              0.00

  Judicial Process\*     12.04          16.00       --3.96           0.00              0.00

  *Estimated Adjustment*                                                               *0.23*

  *Actual Score*                                                                       *67.09*

  *Adjusted Score*                                                                     67.32

                                                                                       

  *\*Statistically                                                                     
  Significant at 10% or                                                                
  better*                                                                              
  ---------------------- -------------- ----------- ---------------- ----------------- -------------

The bottom half of the exhibit shows the adjustment process for cases
with orders for Massachusetts. For Massachusetts, the adjustments from
the regression model for cases with orders leads to a very small
adjustment---from a raw score of 67.09 to an adjusted score of 67.32. In
this case, the state's poverty rate and population stability would lead
to downward adjustments that are almost exactly offset by the high score
on percent urban for the state.

***Exhibit V.3*** shows actual scores, adjustments, and
regression-adjusted scores for the 45 states included in the regression
analysis. The number of states with upward and downward adjustments is
about equal---22 would see an upward adjustment to their payment while
23 states would experience a downward adjustment. For most states, the
adjustments are not large---30 states have an adjustment of less than
five points. Five states would have their scores adjusted down by over
10 points---three New England states (New Hampshire, Maine, and
Vermont), Iowa, and South Dakota. There are also five states that would
have their performance score increased by over 10 points---Texas,
Arizona, Louisiana, New Mexico, and the District of Columbia. The
adjustment for the District of Columbia is especially large, and would
increase measured performance from 26.2 to 48.45.

Given the complexity of the incentive system, the adjustments would not
translate into simple percentage increases or decreases in incentive
payments for each state. Rather, OCSE would have to use these "adjusted"
scores to calculate the incentive payments from scratch---taking into
account the overall cap on incentives. Also note that some states, even
with adjustments, would fail to receive incentive payments because they
fail to reach the minimum performance threshold for a payment---40
percent in this case. Illinois and New Mexico, for instance, have
adjusted scores of 32.06 and 39.50, respectively, so would receive zero
payments under this scenario. Additionally, states that did not pass the
2000 data reliability audit for this measure will receive no incentive
payment, regardless of their adjusted score. Hawaii falls into this
category (not shown).

Exhibit V.3

State Performance on Cases with Orders, FY 2000

  ------------------------ ------------ ---------------- ---------------------
  **STATE**                **Actual**   **Adjustment**   **Adjusted Score**

                                                         

  ALABAMA                  56.42        2.96             59.38

  ALASKA                   78.48        --3.67           74.81

  ARIZONA                  57.37        12.28            69.65

  ARKANSAS                 67.13        2.77             69.90

  COLORADO                 78.11        2.78             80.89

  CONNECTICUT              64.41        --4.62           59.79

  DELAWARE                 59.95        --0.51           59.44

  DIST. OF COL.            26.20        22.25            48.45

  FLORIDA                  47.46        9.31             56.77

  GEORGIA                  55.43        2.44             57.87

  HAWAII                   47.48        1.72             49.20

  IDAHO                    77.52        --1.50           76.02

  ILLINOIS                 30.01        2.05             32.06

  IOWA                     85.90        --11.56          74.34

  KENTUCKY                 64.74        --3.30           61.44

  LOUISIANA                50.06        12.61            62.67

  MAINE                    88.64        --13.36          75.28

  MARYLAND                 61.18        --4.12           57.06

  MASSACHUSETTS            67.09        0.23             67.32

  MICHIGAN                 70.49        --1.82           68.67

  MINNESOTA                74.76        --9.26           65.50

  MISSISSIPPI              49.16        0.48             49.64

  MISSOURI                 73.84        --1.63           72.21

  MONTANA                  83.86        1.84             85.70

  NEBRASKA                 74.95        --2.39           72.56

  NEW HAMPSHIRE            78.40        --13.67          64.73

  NEW JERSEY               74.01        --2.82           71.19

  NEW MEXICO               26.37        13.13            39.50

  NEW YORK                 65.53        6.90             72.43

  NORTH CAROLINA           56.65        --0.06           56.59

  NORTH DAKOTA             75.78        --2.91           72.87

  OKLAHOMA                 61.91        4.10             66.01

  OREGON                   66.29        5.81             72.10

  RHODE ISLAND             51.00        0.99             51.99

  SOUTH CAROLINA           65.66        --2.49           63.17

  SOUTH DAKOTA             92.59        --11.04          81.55

  TENNESSEE                43.00        1.00             44.00

  TEXAS                    55.78        11.37            67.15

  UTAH                     79.56        0.42             79.98

  VERMONT                  84.94        --12.51          72.43

  VIRGINIA                 67.85        --4.10           63.75

  WASHINGTON               89.35        2.33             91.68

  WEST VIRGINIA            61.28        --2.02           59.26

  WISCONSIN                76.56        --6.01           70.55

  WYOMING                  71.01        --4.39           66.62
  ------------------------ ------------ ---------------- ---------------------

> Note: Adjustments made to FY 2000 performance scores.

The scatter diagram in ***Exhibit V.4*** illustrates how the raw and
adjusted scores tend to be closely associated---states with low raw
scores tend to have low adjusted scores and states with high raw scores
tend to have high adjusted scores. The extent to which state rankings
change from the adjustment procedure used here can be measured with the
Spearman rank order correlation. If all rankings remain the same after
the adjustments, i.e., no state's relative position is changed, then the
Spearman correlation will be 1.0; if the adjustments completely reverse
the rankings, then the Spearman correlation would be --1.0. (See
***Appendix G*** for the Spearman correlation data.) The Spearman
correlation of 0.897 indicates that the relative rankings of the states
are mostly the same with and without the adjustments. Thus, an
adjustment would not change the order of states. Those that ranked high
on the cases with order measure prior to the adjustment do so after it.

Exhibit V.4

Scatter Plot for Cases with Orders

![](media/image1.wmf){width="6.49375in" height="5.238194444444445in"}

### 2. Percent of Current Support Paid

We conducted similar analyses for the percent of current support paid.
***Exhibit V.5*** shows how the model could be used to adjust scores for
Arizona and Massachusetts. In this model there are five explanatory
variables that can be used to adjust measured performance: personal
income, percent of males 20 to 64 who are not employed, percent urban,
percent of the TANF caseload under age 30, and population stability.
Once again, Arizona is a good example of a state where conditions might
warrant an upward adjustment. Personal income in Arizona is below the
national average, \$24,991 compared to a national average of \$28,065.
Because the regression model indicates that each dollar of personal
income increases collections on child support, Arizona receives an
upward adjustment of 1.77 points for its below-average personal income.
The proportion of males age 20 to 64 not employed in Arizona is slightly
above the national average, and the state receives a small upward
adjustment of .53 points for this variable. Arizona's population is
highly concentrated in urban areas, and the model indicates that this
would lead to lower expected performance. This results in an upward
adjustment of 1.96 points. Having a higher proportion of TANF recipients
under age 30 leads to lower performance on this measure, and because
Arizona's proportion of TANF recipients under age 30 is below the
national average, the adjustment for this factor is negative. Finally,
Arizona had below-average population stability, which leads to an upward
adjustment of 5.34 points. Overall, when Arizona's levels of these five
explanatory variables are taken into account, the regression model leads
to an adjustment of 8.60 points in Arizona's measured performance for
percent of current support paid, from 44.65 to 53.25.

Exhibit V.5

Adjusted Levels of Performance: Current Collections, FY 2000

  ---------------------- -------------- ----------- ---------------- ----------------- -------------
  *FY 2000 National                                                                    
  Average: 55.20*                                                                      

                                                                                       

  **State: Arizona**                                                                   

  **FY 2000 Score:                                                                     
  44.65**                                                                              

                                                                                       

  **Econ/Demographic**   **National**                                                  

  **Variable**           **Average**    **State**   **Difference**   **Coefficient**   **Product**

  Per Capita Pers.       28,065.20      24,991.00   3,074.20         0.00              1.77
  Inc.\*                                                                               

  Men 20-64 not          15.62          16.15       --0.53           --0.99            0.53
  Working\*                                                                            

  TANF Head under 30\*   51.94          49.70       2.24             --0.45            --1.00

  Pop. Stability 99-00\* 83.58          78.70       4.88             1.09              5.34

  Percent Urban          51.90          72.48       --20.57          --0.10            1.96

  Cases per FTE\*        317.84         236.32      81.52            0.00              0.00

  \% IV-D cases on       18.50          24.41       --5.91           0.00              0.00
  TANF\*                                                                               

  *Estimated Adjustment*                                                               *8.60*

  *Actual Score*                                                                       44.65

  *Adjusted Score*                                                                     53.25
  ---------------------- -------------- ----------- ---------------- ----------------- -------------

  ---------------------- -------------- ----------- ---------------- ----------------- -------------
  **State:                                                                             
  Massachusetts**                                                                      

  **FY 2000 Score:                                                                     
  58.72**                                                                              

                                                                                       

  **Econ/Demographic**   **National**                                                  

  **Variable**           **Average**    **State**   **Difference**   **Coefficient**   **Product**

  Per Capita Pers.       28,065.20      37,710.00   --9,644.80       0.00              --5.55
  Inc.\*                                                                               

  Men 20-64 not          15.62          15.09       0.53             --0.99            --0.53
  Working\*                                                                            

  TANF Head under 30\*   51.94          52.30       --0.36           --0.45            0.16

  Pop. Stability 99-00\* 83.58          86.90       --3.32           1.09              --3.63

  Percent Urban          51.90          78.61       --26.71          --0.10            2.54

  Cases per FTE\*        317.84         303.18      14.66            0.00              0.00

  \% IV-D cases on       18.50          23.56       --5.06           0.00              0.00
  TANF\*                                                                               

  *Estimated Adjustment*                                                               *--7.00*

  *Actual Score*                                                                       58.72

  *Adjusted Score*                                                                     51.72

                                                                                       

  *\*Statistically                                                                     
  Significant at 10% or                                                                
  better*                                                                              
  ---------------------- -------------- ----------- ---------------- ----------------- -------------

Massachusetts, once again, scores high on characteristics that lead to
high performance on this measure. The state's scores relative to the
national average on personal income, percent of males age 20 to 64 who
are not employed, and population stability lead to downward adjustments
in measured performance, while the state's scores on percent who live in
urban areas and the percent of the TANF caseload under age 30 lead to
upward adjustments. The combined effect for all the explanatory
variables is for a downward adjustment of 7.00 points, from 58.72 to
51.72.

***Exhibit V.6*** shows actual scores, adjustments, and
regression-adjusted scores for the 45 states included in the regression
analysis for the analysis of current support paid. Twenty-one states
would experience a downward adjustment, while 24 would receive higher
incentive payments. Once again, the majority of states do not have large
adjustments---23 of the 45 states analyzed have adjustments less than 5
points. New Hampshire and Connecticut have downward adjustments more
than 10 points, and no states have upward adjustments as large as 10
points. Thus, the adjustments for percent of support paid do not include
as many large adjustments as for cases with orders.

Again, a number of states would not receive incentive payments, despite
the adjustments. Both Illinois and South Carolina had adjusted scores
below the 40 percent performance threshold (34.14 percent and 38.72
percent, respectively). Additionally, audits found the data from New
York, West Virginia, and Wisconsin to be unreliable for this measure
(not shown). Thus, these states would also receive no incentive payments
related to their performance on the current collections measure.

Exhibit V.6

State Performance on Collections, FY 2000

  ------------------------- ------------ ---------------- ---------------------
  **STATE**                 **Actual**   **Adjustment**   **Adjusted Score**

                                                          

  ALABAMA                   35.15        9.63             44.78

  ALASKA                    49.18        2.62             51.80

  ARIZONA                   44.65        8.60             53.25

  ARKANSAS                  47.39        9.59             56.98

  COLORADO                  50.72        1.36             52.08

  CONNECTICUT               53.48        --10.61          42.87

  DELAWARE                  58.19        --0.84           57.35

  DIST. OF COL.             44.35        4.26             48.61

  FLORIDA                   49.88        5.07             54.95

  GEORGIA                   47.51        2.23             49.74

  HAWAII                    49.94        --0.22           49.72

  IDAHO                     52.68        1.28             53.96

  ILLINOIS                  36.48        --2.34           34.14

  IOWA                      62.37        --2.54           59.83

  KENTUCKY                  50.91        5.02             55.93

  LOUISIANA                 52.39        9.89             62.28

  MAINE                     57.30        --7.04           50.26

  MARYLAND                  58.52        --6.42           52.10

  MASSACHUSETTS             58.72        --7.00           51.72

  MICHIGAN                  67.25        --9.66           57.59

  MINNESOTA                 68.25        --8.28           59.97

  MISSISSIPPI               49.09        8.55             57.64

  MISSOURI                  47.83        4.24             52.07

  MONTANA                   56.80        --0.12           56.68

  NEBRASKA                  60.63        --1.74           58.89

  NEW HAMPSHIRE             65.03        --11.07          53.96

  NEW JERSEY                63.14        --9.72           53.42

  NEW MEXICO                40.36        5.95             46.31

  NEW YORK                  73.64        --7.18           66.46

  NORTH CAROLINA            59.21        2.72             61.93

  NORTH DAKOTA              67.13        --3.30           63.83

  OKLAHOMA                  44.32        7.57             51.89

  OREGON                    59.65        2.93             62.58

  RHODE ISLAND              53.70        --3.35           50.35

  SOUTH CAROLINA            37.82        1.90             39.72

  SOUTH DAKOTA              67.67        --6.54           61.13

  TENNESSEE                 44.91        4.79             49.70

  TEXAS                     65.10        2.68             67.78

  UTAH                      55.49        2.21             57.70

  VERMONT                   65.60        --9.19           56.41

  VIRGINIA                  56.50        --1.92           54.58

  WASHINGTON                60.29        1.27             61.56

  WEST VIRGINIA             62.44        6.30             68.74

  WISCONSIN                 76.64        0.13             76.77

  WYOMING                   55.81        --1.69           54.12

  Note: Adjustments Made to                               
  FY 2000 performance                                     
  scores.                                                 
  ------------------------- ------------ ---------------- ---------------------

The scatter diagram for actual and adjusted scores for percent of
current support paid is shown in ***Exhibit V.7***. Again, the points in
the diagram indicate that states with low actual scores generally still
have low scores after the adjustment is made. The Spearman rank order
correlation for percent of current support paid is 0.737, which, while
as not as strong a correlation as for cases with orders, is still
reasonably high.

Exhibit V.7

Scatter Plot for Collections

![](media/image2.wmf){width="4.927777777777778in"
height="4.278472222222222in"}

### 3. Percent of Cases Paying Toward Arrears

In this section we present the same material for the percent of cases
paying toward arrears performance measure. ***Exhibit V.8*** shows the
adjustments for Arizona and Massachusetts. The regression model for
estimating percent of cases paying toward arrears has only three
explanatory variables beyond the state's control---percentage of the
population in poverty, job growth, and percent urban. For this
performance measure, the adjustments differ from the two measures we
have examined previously. First, the directions of the adjustments are
reversed---the adjustment for Arizona is negative and the adjustment for
Massachusetts is positive. Second, the adjustments are quite small. In
both states the adjustments are less than two percentage points.

Exhibit V.8

Adjusted Levels of Performance: Arrearages, FY 2000

  ---------------------- -------------- ----------- ---------------- ----------------- -------------
  *FY 2000 National                                                                    
  Average: 59.63*                                                                      

                                                                                       

  **State: Arizona**                                                                   

  **FY 2000 Score:                                                                     
  50.67**                                                                              

                                                                                       

  **Econ/Demographic**   **National**                                                  

  **Variable**           **Average**    **State**   **Difference**   **Coefficient**   **Product**

  State Poverty Rate\*   11.68          13.53       --1.85           --1.14            2.11

  Job Growth 98-99\*     1.45           3.70        --2.25           2.93              --6.59

  Percent Urban\*        51.90          72.48       --20.57          --0.15            3.12

  \% IV-D cases on TANF  18.50          24.41       --5.91           0.00              0.00

  *Estimated Adjustment*                                                               *--1.35*

  *Actual Score*                                                                       50.67

  *Adjusted Score*                                                                     49.32
  ---------------------- -------------- ----------- ---------------- ----------------- -------------

  ---------------------- -------------- ----------- ---------------- ----------------- -------------
  **State:                                                                             
  Massachusetts**                                                                      

  **FY 2000 Score:                                                                     
  55.34**                                                                              

                                                                                       

  **Econ/Demographic**   **National**                                                  

  **Variable**           **Average**    **State**   **Difference**   **Coefficient**   **Product**

  State Poverty Rate\*   11.68          10.17       1.5141           --1.14            --1.73

  Job Growth 98-99\*     1.45           1.70        --0.2467         2.93              --0.72

  Percent Urban\*        51.90          78.60       --26.6994        --0.15            4.05

  \% IV-D cases on TANF  18.50          23.56       --5.0556         0.00              0.00

  *Estimated Adjustment*                                                               *1.60*

  *Actual Score*                                                                       55.34

  *Adjusted Score*                                                                     56.94

                                                                                       

  *\*Statistically                                                                     
  Significant at 10% or                                                                
  better*                                                                              
  ---------------------- -------------- ----------- ---------------- ----------------- -------------

***Exhibit V.9*** shows actual scores, adjustments, and
regression-adjusted scores for the 45 states included in the regression
analysis for the analysis of current support paid. Twenty-three states
would experience an upward adjustment, while 22 would have a lower
payment. The adjustments for this model are generally quite small. Only
seven states had adjustments that exceed five points, and only one
state, the District of Columbia, has an adjustment greater than 10
points. For the most part, then, the regression model for percent of
cases paying toward arrears makes only small adjustments to states'
measured performance.

One state, Hawaii, would fail to get an incentive payment even with an
adjustment because its score, 36.11 percent, is below the required 40
percent performance threshold for payments. Moreover, Kentucky, West
Virginia, and Wisconsin failed their audits on this measure, so would
also receive no incentive payment.

Exhibit V.9

State Performance on Arrears, FY 2000

  ------------------------- ------------ ---------------- ---------------------
  **STATE**                 **Actual**   **Adjustment**   **Adjusted Score**

                                                          

  ALABAMA                   48.72        6.71             55.43

  ALASKA                    65.11        --5.05           60.06

  ARIZONA                   50.67        --1.35           49.32

  ARKANSAS                  56.64        --1.33           55.31

  COLORADO                  62.51        --3.00           59.51

  CONNECTICUT               56.13        0.28             56.41

  DELAWARE                  67.16        0.17             67.33

  DIST. OF COL.             33.17        15.12            48.29

  FLORIDA                   81.81        --2.12           79.69

  GEORGIA                   73.22        1.48             74.70

  HAWAII                    37.22        --1.11           36.11

  IDAHO                     56.12        --7.87           48.25

  ILLINOIS                  48.86        4.24             53.10

  IOWA                      54.76        --8.31           46.45

  KENTUCKY                  82.66        --2.11           80.55

  LOUISIANA                 57.00        9.58             66.58

  MAINE                     68.67        --7.74           60.93

  MARYLAND                  59.88        --3.33           56.55

  MASSACHUSETTS             55.34        1.60             56.94

  MICHIGAN                  60.03        3.05             63.08

  MINNESOTA                 70.02        --4.18           65.84

  MISSISSIPPI               58.90        6.77             65.67

  MISSOURI                  47.33        --0.73           46.60

  MONTANA                   66.73        0.26             66.99

  NEBRASKA                  56.81        4.56             61.37

  NEW HAMPSHIRE             64.08        --4.54           59.54

  NEW JERSEY                56.16        1.54             57.70

  NEW MEXICO                55.47        5.72             61.19

  NEW YORK                  59.82        6.50             66.32

  NORTH CAROLINA            70.73        2.11             72.84

  NORTH DAKOTA              57.81        2.69             60.50

  OKLAHOMA                  52.18        1.83             54.01

  OREGON                    55.49        3.64             59.13

  RHODE ISLAND              61.03        2.77             63.80

  SOUTH CAROLINA            56.57        --1.74           54.83

  SOUTH DAKOTA              76.31        --4.73           71.58

  TENNESSEE                 47.88        2.82             50.70

  TEXAS                     63.44        2.05             65.49

  UTAH                      64.45        --3.12           61.33

  VERMONT                   70.24        --8.42           61.82

  VIRGINIA                  54.22        --4.14           50.08

  WASHINGTON                64.34        --1.95           62.39

  WEST VIRGINIA             54.66        2.07             56.73

  WISCONSIN                 65.97        --3.13           62.84

  WYOMING                   56.88        --7.54           49.34
  ------------------------- ------------ ---------------- ---------------------

> Note: Adjustments Made to FY 2000 performance scores.

The scatter diagram for actual and adjusted scores for percent of
current support paid is shown in ***Exhibit V.10***. Again, the points
in the diagram indicate that states with low actual scores generally
still have low scores after the adjustment is made. The Spearman rank
order correlation for percent of current support paid is 0.848,
indicating that for the most part, rankings are retained when the model
adjustments are made.

Exhibit V.10

Scatter Plot for Arrears

![](media/image3.wmf){width="5.297916666666667in" height="4.51875in"}

### 4. Cost-effectiveness

***Exhibit V.11*** shows the adjustments for Arizona and Massachusetts.
As in the first two outcomes considered, the adjustment is upward for
Arizona and downward for Massachusetts. Although the magnitudes of the
adjustments appear small compared to the previous scenarios, it should
be kept in mind that the cost-effectiveness outcome variable has a much
smaller range than the three other outcomes.

Exhibit V.11

Adjusted Levels of Performance: Cost-effectiveness

  ---------------------- -------------- ----------- ---------------- ----------------- -------------
  *FY 2000 National                                                                    
  Average: \$4.16*                                                                     

                                                                                       

  **State: Arizona**                                                                   

  **FY 2000 Score:                                                                     
  \$3.72**                                                                             

                                                                                       

  **Econ/Demographic**   **National**                                                  

  **Variable**           **Average**    **State**   **Difference**   **Coefficient**   **Product**

  Men 20-64 not          15.62          16.15       --0.53           --0.13            0.07
  Working\*                                                                            

  TANF Head under 30\*   51.94          49.70       2.24             --0.10            --0.22

  Pop. Stability 99-00\* 83.58          78.70       4.88             0.15              0.73

  Percent Urban\*        51.90          72.48       --20.57          --0.01            0.31

  Expenditures per case  317.84         236.32      81.52            0.00              0.00
  99\*                                                                                 

  \% IV-D cases on       18.50          24.41       -5.91            0.00              0.00
  TANF\*                                                                               

  *Estimated Adjustment*                                                               *0.88*

  *Actual Score*                                                                       3.72

  *Adjusted Score*                                                                     4.60
  ---------------------- -------------- ----------- ---------------- ----------------- -------------

  ---------------------- -------------- ----------- ---------------- ----------------- -------------
  **State:                                                                             
  Massachusetts**                                                                      

  **FY 2000 Score:                                                                     
  \$3.5**                                                                              

                                                                                       

  **Econ/Demographic**   **National**                                                  

  **Variable**           **Average**    **State**   **Difference**   **Coefficient**   **Product**

  Men 20-64 not          15.62          15.09       0.53             --0.13            --0.07
  Working\*                                                                            

  TANF Head under 30\*   51.94          52.30       --0.36           --0.10            0.04

  Pop. Stability 99-00\* 83.58          86.90       --3.32           0.15              --0.50

  Percent Urban\*        51.90          78.61       --26.71          --0.01            0.40

  Expenditures per case  317.84         303.18      14.66            0.00              0.00
  99\*                                                                                 

  \% IV-D cases on       18.50          23.56       --5.06           0.00              0.00
  TANF\*                                                                               

  *Estimated Adjustment*                                                               *--0.13*

  *Actual Score*                                                                       3.50

  *Adjusted Score*                                                                     3.37

                                                                                       

  *\*Statistically                                                                     
  Significant at 10% or                                                                
  better*                                                                              
  ---------------------- -------------- ----------- ---------------- ----------------- -------------

As shown in ***Exhibit V.12***, the cost-effectiveness variable ranges
from 1.31 to 6.95. Twenty-four states would receive an upward adjustment
on this measure. The adjustments, which range from --1.86 to 1.41, are
on a proportionate basis much larger than for the other outcomes
considered.

One state, New Mexico, would fail to receive a payment despite the
adjustment. The state failed to reach the threshold necessary for a
payment (\$2.00), and it failed the audit.

Exhibit V.12

State Performance on Cost-effectiveness, FY 2000

  ---------------------- ------------ ---------------- -----------------------
  **STATE**              **Actual**   **Adjustment**   **Adjusted Score**

                                                       

  ALABAMA                3.66         1.39             5.05

  ALASKA                 3.89         0.03             3.92

  ARIZONA                3.72         0.88             4.60

  ARKANSAS               3.28         1.09             4.37

  COLORADO               3.23         0.54             3.77

  CONNECTICUT            3.75         --0.34           3.41

  DELAWARE               3.19         0.27             3.46

  DIST. OF COL.          2.64         1.41             4.05

  FLORIDA                3.45         0.59             4.04

  GEORGIA                3.72         0.39             4.11

  HAWAII                 4.54         --0.41           4.13

  IDAHO                  4.32         --0.08           4.24

  ILLINOIS               2.42         --0.06           2.36

  IOWA                   4.24         --0.29           3.95

  KENTUCKY               4.02         0.37             4.39

  LOUISIANA              4.92         1.17             6.09

  MAINE                  4.90         --1.44           3.46

  MARYLAND               3.60         --0.57           3.03

  MASSACHUSETTS          3.50         --0.13           3.37

  MICHIGAN               5.52         --1.86           3.66

  MINNESOTA              4.11         --0.81           3.30

  MISSISSIPPI            4.92         0.85             5.77

  MISSOURI               3.37         0.87             4.24

  MONTANA                3.58         --0.56           3.02

  NEBRASKA               3.78         --0.12           3.66

  NEW HAMPSHIRE          4.82         --1.12           3.70

  NEW JERSEY             4.60         --0.68           3.92

  NEW MEXICO             1.31         0.22             1.53

  NEW YORK               4.90         --0.81           4.09

  NORTH CAROLINA         3.86         0.55             4.41

  NORTH DAKOTA           4.61         --0.69           3.92

  OKLAHOMA               2.83         0.87             3.70

  OREGON                 5.54         0.12             5.66

  RHODE ISLAND           4.44         --0.39           4.05

  SOUTH CAROLINA         5.08         --0.09           4.99

  SOUTH DAKOTA           6.95         --1.15           5.80

  TENNESSEE              4.85         0.66             5.51

  TEXAS                  4.96         0.39             5.35

  UTAH                   3.47         0.01             3.48

  VERMONT                4.02         --1.50           2.52

  VIRGINIA               5.00         0.12             5.12

  WASHINGTON             4.53         0.10             4.63

  WEST VIRGINIA          4.15         0.16             4.31

  WISCONSIN              6.51         0.31             6.82

  WYOMING                4.33         --0.26           4.07
  ---------------------- ------------ ---------------- -----------------------

Note: Adjustments made to FY 2000 performance scores.

The scatter diagram for actual and adjusted scores for
cost-effectiveness is shown in ***Exhibit V.13***. Again, the points in
the diagram indicate that states with low actual scores generally still
have low scores after the adjustment is made, but the points are more
scattered than for the other outcomes examined. The Spearman rank order
correlation for percent of current support paid is 0.561. Although still
positive, the lower rank order correlation is indicative of the fact
that states are more likely to change rank for the cost-effectiveness
variable than for the other outcomes considered.

Exhibit V.13

Scatter Plot for Cost-effectiveness

![](media/image4.wmf){width="5.001388888888889in"
height="4.277777777777778in"}

## D. Conclusions 

The results reported above show that for four of the five outcome
measures used to measure performance of the states for their child
support enforcement programs it is feasible to develop regression models
to adjust expected performance for exogenous factors. The models we have
developed provide, for the most part, modest adjustments to measured
performance that take account of factors in the environment that are
beyond the state's control. This is the logic that was used to develop a
regression-based adjustment system for the Job Training Partnership Act.
In that program, state and local governments generally perceived the
system to be fair, and the adjustment system was widely accepted.

There are, however, a number of possible concerns that should be
addressed before adopting a regression-based adjustment system:

-   In some cases, it is important to set standards at absolute levels
    and not make adjustments to account for differences across states.
    In setting standards for reading and arithmetic, for example, many
    would argue that whatever the background of children in a state, the
    educational system must bring them up to a specific reading level.

-   There are no definitive models of the outcomes used to measure
    success in the child support enforcement system. Thus, in this and
    our previous effort in this area, programmatic and research experts
    suggested a wide range of variables to consider for the models. Many
    of the variables did not perform well in our statistical models,
    sometimes having the opposite effect of what was expected, and in
    other cases the estimated effects were as likely to be due to chance
    as to be real impacts. Thus, developing good models is as much an
    art as a science, and care must be taken to develop reasonably
    accurate models.

-   Some variables of interest are not measured very frequently or may
    not be measured at all. Thus, while good recent measures of income
    and unemployment are available for all states, measures of job
    growth and other variables are somewhat dated. The number of poor
    female-headed households in a state cannot be measured well except
    in decennial census years. The possibilities of omitted variables
    and measurement error mean that the models developed may have biased
    coefficients.

-   The relationships among the explanatory variables and the outcomes
    may change over time. Thus, while adding more years to the analysis
    generally makes the models more robust, if the relationships change
    including prior years in the models could lead to bias.

Thus, although it is clear that state demographic and economic factors
are related to child support enforcement outcomes and that it is
feasible to develop statistical adjustment models for performance,
actual implementation of such a system depends both on careful thought
about the intent of the performance measurement system and the technical
tradeoffs between using statistical adjustment models and using
unadjusted measures.

# Vi. Summary of major findings

## A. Summary

This report responds to a Congressional mandate to describe how
economic, demographic, and programmatic factors affect the performance
of state IV-D programs. We defined state performance through the five
measures created in CSPIA that underlie the programs incentive funding
program:

-   Paternity establishment;

-   Cases with orders;

-   Current collections;

-   Cases paying toward arrears; and

-   Cost-effectiveness.

Underlying the analyses were the performance data reported by states in
Fiscal Years 1999 and 2000. To isolate the relationships between child
support program performance and determinants, we developed a regression
model for each performance measure. In addition, we describe how OCSE
could use the findings from our analyses to adjust each state's
performance score and, in essence, hold states harmless for economic and
demographic characteristics that affect performance but over which they
have no control.

Through a review of the academic literature and discussions with
federal, state, and local program experts, we identified over 50
variables to explain state performance in the five areas. These
variables fell into three broad categories: economic, demographic, and
programmatic. Economic characteristics, such as personal income per
capita and employment rates, gauge the relative ease that non-custodial
and custodial parents will encounter in securing and keeping jobs to
support their children. Demographic characteristics, including migration
rates and urbanicity, indicate populations that IV-D officials have
identified as inherently easy or difficult to serve. Finally,
programmatic characteristics, like staffing levels or degree of program
universality, measured aspects of programs determined in large part by
state policy and funding decisions.

Our final models relied on 12 economic, demographic, and programmatic
variables, as well as one dummy variable. We employed different
combinations of these variables to predict performance for each
indicator, and no model used all of them. The stability and ultimate
reliability of the models varied across the performance indicators. For
example, the model for cases with orders explained more than 73 percent
of the variation in the performance scores reported by states. The model
consisted of seven independent variables---all but one statistically
significant. On the other hand, our model for the statewide paternity
establishment indicator explained only 33 percent of the variation in
state scores.

While the models do appear to confirm several existing theories about
the determinants of child support performance, *the results should be
interpreted with caution*. As discussed previously, we conducted the
analyses on the first two years of federally audited performance data:
fiscal years 1999 and 2000 and consequently, the quality of the
performance data is still evolving. In FY 1999, six states failed to
provide a reliable audit trail---and therefore failed the audits for all
of their indicators---and many other states failed audit tests on
individual indicators. While data were somewhat more reliable in FY
2000, federal and state officials see continued room for improvement. In
addition to the evolving quality of the performance data, relatively
imprecise measures of economic and demographic factors also hinder the
analysis. For example, it would be useful to explore the link between
employment rates specific to non-custodial parents and program
performance. But, in the absence of this ideal measure, we relied on
employment rates for all working-age males in a state---regardless of
their child support status. In some cases these measures exhibited
strong and stable relationships with state child support performance,
and in other cases, they did not.

Further study into the economic and demographic determinants of child
support performance will be facilitated as states improve their data
reporting and as detailed explanatory variables become available over
time. OCSE auditors review each state's data submission of performance
data annually and pinpoint flaws in its completeness and reliability. As
states become more familiar with the audit process, performance data may
become more accurate, and studies of this nature would become more
reliable. Moreover, a proposed interagency research data warehouse may
produce economic and demographic data that are more specific to the IV-D
population. The warehouse---which would link new hire, quarterly wage,
unemployment insurance, TANF, and Medicaid databases---could ultimately
generate employment and demographic information specific to a state's
IV-D clients. Such data could prove highly valuable in understanding and
predicting performance.

## B. Key Findings

Below, we discuss the key findings that emerged from our analyses.

**A robust economy is associated with better performance.** We
ultimately settled on four economic indicators in our final models:
poverty rate, personal income per capita, job growth, and the employment
rate of working-age males. We included at least one of these indicators
in each of our final regression models, and they were statistically
significant in most cases. However, no single indicator performed well
across all models. Specifically:

-   A higher poverty rate was associated with weaker child support
    program performance on the cases with orders and arrearage measures.

-   Per capita personal income did a better job in predicting rates of
    current collections, with higher personal incomes linked to better
    performance.

-   A higher rate of males ages 24-60 not working depressed performance
    on current collections and cost-effectiveness.

-   A higher rate of job growth was associated with better performance
    on the arrearage measure.

**Demographic factors play a role in state performance**. We explored
stability of the local population, percent of the population living in
urban areas, and the percent of TANF heads under age 30. Consistent with
our previous work and related studies in the area, we found:

-   A higher share of urban dwellers is associated with weaker
    performance in each of the models for which it was tested: cases
    with orders, current collections, cases with collections on arrears,
    and cost-effectiveness. We speculate the urban variable is serving
    as a proxy for a host of more specific characteristics that more
    directly influence child support outcomes (e.g., non-marriage
    births, crime, and incarceration rates).

-   Population stability, a variable not explored in previous research,
    exhibits strong and stable relationships with the statewide
    paternity measure, cases with orders, current collections, and
    cost-effectiveness. We found that the more stable a state's
    population---as evidenced by the share who remain in the same house
    from one year to the next---the better is the state performance.

-   States with younger TANF case heads exhibited weaker performance for
    IV-D paternity establishment, current collections, and
    cost-effectiveness.

**Several programmatic factors---determined by state and agency
policies---appear to be related to child support enforcement.** We
explored program universality, cases per full-time equivalent staff,
child support expenditures per case, and processes for establishing
orders.

-   Our findings are consistent with the hypothesis that states that
    serve a large number of non-TANF clients should report better
    performance than programs that primarily serve current recipients of
    cash assistance. Specifically, we find that states with a higher
    share of IV-D cases receiving TANF exhibit weaker performance on the
    statewide paternity, case with orders, current collections,
    arrearages, and cost-effectiveness measures.

-   We found that staff resources devoted to enforcement---expressed in
    terms of cases per FTE are also related to performance.
    Specifically, the lower is the ratio of cases to total program
    staff, the better is performance in cases with orders and current
    collections.

-   Another measure of resources---average IV-D expenditure per
    case---is related to better performance for on the statewide
    paternity measure but weakens the cost-effectiveness ratio.

-   The process by which states establish child support orders appears
    related to their performance on case with orders. Specifically,
    having an administrative process is associated with better
    performance in order establishment.

**States will face tradeoffs in attempting to maximize overall
performance.** Officials will likely discover an inherent tradeoff
between cost-effectiveness and the other performance measures. For
example, if states increased staffing levels in an attempt to boost case
establishment or current collection rates, they would likely increase
spending per case, which could decrease their cost-effectiveness ratios.

**Adjustments to state performance scores would be feasible at this time
for four of the five of the indicators.** Using the findings from the
models, OCSE could adjust state performance for all but the paternity
establishment measure so as to hold states harmless for economic and
demographic factors that appear associated with child support
performance but over which program directors have no control. For
example, states with characteristics that are linked with weaker child
support enforcement performance (e.g., higher-than-average state poverty
rates, lower-than average per capita personal incomes, and high levels
of in- or out-migration) would see upward adjustments, while states with
strong economies and stable populations would receive downward
adjustments. The Department of Labor employed a similar type of
adjustment process in its allocation of funds under the Job Training
Partnership Act program. Advantages and disadvantages would be inherent
in an adjustment process.

-   Advantages include an increased perception of equity in the
    incentive funding system, particularly among states that perceive
    themselves as penalized by factors beyond their control (e.g., weak
    economies).

-   Disadvantages would stem from mistrust of the regression models, and
    their underlying data, employed to make the adjustments. Moreover,
    the process for determining state incentive payments is already long
    and complex. Adjusting state scores based on economic and
    demographic factors would lengthen the time of the process, thus
    delaying the payment of incentives. This is due to the interactive
    nature of the incentive system. A capped incentive pool means that
    an upward adjustment to one state would have to be matched by a
    same-size downward adjustment in other states.

**Further research will be necessary**. This study is based on two years
of data. The original modeling used FY 1999 data. We re-ran the
regressions using FY 2000 data and found that most, but not all, of the
relationships remained stable. By combining FY 1999 and 2000 data, we
increased the sample size. This resulted in our strongest results.
Further studies should aim to replicate our findings. By using
individual-year data, researchers can explore whether the variables we
identified as significant factors in child support performance remain
stable over time. Combining the data for additional years would increase
the sample size further. At some point, it may be possible to model
adjustments for the paternity establishment measures.

**Study of State Demographic, Economic, and Programmatic Variables and
Their Impact on the Performance-Based Child Support Incentive System**

Final Report

Appendices

Prepared for:\
Department of Health and Human Services\
Office of Child Support Enforcement

Prepared by:\
John Tapogna, ECONorthwest

Karen Gardiner, The Lewin Group

Burt Barnow, Johns Hopkins University

Michael Fishman, The Lewin Group

Plamen Nikolov, The Lewin Group

July 2003

**Appendix A**

**Expert Contributors**

**Exhibit A.1**

**List of Expert Contributors**

+-----------------------------------------------------------------------+
| **Academic/Policy Experts**                                           |
+-----------------------------------------------------------------------+
| Michelle Ahern, Office of Management and Budget                       |
|                                                                       |
| Libbie Buchele, Department of Health and Human Services               |
|                                                                       |
| Sheila Dacey, Congressional Budget Office                             |
|                                                                       |
| Steve Garasky, Iowa State University                                  |
|                                                                       |
| Meg Haynes, American Bar Association                                  |
|                                                                       |
| Maury Huguley, Department of Health and Human Services                |
|                                                                       |
| Karl Koerper, Administration for Children and Families, DHHS          |
|                                                                       |
| Bob Lerman, Urban Institute                                           |
|                                                                       |
| Ken Maniha, Administration for Children and Families, DHHS            |
|                                                                       |
| Dan Meyer, University of Wisconsin                                    |
|                                                                       |
| Linda Mellgren, Office of the Assistant Secretary for Planning and    |
| Evaluation, DHHS                                                      |
|                                                                       |
| Maureen Pirog, University of Indiana                                  |
|                                                                       |
| Howard Rolston, Administration for Children and Families, DHHS        |
+-----------------------------------------------------------------------+
| **State-level IV-D Staff**                                            |
+-----------------------------------------------------------------------+
| Jeffery Cohen, Vermont                                                |
|                                                                       |
| Pam Compton, Illinois                                                 |
|                                                                       |
| Alice Embree, Texas                                                   |
|                                                                       |
| Stephen Hussey, Maine                                                 |
|                                                                       |
| Laura Kadwell, Minnesota                                              |
|                                                                       |
| Daniel Richard, Pennsylvania                                          |
|                                                                       |
| Barbara Saunders, Ohio                                                |
|                                                                       |
| Marilyn Smith, Massachusetts                                          |
|                                                                       |
| Nathaniel Young, Virginia                                             |
+-----------------------------------------------------------------------+
| **Advocacy and Policy Groups**                                        |
+-----------------------------------------------------------------------+
| Justin Latus, American Public Human Services Association              |
|                                                                       |
| Wendell Primus, Center for Budget and Policy Priorities               |
|                                                                       |
| Vicki Turetsky, Center for Law and Social Policy                      |
|                                                                       |
| Bob Williams, Policy Studies, Inc.                                    |
+-----------------------------------------------------------------------+

**Appendix B**

**Independent Variable Selection**

**Exhibit B.1**

Candidate Roster of Explanatory Variables

+--------------------------------+-------+------+------+------+------+
| **Variable**                   | **Hy  |      |      |      |      |
|                                | pothe |      |      |      |      |
|                                | sized |      |      |      |      |
|                                | E     |      |      |      |      |
|                                | ffect |      |      |      |      |
|                                | on    |      |      |      |      |
|                                | CSE   |      |      |      |      |
|                                | Per   |      |      |      |      |
|                                | forma |      |      |      |      |
|                                | nce** |      |      |      |      |
+================================+=======+======+======+======+======+
|                                | > **P | > *  | > ** | > Co | > ** |
|                                | atern | *Ord | Coll | llec | Cost |
|                                | ity** | er** | ecti | tion | >    |
|                                | >     | >    | on** | >    | Effe |
|                                | >     | > ** | >    | Past | ctiv |
|                                |  **Es | Esta | >    | >    | e.** |
|                                | tabli | blis |  **C | >    |      |
|                                | sh.** | h.** | urre |  **S |      |
|                                |       |      | nt** | uppo |      |
|                                |       |      | >    | rt** |      |
|                                |       |      | >    |      |      |
|                                |       |      |  **S |      |      |
|                                |       |      | uppo |      |      |
|                                |       |      | rt** |      |      |
+--------------------------------+-------+------+------+------+------+
| **INCOME AND POVERTY           |       |      |      |      |      |
| MEASURES**                     |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Poverty and Welfare Measures   |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% of population in poverty    | \-    | \-   | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+
| \% children in poverty         | \-    | \-   | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+
| \% children in state receiving | ?     | ?    | \-   | \-   | \-   |
| TANF                           |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Infant mortality rate per      | ?     | ?    | ?    | ?    | ?    |
| 1,000 births                   |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Earnings and Income Measures   |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Per capita income              | \+    | \+   | \+   | \+   | \+   |
+--------------------------------+-------+------+------+------+------+
| \% AGI in lowest income        | \-    | \-   | \-   | \-   | \-   |
| quintile                       |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Median earnings---males        | \+    | \+   | \+   | \+   | \+   |
+--------------------------------+-------+------+------+------+------+
| Median earnings---females      | \-    | \-   | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+
| Employment Status              |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Unemployment rate for          | \-    | \-   | \-   | \-   | \-   |
| population                     |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% males 20 to 45 not working  | \-    | \-   | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+
| \% males 20 to 64 not working  | \-    | \-   | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+
| \% workers in high turnover    | \-    | \-   | \-   | \-   | \-   |
| industries                     |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% workers self-employed       | \-    | \-   | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+
| Level of unionization          | \+    | \+   | \+   | \+   | \+   |
+--------------------------------+-------+------+------+------+------+
| Job growth                     | \+    | \+   | \+   | \+   | \+   |
+--------------------------------+-------+------+------+------+------+
| **POPULATION CHARACTERISTICS** |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Fertility measures             |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+

\- Hypothesized negative association

\+ Hypothesized positive association

? No hypothesized association

+--------------------------------+-------+------+------+------+------+
| **Variable**                   | **Hy  |      |      |      |      |
|                                | pothe |      |      |      |      |
|                                | sized |      |      |      |      |
|                                | E     |      |      |      |      |
|                                | ffect |      |      |      |      |
|                                | on    |      |      |      |      |
|                                | CSE   |      |      |      |      |
|                                | Per   |      |      |      |      |
|                                | forma |      |      |      |      |
|                                | nce** |      |      |      |      |
+================================+=======+======+======+======+======+
|                                | > **P | > *  | > ** | > Co | > ** |
|                                | atern | *Ord | Coll | llec | Cost |
|                                | ity** | er** | ecti | tion | >    |
|                                | >     | >    | on** | >    | Effe |
|                                | >     | > ** | >    | Past | ctiv |
|                                |  **Es | Esta | >    | >    | e.** |
|                                | tabli | blis |  **C | >    |      |
|                                | sh.** | h.** | urre |  **S |      |
|                                |       |      | nt** | uppo |      |
|                                |       |      | >    | rt** |      |
|                                |       |      | >    |      |      |
|                                |       |      |  **S |      |      |
|                                |       |      | uppo |      |      |
|                                |       |      | rt** |      |      |
+--------------------------------+-------+------+------+------+------+
| Non-marital birth rates per    | \-    | \-   | \-   | \-   | \-   |
| women 15 to 44                 |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% births to unmarried         | \-    | \-   | \-   | \-   | \-   |
| women---stock and flow         |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% births to teens---stock and | \-    | \-   | \-   | \-   | \-   |
| flow                           |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Race/Ethnicity                 |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% population that is Native   | ?     | ?    | ?    | ?    | ?    |
| American                       |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% population African-American | ?     | ?    | ?    | ?    | ?    |
+--------------------------------+-------+------+------+------+------+
| \% population that is Hispanic | ?     | ?    | ?    | ?    | ?    |
+--------------------------------+-------+------+------+------+------+
| \% of TANF/AFDC/FS Caseload    | ?     | ?    | ?    | ?    | ?    |
| African-American               |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% of TANF/AFDC/FS Caseload    | ?     | ?    | ?    | ?    | ?    |
| that is Hispanic               |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Other Demographic              |       |      |      |      |      |
| Characteristics                |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% population under age 19     | ?     | ?    | ?    | ?    | ?    |
+--------------------------------+-------+------+------+------+------+
| Population stability (same     | \+    | \+   | \+   | \+   | \+   |
| house after 1 year)            |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% TANF/AFDC/FS households     | \-    | \-   | \-   | \-   | \-   |
| under age 30                   |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Average household size         | \-    | \-   | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+
| Average size of households     | \-    | \-   | \-   | \-   | \-   |
| receiving TANF/AFDC/FS         |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% households headed by female | \-    | \-   | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+
| \% population living in urban  | \-    | \-   | \-   | \-   | \-   |
| areas                          |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% pop.\> 25 with less than HS | \-    | \-   | \-   | \-   | \-   |
| degree                         |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Incarceration rate             | \-    | \-   | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+

\- Hypothesized negative association

\+ Hypothesized positive association

? No hypothesized association

+--------------------------------+-------+------+------+------+------+
| **Variable**                   | **Hy  |      |      |      |      |
|                                | pothe |      |      |      |      |
|                                | sized |      |      |      |      |
|                                | E     |      |      |      |      |
|                                | ffect |      |      |      |      |
|                                | on    |      |      |      |      |
|                                | CSE   |      |      |      |      |
|                                | Per   |      |      |      |      |
|                                | forma |      |      |      |      |
|                                | nce** |      |      |      |      |
+================================+=======+======+======+======+======+
|                                | > **P | > *  | > ** | > Co | > ** |
|                                | atern | *Ord | Coll | llec | Cost |
|                                | ity** | er** | ecti | tion | >    |
|                                | >     | >    | on** | >    | Effe |
|                                | >     | > ** | >    | Past | ctiv |
|                                |  **Es | Esta | >    | >    | e.** |
|                                | tabli | blis |  **C | >    |      |
|                                | sh.** | h.** | urre |  **S |      |
|                                |       |      | nt** | uppo |      |
|                                |       |      | >    | rt** |      |
|                                |       |      | >    |      |      |
|                                |       |      |  **S |      |      |
|                                |       |      | uppo |      |      |
|                                |       |      | rt** |      |      |
+--------------------------------+-------+------+------+------+------+
| **PROGRAMMATIC VARIABLES**     |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Staffing and Expenditures      |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Total expenditures per case    | \+    | \+   | \+   | \+   | \-   |
+--------------------------------+-------+------+------+------+------+
| Expenditure per case, past     | \+    | \+   | \+   | \+   | \-   |
| five years                     |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Ratio IV-D expenditures to     | \+    | \+   | \+   | \+   | \-   |
| state personal income          |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Cases per FTE                  | \+    | \+   | \+   | \+   | \-   |
+--------------------------------+-------+------+------+------+------+
| \# IV-D directors in past five | ?     | ?    | ?    | ?    | ?    |
| years                          |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% staff that are public       | ?     | ?    | ?    | ?    | ?    |
| employees vs. contractors      |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| System automation              | ?     | ?    | ?    | ?    | ?    |
+--------------------------------+-------+------+------+------+------+
| Structure and Organization     |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Judicial vs. administrative    | ?     | ?    | ?    | ?    | ?    |
| process                        |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Locally elected officials can  | ?     | ?    | ?    | ?    | ?    |
| influence policy               |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| State vs. county-based system  | ?     | ?    | ?    | ?    | ?    |
+--------------------------------+-------+------+------+------+------+
| \% funding administered at     | ?     | ?    | ?    | ?    | ?    |
| county level                   |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Location of CSE (e.g.,         | ?     | ?    | ?    | ?    | ?    |
| Department of Social Services) |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| CSE Caseload and Enforcement   |       |      |      |      |      |
| Characteristics                |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Proportion of IV-D caseload    | \-    | \-   | \-   | \-   | \-   |
| that is interstate             |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Proportion of IV-D caseload on | ?     | ?    | \-   | \-   | \-   |
| TANF                           |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% cases ever TANF/AFDC        | ?     | ?    | \-   | \-   | \-   |
+--------------------------------+-------+------+------+------+------+
| Orders must be paid through    | ?     | ?    | \+   | \+   | \+   |
| the IV-D system                |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| TANF break-even levels by      | \+    | \+   | \+   | \+   | \+   |
| state                          |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| TANF grant as a percent of the | \+    | \+   | \+   | \+   | \+   |
| poverty line                   |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+

\- Hypothesized negative association

\+ Hypothesized positive association

? No hypothesized association

+--------------------------------+-------+------+------+------+------+
| **Variable**                   | **Hy  |      |      |      |      |
|                                | pothe |      |      |      |      |
|                                | sized |      |      |      |      |
|                                | E     |      |      |      |      |
|                                | ffect |      |      |      |      |
|                                | on    |      |      |      |      |
|                                | CSE   |      |      |      |      |
|                                | Per   |      |      |      |      |
|                                | forma |      |      |      |      |
|                                | nce** |      |      |      |      |
+================================+=======+======+======+======+======+
|                                | > **P | > *  | > ** | > Co | > ** |
|                                | atern | *Ord | Coll | llec | Cost |
|                                | ity** | er** | ecti | tion | >    |
|                                | >     | >    | on** | >    | Effe |
|                                | >     | > ** | >    | Past | ctiv |
|                                |  **Es | Esta | >    | >    | e.** |
|                                | tabli | blis |  **C | >    |      |
|                                | sh.** | h.** | urre |  **S |      |
|                                |       |      | nt** | uppo |      |
|                                |       |      | >    | rt** |      |
|                                |       |      | >    |      |      |
|                                |       |      |  **S |      |      |
|                                |       |      | uppo |      |      |
|                                |       |      | rt** |      |      |
+--------------------------------+-------+------+------+------+------+
| Level of order/guidelines      | ?     | ?    | \-   | \-   | \+   |
| structure                      |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% collections through wage    | ?     | ?    | \+   | \+   | \+   |
| withholding                    |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| \% collections through tax     | ?     | ?    | \+   | \+   | \+   |
| intercepts                     |       |      |      |      |      |
+--------------------------------+-------+------+------+------+------+
| Use of new hire directory      | \+    | \+   | \+   | \+   | \+   |
+--------------------------------+-------+------+------+------+------+
| \% licenses suspended          | ?     | ?    | ?    | ?    | ?    |
+--------------------------------+-------+------+------+------+------+

\- Hypothesized negative association

\+ Hypothesized positive association

? No hypothesized association

**Exhibit B.2**

**Variables Not Included in Study**

  -------------------------------------- --------------------------------
  **Variable**                           **Reason for non-inclusion**

  State tax collections per capita       No hypothesized relationship
                                         with CSE

  State tax collections per \$1,000      No hypothesized relationship
  income                                 with CSE

  Unemployment rate of non-custodial     Data unavailable at state level
  parents                                

  Percent men and women earning less     Data unavailable at state level
  than "X"                               

  \% IV-D families in poverty            Data unavailable

  State industries/occupations           No hypothesized relationship
                                         with CSE

  Proportion population non-English      No hypothesized association with
  speaking                               CSE

  State proximity to Mexican border      No hypothesized association with
                                         CSE

  Percent population immigrants          No hypothesized association with
                                         CSE

  Proportion single parents divorced     Data unavailable at state level
  versus never married                   

  Percent non-custodial parents who are  Data unavailable at state level
  re-married                             

  Percent NCP with joint custody or      Data unavailable at state level
  visitation rights                      

  Non-marital custodial parents as % of  Data unavailable
  the IV-D caseload                      

  Education levels of non-custodial      Data unavailable at state level
  parents                                

  High school dropout rate               Data unreliable at state level

  Religiosity                            Data unavailable

  Computer vendor by state               No hypothesized association with
                                         CSE

  Tenure of IV-D director                Data unavailable

  Staff placement (% in customer         Data unavailable at state level
  service, admin, etc.)                  
  -------------------------------------- --------------------------------

**Appendix C**

**State-level Data**

**State-level Data**

  --------------- ---------- ---------- ---------- ---------- ------------ ---------- ---------- ----------
  ** **           **3-Year              **Per                 **Men Age               **Job      
                  State                 Capita                20-64 Not               Growth**   
                  Poverty               Personal              Employed**                         
                  Rate**                Income**                                                 

  ** **           **1999**   **2000**   **1999**   **2000**   **1999**     **2000**   **1999**   **2000**

  ALABAMA         14.70      14.67      22,123     23,460     18.94        19.39      2.1        0

  ALASKA          8.80       8.40       27,904     29,597     16.95        18.78      2.0        1.3

  ARIZONA         18.10      13.53      24,133     24,991     14.68        16.15      3.8        3.7

  ARKANSAS        17.20      15.73      21,260     21,945     19.52        19.07      2.0        2.1

  CALIFORNIA      16.30      14.00      28,280     32,225     16.69        16.07      3.2        3.1

  COLORADO        9.30       8.53       29,860     32,441     11.90        12.84      2.4        2.3

  CONNECTICUT     9.90       7.73       37,452     40,870     14.34        12.59      1.2        1.0

  DELAWARE        9.50       9.93       29,571     31,074     14.90        14.88      2.0        1.6

  DIST. OF COL.   22.70      17.37      37,714     38,374     20.13        20.53      1.7        1.0

  FLORIDA         13.90      12.03      26,930     27,836     16.23        16.17      4.3        3.7

  GEORGIA         14.30      12.53      26,134     27,790     14.64        15.13      4.5        1.2

  HAWAII          12.30      10.57      26,725     27,819     18.47        17.68      1.5        2.2

  IDAHO           13.20      13.27      21,923     23,640     14.05        14.01      2.6        3.5

  ILLINOIS        11.10      10.50      29,974     31,842     14.32        15.08      1.0        0.7

  INDIANA         8.60       8.27       25,182     26,838     13.28        14.54      2.2        0.1

  IOWA            9.40       7.93       24,844     26,376     11.71        12.18      3.6        1.9

  KANSAS          10.10      10.47      25,687     27,408     12.31        15.04      2.5        1.9

  KENTUCKY        15.50      12.50      22,353     24,057     20.17        20.59      2.3        1.6

  LOUISIANA       18.60      18.53      22,352     23,041     19.25        20.31      1.8        0.9

  MAINE           10.60      9.80       23,529     25,399     17.43        16.27      3.3        1.8

  MARYLAND        8.60       7.37       30,850     33,621     15.18        13.81      3.1        2.1

  MASSACHUSETTS   10.30      10.17      33,394     37,710     14.97        15.09      0.7        1.7

  MICHIGAN        10.80      10.23      26,807     29,071     15.01        14.73      0.7        0.4

  MINNESOTA       9.90       7.83       29,503     31,913     11.48        12.63      2.6        1.5

  MISSISSIPPI     18.30      15.53      20,013     20,856     20.60        19.64      0.9        -0.8

  MISSOURI        10.40      9.80       25,403     27,186     14.67        13.52      0.4        1.1

  MONTANA         16.40      15.97      21,324     22,541     16.48        15.97      2.9        1.7

  NEBRASKA        10.80      10.73      25,861     27,658     10.33        11.21      0.2        -0.9

  NEVADA          9.90       10.13      29,806     29,551     15.14        14.36      4.3        4.9

  NEW HAMPSHIRE   8.40       7.57       29,679     33,042     10.84        10.80      5.5        0.3

  NEW JERSEY      9.00       8.13       34,310     37,112     15.48        14.03      1.7        1.3

  NEW MEXICO      22.40      19.30      21,178     21,883     20.13        18.52      1.9        2.0

  NEW YORK        16.60      14.73      32,236     34,502     19.86        19.68      2.9        1.8

  NORTH CAROLINA  12.50      13.20      25,454     26,842     14.52        15.28      1.8        0.6

  NORTH DAKOTA    13.20      12.73      22,767     24,780     11.11        12.35      0.8        -0.1

  OHIO            11.60      11.07      26,164     27,914     16.16        16.19      1.1        0.5

  OKLAHOMA        14.80      14.07      22,199     23,582     16.65        17.01      3.0        1.3

  OREGON          12.80      12.93      25,958     27,649     16.75        16.34      0.8        0.6

  PENNSYLVANIA    11.30      9.87       27,358     29,533     17.02        16.78      0.8        0.3

  RHODE ISLAND    11.80      10.20      28,012     29,158     15.69        14.61      2.3        1.5

  SOUTH CAROLINA  13.30      12.00      22,544     23,952     17.57        16.98      1.9        1.6

  SOUTH DAKOTA    13.00      9.37       23,797     25,993     11.39        11.76      1.8        0.7

  TENNESSEE       14.50      13.33      24,576     25,878     17.25        18.22      1.4        0.8

  TEXAS           16.10      14.93      25,803     27,722     13.89        13.95      3.6        2.8

  UTAH            8.50       8.10       22,294     23,364     10.45        10.62      2.8        2.4

  VERMONT         10.60      10.30      24,803     26,904     12.36        12.66      3.3        1.9

  VIRGINIA        11.30      8.13       28,343     31,065     12.94        12.82      1.7        2.0

  WASHINGTON      10.00      9.50       28,632     31,129     15.65        17.10      2.0        2.0

  WEST VIRGINIA   17.60      15.83      20,246     21,767     25.30        26.23      0.0        0.8

  WISCONSIN       8.60       9.00       26,245     28,066     12.69        13.53      1.0        1.4

  WYOMING         12.00      11.07      24,927     27,436     14.39        12.06      2.1        2.4
  --------------- ---------- ---------- ---------- ---------- ------------ ---------- ---------- ----------

**State-level Data (continued)**

  --------------- ----------- ---------- ---------- ---------- ---------- ---------- ------------------ ----------
  ** **           **Percent              **TANF                **Cases               **Judicial /       
                  Urban**                Heads                 per FTE**             Administrative**   
                                         Under Age                                                      
                                         30**                                                           

  ** **           **1999**    **2000**   **1999**   **2000**   **1999**   **2000**   **1999**           **2000**

  ALABAMA         45.54       45.54      63.8       63.8       426.11     437.93     16                 16

  ALASKA          40.31       40.31      41.3       41.3       197.49     183.63     5                  5

  ARIZONA         72.48       72.48      49.7       49.7       261.44     236.32     16                 16

  ARKANSAS        25.17       25.17      60.2       60.2       211.41     236.19     9                  9

  CALIFORNIA      85.57       85.57      34.5       34.5       238.00     227.04     14                 14

  COLORADO        72.18       72.18      51.0       51.0       241.55     202.03     9                  9

  CONNECTICUT     74.70       74.70      53.2       53.2       366.86     341.91     13                 13

  DELAWARE        68.98       68.98      54.9       54.9       288.26     283.02     13                 13

  DIST. OF COL.   100.00      100.00     50.7       50.7       556.61     609.00     16                 16

  FLORIDA         78.69       78.69      48.5       48.5       218.94     231.83     14                 14

  GEORGIA         50.32       50.32      54.6       54.6       485.27     503.61     16                 16

  HAWAII          67.41       67.41      42.3       42.3       364.86     405.59     12                 12

  IDAHO           27.66       27.66      55.2       55.2       348.69     381.26     16                 16

  ILLINOIS        74.18       74.18      50.0       50.0       544.24     546.37     15                 15

  INDIANA         48.54       48.54      55.6       55.6       576.86     551.25     16                 16

  IOWA            33.94       33.94      57.3       57.3       281.49     281.42     11                 11

  KANSAS          41.11       41.11      60.3       60.3       269.24     285.67     14                 14

  KENTUCKY        34.65       34.65      53.4       53.4       288.47     284.01     16                 16

  LOUISIANA       52.80       52.80      57.8       57.8       372.52     334.82     15                 15

  MAINE           21.71       21.71      46.6       46.6       212.27     217.36     5                  5

  MARYLAND        74.90       74.90      47.3       47.3       287.89     312.33     10                 10

  MASSACHUSETTS   78.61       78.61      52.3       52.3       240.22     303.18     16                 16

  MICHIGAN        62.52       62.52      35.5       35.5       363.32     383.05     14                 14

  MINNESOTA       54.18       54.18      51.9       51.9       169.82     158.27     14                 14

  MISSISSIPPI     24.01       24.01      60.1       60.1       417.08     492.31     14                 14

  MISSOURI        54.38       54.38      60.8       60.8       284.49     284.48     6                  6

  MONTANA         26.12       26.12      50.3       50.3       186.00     206.41     4                  4

  NEBRASKA        43.58       43.58      55.7       55.7       254.30     252.37     15                 15

  NEVADA          75.79       75.79      54.4       54.4       278.27     316.19     14                 14

  NEW HAMPSHIRE   30.63       30.63      52.5       52.5       228.67     227.57     12                 12

  NEW JERSEY      85.77       85.77      48.7       48.7       173.94     168.43     12                 12

  NEW MEXICO      42.86       42.86      49.9       49.9       346.34     340.81     14                 14

  NEW YORK        78.47       78.47      42.5       42.5       338.13     326.43     14                 14

  NORTH CAROLINA  37.88       37.88      59.5       59.5       303.94     314.32     11                 11

  NORTH DAKOTA    31.70       31.70      52.9       52.9       264.84     237.95     16                 16

  OHIO            61.36       61.36      58.4       58.4       185.49     225.90     6                  6

  OKLAHOMA        43.06       43.06      57.3       57.3       251.49     227.60     11                 11

  OREGON          49.96       49.96      45.8       45.8       306.17     324.05     6                  6

  PENNSYLVANIA    60.68       60.68      50.0       50.0       376.26     236.82     12                 12

  RHODE ISLAND    82.17       82.17      50.0       50.0       467.18     428.33     12                 12

  SOUTH CAROLINA  40.88       40.88      52.0       52.0       797.72     645.76     10                 10

  SOUTH DAKOTA    23.56       23.56      50.5       50.5       323.89     314.74     9                  9

  TENNESSEE       45.46       45.46      57.2       57.2       631.15     573.39     16                 16

  TEXAS           66.96       66.96      52.4       52.4       497.45     415.46     14                 14

  UTAH            76.61       76.61      52.3       52.3       152.21     145.36     8                  8

  VERMONT         15.44       15.44      49.4       49.4       193.75     201.95     12                 12

  VIRGINIA        61.89       61.89      54.9       54.9       374.55     368.97     5                  5

  WASHINGTON      66.04       66.04      43.4       43.4       183.24     186.29     9                  9

  WEST VIRGINIA   21.67       21.67      49.2       49.2       248.49     248.87     12                 12

  WISCONSIN       50.39       50.39      59.2       59.2       328.72     339.39     15                 15

  WYOMING         25.13       25.13      53.4       53.4       170.73     158.49     14                 14
  --------------- ----------- ---------- ---------- ---------- ---------- ---------- ------------------ ----------

**State-level Data (continued)**

  --------------- ------------- ---------- ------------------ ---------- -------------- ---------- ---------------- ----------
  ** **           **Currently              **Universality**              **Population              **Expenditures   
                  on TANF**                                              Stability**               per Case**       

  ** **           **1999**      **2000**   **1999**           **2000**   **1999**       **2000**   **1999**         **2000**

  ALABAMA         11.61         11.03      37.42              37.97      84.9           84.9       167.94           187.04

  ALASKA          20.54         17.11      35.81              29.52      77.9           77.9       371.33           457.47

  ARIZONA         23.50         24.41      19.37              19.90      78.7           78.7       221.58           245.96

  ARKANSAS        14.02         15.35      49.97              50.81      82.2           82.2       296.45           269.40

  CALIFORNIA      34.92         28.91      17.42              17.32      83.4           83.4       273.44           333.18

  COLORADO        14.32         11.77      20.31              22.58      78.9           78.9       302.25           433.51

  CONNECTICUT     20.58         19.68      20.34              22.29      86.3           86.3       249.57           297.69

  DELAWARE        17.37         16.62      48.05              49.39      84.8           84.8       288.75           332.46

  DIST. OF COL.   30.80         30.55      30.86              31.98      82.4           82.4       131.58           125.15

  FLORIDA         8.16          12.95      25.43              28.09      80.5           80.5       245.52           275.82

  GEORGIA         20.48         18.54      34.44              34.39      82.2           82.2       137.85           158.65

  HAWAII          33.75         31.68      32.58              31.04      83.2           83.2       278.20           179.36

  IDAHO           5.22          5.09       48.44              51.67      82.5           82.5       206.92           272.08

  ILLINOIS        27.87         17.04      44.76              38.34      84.4           84.4       134.13           148.46

  INDIANA         8.20          10.40      68.22              66.22      83.8           83.8       97.67            106.30

  IOWA            19.11         16.81      22.11              23.74      84.3           84.3       249.22           330.22

  KANSAS          14.48         14.75      45.64              45.83      83             83         315.65           337.65

  KENTUCKY        20.26         19.91      58.26              58.53      84.7           84.7       188.04           196.30

  LOUISIANA       18.20         14.95      29.12              29.88      83.9           83.9       148.61           159.46

  MAINE           23.63         20.84      22.52              22.17      87.2           87.2       299.56           323.37

  MARYLAND        15.13         14.66      54.44              53.85      85             85         247.14           317.33

  MASSACHUSETTS   26.41         23.56      20.27              20.79      86.9           86.9       306.33           404.80

  MICHIGAN        14.48         12.64      39.68              40.37      85.3           85.3       172.10           243.72

  MINNESOTA       28.01         25.70      30.22              18.61      86.6           86.6       485.43           525.76

  MISSISSIPPI     11.25         10.52      50.63              52.46      84.1           84.1       123.15           117.93

  MISSOURI        21.51         21.40      44.72              43.10      82.1           82.1       264.31           286.62

  MONTANA         14.36         15.73      26.00              23.72      84             84         261.55           353.59

  NEBRASKA        15.95         12.77      44.82              42.73      82.2           82.2       306.83           396.51

  NEVADA          29.84         10.19      48.79              63.78      76.3           76.3       241.33           392.24

  NEW HAMPSHIRE   18.52         17.53      37.93              37.94      85.1           85.1       341.77           399.60

  NEW JERSEY      23.87         21.07      32.11              33.46      87.9           87.9       370.41           431.64

  NEW MEXICO      39.77         34.61      24.30              22.25      82.4           82.4       310.40           314.37

  NEW YORK        33.90         29.90      20.45              22.02      88.9           88.9       200.61           242.98

  NORTH CAROLINA  15.56         13.75      32.41              31.89      83.3           83.3       235.45           221.30

  NORTH DAKOTA    14.70         13.61      40.00              41.30      84             84         236.67           265.99

  OHIO            22.89         13.18      67.41              59.42      85.6           85.6       301.52           393.75

  OKLAHOMA        19.21         17.19      28.22              30.25      81.7           81.7       200.30           297.66

  OREGON          16.40         15.72      35.54              38.10      79.1           79.1       184.65           205.87

  PENNSYLVANIA    17.04         17.26      42.49              41.80      88.4           88.4       254.60           319.38

  RHODE ISLAND    44.83         36.44      15.03              15.05      87             87         163.83           187.15

  SOUTH CAROLINA  16.07         17.24      28.64              29.68      84.3           84.3       134.94           174.36

  SOUTH DAKOTA    12.42         12.05      28.39              28.80      84.1           84.1       198.45           232.54

  TENNESSEE       25.18         28.23      33.63              30.59      84.3           84.3       114.43           127.99

  TEXAS           17.44         17.90      34.80              40.07      81.3           81.3       155.87           195.99

  UTAH            19.84         19.21      26.33              25.34      81.8           81.8       398.62           459.81

  VERMONT         39.87         37.79      15.65              17.87      85.7           85.7       378.75           415.20

  VIRGINIA        15.40         13.79      45.79              45.87      83.3           83.3       181.24           202.72

  WASHINGTON      18.36         16.50      26.45              27.34      79.9           79.9       393.51           402.38

  WEST VIRGINIA   15.08         13.43      64.94              37.41      87             87         209.99           243.15

  WISCONSIN       8.15          7.07       24.12              29.37      84.4           84.4       234.70           255.59

  WYOMING         8.94          8.19       63.10              39.63      80.5           80.5       190.99           240.22
  --------------- ------------- ---------- ------------------ ---------- -------------- ---------- ---------------- ----------

**Appendix D**

**Child Support Order Establishment Taxonomy**

The taxonomy provides a broad, macro view of the child support order
establishment processes. We did not collect detailed information on
order establishment, but instead construct a method to categorize
states. Some factors we explored in classifying state processes were:

-   *Ministerial functions*: To what extent does the IV-D office
    establish support orders without a hearing?

-   *Forum*: Where is the order established? Is it the judicial or
    executive branch? Does the forum differ if the order is contested?

-   *Presiding officer*: If the order is contested, is the presiding
    officer is a judge, a non-judge, or an executive agency hearings
    officer?

-   *Attorney involvement*: does an attorney represent the IV-D office
    in the order establishment process?

We used responses to the 1997 Center for Law and Social Policy (CLASP)
survey of state IV-D directors to lay the foundation for our state
classification exercise. The CLASP survey asked IV-D directors or their
designated staff about the key "players" in their child support
programs. Two questions specifically asked about the agencies that are
involved in establishing support obligations in uncontested and
contested cases. For purposes of this study, an uncontested case is one
in which the parties generally follow the standard establishment process
and do not appeal the state's decision to a review agency or governing
body. In a contested case, one or both of the parties disagrees with a
preliminary outcome in the establishment process and requests a review
of the guidelines finding.

Many states checked multiple players, indicating that many employ a mix
of judicial and administrative elements. To clarify state processes, we
incorporated information from other sources. Specifically, we consulted
the following studies:

-   The National Child Support Enforcement Association (NCSEA)
    Interstate Roster and Referral Guide,[^29] which provided profiles
    on each state's regulations and procedures on support order
    establishment;

-   West Virginia Bureau for Child Support Enforcement study of state
    administrative processes,[^30] which included profiles of Colorado,
    Maine, Missouri, Montana, Oregon, South Carolina, and Virginia;

-   Canada Department of Justice study[^31] of expedited child support,
    which included profiles of Alaska, California, Colorado,
    Connecticut, Maine, Montana, Oregon, Texas, and Washington; and,

-   Policy Studies, Inc.,[^32] report on Minnesota's administrative
    process.

After completing a draft of the taxonomy, we shared it with the state
IV-D directors to solicit feedback. A response form asked whether we
classified their states correctly and if there were any elements missing
from the taxonomy.

Using these various sources, we developed a continuum of state policies,
ranging from highly judicial to highly administrative. We assigned state
processes into one of seven categories. Each is described below.

*Judicial forum, judge presides*. This is the most judicial process. The
tribunal is in the court. A judge hears cases and sets the orders.

*Judicial forum, non-judge presides*. States in this category have
tribunals in the judiciary---either the court or an administrative
office of the court. The presiding officer, however, is not a judge, but
an appointed magistrate, master of the court, or another type of hearing
officer, such as an administrative law judge. This judicial officer
holds hearings and sets orders.

*Administrative forum, office of administrative hearings or attorney
general*. States in this category have tribunals in the executive branch
of government, although not in the same agency that houses the child
support division. The presiding officer could be an administrative law
judge or another type of hearings officer.

*Administrative forum, IV-D umbrella agency*. In this category, the
tribunal is located within an agency that houses the IV-D unit (e.g.,
Department of Social Services, Department of Revenue), but not within
the IV-D agency itself. The presiding officer is generally not an
administrative law judge, but some other type of hearings officer.

*Administrative forum, IV-D agency*. Some states use IV-D staff to
convene hearings and set orders. Like the previous category, the
hearings officer is generally not a formal administrative law judge.
Often child support workers conduct hearings.

*Administrative forum, consent order*. Some states make significant use
of consent orders---the IV-D agency creates an order using guidelines
and the non-custodial parent signs the consent if he/she accepts. In
some states, the consent order is finalized by a IV-D administrative
hearing officer, while in others it goes to the court for sign-off, and
only becomes official when signed by a judge. We classify a state in
this category if the majority of orders are set by the consent process,
regardless of whether the order requires IV-D hearings officer or
judicial sign-off.

*Administrative forum, no hearing*. States using this most
administrative forum create orders administratively without any hearing.
IV-D staff set orders.

*Attorney involvement in process*. In addition to these seven
categories, we examined whether the IV-D office is represented in the
proceedings by an attorney (e.g., district attorney or attorney
general). Our theory is that states that use attorneys extensively have
more formal, judicial-like processes.

*Uncontested and contested orders*. While reviewing documents on order
establishment processes, it became clear that states often use different
forums depending upon whether the order is uncontested or contested. In
many instances, a state would have an administrative process for
uncontested cases and a more judicial process for contested ones. Thus,
each state was assigned two marks, indicating its categorization for
each type of order.

In scoring the taxonomy, we assigned a "7" to most highly judicial
forum, and a "1" to the most administrative one. The "scores" are simply
an accounting method. We do not mean to imply that a high score or a low
score is better. Rather we are seeking a way to group states that appear
to have similar processes together. In addition to scoring both types of
orders, we assign a point if an attorney represented the IV-D agency in
an uncontested order and a point if the agency is represented in a
contested one. For example:

-   A state that uses a judge to set both uncontested and contested
    orders received a score of 14. If attorneys represent the IV-D
    agency for both types of orders, the score was 16.

-   A state that set initial orders without a hearing, used IV-D staff
    to hear contested orders, and did not utilize attorneys received a
    score of 4.

-   A state that used a consent process for uncontested orders would get
    a score of 2. If judges in the courts heard contested orders, the
    score would be 7. If attorneys represented the state agency in
    contested cases only, the state received an extra point. This
    state's total score would be 10.

Although the majority of states were easily placed into one of the seven
general categories, a few presented problems. For instance, some states
had multiple levels of hearings (e.g., the first hearing occurs within
the IV-D agency and the subsequent hearing within the larger
department). In these instances, we based scoring on the entity that had
the ultimate decision (in the example above, it would be the larger
agency). There were also states that had different processes for
different counties. For instance, judges hear cases in some counties
while non-judges (e.g., magistrates) hear cases in others. For these
states, we classified the process according to the counties where the
bulk of the IV-D caseloads resided. Still other states had both
administrative and judicial processes operating side by side. We
classified the state according to which process was used to establish
the majority of orders.

As ***Exhibit D.1*** indicates, taxonomy scores range from 16 to 4. We
defined any state that has a score of 14 or above as "highly judicial."
States with these scores generally establish all orders in a judicial
forum and use IV-D attorneys for both uncontested and contested cases.
States that score under 10 are classified as "highly administrative."
These states generally set uncontested orders without a hearing, through
a consent process, or through hearings within the IV-D agency. Contested
cases are heard in an administrative forum. Attorney involvement is
uncommon. States in the 10 to 14 range are classified as
"quasi-judicial" because they employ a mix of administrative forums for
uncontested orders and judicial forums for contested ones. Some also use
attorneys for both types of processes.

  --------------- ---------- ----------- ---------------- --------- ---------- --------- --------- ------------ -------
                  Judicial               Administrative                                                          
                  Forum                  Forum                                                                  

                  Judge      Non-Judge   OAH or AG (5)    Non-IVD   IVD        Consent   IVD Sets  Attorney     Score
                  Presides   Presides                     Hearing   Conducts   Order (2) Order     Represents   
                  (7)        (6)                          Officer   In-House             without   IVD Agency   
                                                          (4)       Hearings             Hearing   (1,1)        
                                                                    (3)                  (1)                    

  Alabama         U, C                                                                             U, C         16

  Alaska                                                  C                              U                      5

  Arizona         U, C                                                                             U, C         16

  Arkansas        C                                                            U                                9

  California                 U, C                                                                  U, C         14

  Colorado                   C                                                 U                   C            9

  Connecticut                U, C                                                                  C            13

  Delaware                   U, C                                                                  C            13

  District of     U, C                                                                             U, C         16
  Columbia                                                                                                      

  Florida                    U, C                                                                  U, C         14

  Georgia         U, C                                                                             U, C         16

  Hawaii                                 U, C                                                      U, C         12

  Idaho           U, C                                                                             U, C         16

  Illinois        C          U                                                                     U, C         15

  Indiana         U, C                                                                             U, C         16

  Iowa            C                                                            U                   U, C         11

  Kansas                     C                                                 U                   U,C          10

  Kentucky        U, C                                                                             U, C         16

  Louisiana       C          U                                                                     U, C         15

  Maine                                                   C                              U                      5

  Maryland                   C                                                 U                   U, C         10

  Massachusetts   U, C                                                                             U, C         16

  Michigan                   U, C                                                                  U, C         14

  Minnesota                  U, C                                                                  U, C         14
  --------------- ---------- ----------- ---------------- --------- ---------- --------- --------- ------------ -------

  -------------- ---------- ----------- ---------------- --------- ---------- --------- --------- ------------ -------
                 Judicial               Administrative                                                          
                 Forum                  Forum                                                                  

                 Judge      Non-Judge   OAH or AG (5)    Non-IVD   IVD        Consent   IVD Sets  Attorney     Score
                 Presides   Presides                     Hearing   Conducts   Order (2) Order     Represents   
                 (7)        (6)                          Officer   In-House             without   IVD Agency   
                                                         (4)       Hearings             Hearing   (1,1)        
                                                                   (3)                  (1)                    

  Mississippi    U, C                                                                                          14

  Missouri                                               C                              U         C            6

  Montana                                                          C                    U                      4

  Nebraska       C          U                                                                     U, C         15

  Nevada                    U, C                                                                  U, C         14

  New Hampshire             U, C                                                                               12

  New Jersey                U, C                                                                               12

  New Mexico                U, C                                                                   U, C        14

  New York                  U, C                                                                  U, C         14

  North Carolina C                                                            U                   U, C         11

  North Dakota   U, C                                                                             U, C         16

  Ohio                                                             C          U                   C            6

  Oklahoma                              C                U                                        U, C         11

  Oregon                                C                                               U                      6

  Pennsylvania              U, C                                                                               12

  Rhode Island              U, C                                                                               12

  South Carolina C                                                 U                                           10

  South Dakota              C                                                 U                   C            9

  Tennessee      U, C                                                                             U, C         16

  Texas                     U, C                                                                  U, C         14

  Utah                                                   C         U                              C            8

  Vermont                   U, C                                                                               12

  Virginia                                               C                              U                      5

  Washington                            C                                     U                   U, C         9

  West Virginia             U, C                                                                               12

  Wisconsin      C          U                                                                     U, C         15

  Wyoming                   U, C                                                                  U, C         14
  -------------- ---------- ----------- ---------------- --------- ---------- --------- --------- ------------ -------

**Appendix E**

**Correlations**

**Exhibit E.1: Correlations between Dependent and Explanatory
Variables**

  -------------------- ----------- --------------- ----------------- --------------- -----------------
                       **Cases     **Current       **Cost            **Collections   **Paternity
                       with        Collections**   Effectiveness**   on Arrears**    Establishment**
                       Orders**                                                      

                                                                                     

  **State Poverty**    -0.50435    -0.29742        -0.29305          -0.26924        -0.14171

                       (\<.0001)   (0.0024)        (0.0028)          (0.0062)        (0.1554)

  **TANF Now**         -0.25586    -0.07507        -0.32672          -0.19466        0.18432

                       (0.0094)    (0.4534)        (0.0008)          (0.0499)        (0.0637)

  **Job Growth**       -0.0042     -0.16916        -0.20482          0.20787         -0.09883

                       (0.9666)    (0.0892)        (0.0389)          (0.036)         (0.323)

  **Percent Urban**    -0.35063    -0.05029        -0.20712          -0.20741        0.28601

                       (0.0003)    (0.6157)        (0.0367)          (0.0365)        (0.0036)

  **Men 20-64 Not      -0.40191    -0.2779         -0.16274          -0.20358        0.1017
  Employed**                                                                         

                       (\<.0001)   (0.0047)        (0.1022)          (0.0401)        (0.3091)

  **TANF Less Than     -0.14283    -0.13867        -0.09595          0.10409         -0.46185
  30**                                                                               

                       (0.1521)    (0.1645)        (0.3374)          (0.2978)        (\<.0001)

  **Expenditure per    0.5484      0.37255         -0.25925          0.30881         0.24493
  Case**                                                                             

                       (\<.0001)   (0.0001)        (0.0085)          (0.0016)        (0.0131)

  **Population         0.12685     0.32746         0.26668           -0.04745        0.2496
  Stability**                                                                        

                       (0.2039)    (0.0008)        (0.0067)          (0.6358)        (0.0114)

  **Per Capita         -0.0375     0.25477         -0.07669          -0.07334        0.23572
  Personal Income**                                                                  

                       (0.7082)    (0.0098)        (0.4436)          (0.4638)        (0.0171)

  **Cases per FTE**    -0.60248    -0.4135         0.12492           -0.27707        -0.19646

                       (\<.0001)   (\<.0001)       (0.2109)          (0.0048)        (0.0478)

  **Judicial/Admin     -0.50881    -0.15516        -0.08813          -0.09931        -0.18037
  Process**                                                                          

                       (\<.0001)   (0.1194)        (0.3784)          (0.3206)        (0.0697)

  **Universality       -0.12168    -0.08436        0.14797           -0.08436        -0.06076
  Constant**                                                                         

                       (0.2231)    (0.3992)        (0.1377)          (0.3992)        (0.5441)

  **PEPIVDST**         0.09875     -0.12287        0.11935           0.10291         -0.34216

                       (0.3234)    (0.2186)        (0.2322)          (0.3034)        (0.0004)

  **CWO_DUM**          0.13731     0.11302         -0.0718           -0.17367        -0.16932

                       (0.1688)    (0.2581)        (0.4733)          (0.0809)        (0.0889)

  **COL_DUM**          0.10705     -0.12193        -0.18365          -0.04268        -0.07244

                       (0.2842)    (0.2222)        (0.0647)          (0.6701)        (0.4693)

  **CE_DUM**           0.13976     0.01297         0.04512           -0.01489        -0.26982

                       (0.1612)    (0.897)         (0.6525)          (0.8819)        (0.0061)

  **ARR_DUM**          0.10611     -0.05089        -0.23332          -0.0791         -0.18262

                       (0.2885)    (0.6115)        (0.0183)          (0.4294)        (0.0662)

  **PEP_DUM**          0.20005     0.14068         -0.04539          0.08649         0.00859

                       (0.0438)    (0.1584)        (0.6506)          (0.3874)        (0.9317)
  -------------------- ----------- --------------- ----------------- --------------- -----------------

**Exhibit E.2: Correlations among Explanatory Variables Used in
Regressions**

  --------------- ----------- ---------- ---------- ----------- ------------- ------------ ---------- ---------------
                  **State     **TANF     **Job      **Percent   **ARR_DUM**   **Men 20-64  **TANF     **Expenditure
                  Poverty**   Now**      Growth**   Urban**                   Not          Less Than  Per Case**
                                                                              Employed**   30**       

                                                                                                      

  **State         1.0000      0.18722    0.02365    -0.1015     0.15162       0.65726      0.05228    -0.45272
  Poverty**                                                                                           

                              (0.0595)   (0.8134)   (0.31)      (0.1282)      (\<.0001)    (0.6018)   (\<.0001)

  **TANF Now**    0.18722     1.0000     0.14681    0.33087     0.08535       0.1562       -0.36158   0.0951

                  (0.0595)               (0.1409)   (0.0007)    (0.3937)      (0.1169)     (0.0002)   (0.3417)

  **Job Growth**  0.02365     0.14681    1.0000     0.15234     0.06967       -0.161       -0.14632   0.11618

                  (0.8134)    (0.1409)              (0.1264)    (0.4865)      (0.106)      (0.1423)   (0.2449)

  **Percent       -0.1015     0.33087    0.15234    1.0000      -0.04308      -0.0425      -0.32791   0.03575
  Urban**                                                                                             

                  (0.31)      (0.0007)   (0.1264)               (0.6672)      (0.6715)     (0.0008)   (0.7213)

  **ARR_DUM**     0.15162     0.08535    0.06967    -0.04308    1.0000        -0.03561     -0.0105    0.10713

                  (0.1282)    (0.3937)   (0.4865)   (0.6672)                  (0.7223)     (0.9166)   (0.2839)

  **Men 20-64 Not 0.65726     0.1562     -0.161     -0.0425     -0.03561      1.0000       -0.1016    -0.35288
  Employed**                                                                                          

                  (\<.0001)   (0.1169)   (0.106)    (0.6715)    (0.7223)                   (0.3097)   (0.0003)

  **TANF Less     0.05228     -0.36158   -0.14632   -0.32791    -0.0105       -0.10158     1.0000     -0.17893
  Than 30**                                                                                           

                  (0.6018)    (0.0002)   (0.1423)   (0.0008)    (0.9166)      (0.3097)                (0.072)

  **Expenditure   -0.45272    0.0951     0.11618    0.03575     0.10713       -0.35288     -0.17893   1.0000
  Per Case**                                                                                          

                  (\<.0001)   (0.3417)   (0.2449)   (0.7213)    (0.2839)      (0.0003)     (0.072)    

  **Population    -0.06997    0.22449    -0.32174   -0.03871    -0.03953      0.12158      -0.01787   -0.02282
  Stability**                                                                                         

                  (0.4847)    (0.0233)   (0.001)    (0.6993)    (0.6933)      (0.2235)     (0.8586)   (0.8199)

  **CE_DUM**      -0.13433    -0.11643   0.05069    0.00905     0.11396       -0.10194     0.04729    -0.03175

                  (0.1783)    (0.2439)   (0.6129)   (0.9281)    (0.2541)      (0.308)      (0.6369)   (0.7514)

  **Per Capita    -0.40109    0.24036    -0.02052   0.6836      0.05857       -0.2287      -0.30744   0.25708
  Personal                                                                                            
  Income**                                                                                            

                  (\<.0001)   (0.015)    (0.8378)   (\<.0001)   (0.5587)      (0.0208)     (0.0017)   (0.0091)

  **Cases per     0.25758     0.05043    -0.1728    0.12442     -0.15503      0.22327      0.15482    -0.75123
  FTE**                                                                                               

                  (0.009)     (0.6147)   (0.0824)   (0.2128)    (0.1198)      (0.0241)     (0.1202)   (\<.0001)

  **COL_DUM**     0.04142     0.06465    0.04634    -0.06081    0.74128       -0.11953     -0.05796   0.08557

                  (0.6793)    (0.5186)   (0.6438)   (0.5438)    (\<.0001)     (0.2315)     (0.5628)   (0.3925)
  --------------- ----------- ---------- ---------- ----------- ------------- ------------ ---------- ---------------

**Exhibit E.2: Correlations among Explanatory Variables Used in
Regressions (continued)**

  ------------------ ----------- ----------- ---------- ----------- ------------- ------------ ---------- ---------------
                     **State     **TANF      **Job      **Percent   **ARR_DUM**   **Men 20-64  **TANF     **Expenditure
                     Poverty**   Now**       Growth**   Urban**                   Not          Less Than  Per Case**
                                                                                  Employed**   30**       

                                                                                                          

  **Judicial/Admin   0.25622     0.06131     0.01227    0.16472     -0.18559      0.06808      0.13314    -0.3265
  Process**                                                                                               

                     (0.0093)    (0.5404)    (0.9026)   (0.0981)    (0.0618)      (0.4966)     (0.1822)   (0.0008)

  **Universality     -0.05506    -0.4963     -0.19426   -0.20859    -0.32515      0.1103       0.33974    -0.22966
  Constant**                                                                                              

                     (0.5826)    (\<.0001)   (0.0504)   (0.0354)    (0.0009)      (0.2698)     (0.0005)   (0.0202)

  **CWO_DUM**        0.09147     -0.09236    -0.11047   -0.09891    0.51607       0.01433      -0.00012   0.03992

                     (0.3606)    (0.3559)    (0.269)    (0.3226)    (\<.0001)     (0.8863)     (0.9991)   (0.6904)

  **Iv-D/State       0.04662     -0.08986    0.12433    -0.31874    -0.03457      -0.00328     0.19187    -0.1729
  Wide**                                                                                                  

                     (0.6417)    (0.3691)    (0.2131)   (0.0011)    (0.7301)      (0.9739)     (0.0534)   (0.0823)

  **PEP_DUM**        0.00884     0.03186     -0.16223   -0.04903    0.25668       0.06192      0.02445    0.17734

                     (0.9297)    (0.7505)    (0.1033)   (0.6246)    (0.0092)      (0.5364)     (0.8073)   (0.0746)
  ------------------ ----------- ----------- ---------- ----------- ------------- ------------ ---------- ---------------

**Exhibit E.2: Correlations among Explanatory Variables Used in
Regressions (continued)**

  --------------- -------------- ------------ ----------- ----------- ------------- ------------------ ---------------- ------------- --------------------- -------------
                  **Population   **CE_DUM**   **Per       **Cases per **COL_DUM**   **Judicial/Admin   **Universality   **CWO_DUM**   **IV-D/State-Wide**   **PEP_DUM**
                  Stability**                 Capita      FTE**                     Process**          Constant**                                           
                                              Personal                                                                                                      
                                              Income**                                                                                                      

                                                                                                                                                            

  **State         -0.06997       -0.13433     -0.40109    0.25758     0.04142       0.25622            -0.05506         0.09147       0.04662               0.00884
  Poverty**                                                                                                                                                 

                  (0.4847)       (0.1783)     (\<.0001)   (0.009)     (0.6793)      (0.0093)           (0.5826)         (0.3606)      (0.6417)              (0.9297)

  **TANF Now**    0.22449        -0.11643     0.24036     0.05043     0.06465       0.06131            -0.4963          -0.09236      -0.08986              0.03186

                  (0.0233)       (0.2439)     (0.015)     (0.6147)    (0.5186)      (0.5404)           (\<.0001)        (0.3559)      (0.3691)              (0.7505)

  **Job Growth**  -0.32174       0.05069      -0.02052    -0.1728     0.04634       0.01227            -0.19426         -0.11047      0.12433               -0.16223

                  (0.001)        (0.6129)     (0.8378)    (0.0824)    (0.6438)      (0.9026)           (0.0504)         (0.269)       (0.2131)              (0.1033)

  **Percent       -0.03871       0.00905      0.6836      0.12442     -0.06081      0.16472            -0.20859         -0.09891      -0.31874              -0.04903
  Urban**                                                                                                                                                   

                  (0.6993)       (0.9281)     (\<.0001)   (0.2128)    (0.5438)      (0.0981)           (0.0354)         (0.3226)      (0.0011)              (0.6246)

  **ARR_DUM**     -0.03953       0.11396      0.05857     -0.15503    0.74128       -0.18559           -0.32515         0.51607       -0.03457              0.25668

                  (0.6933)       (0.2541)     (0.5587)    (0.1198)    (\<.0001)     (0.0618)           (0.0009)         (\<.0001)     (0.7301)              (0.0092)

  **Men 20-64 Not 0.12158        -0.10194     -0.2287     0.22327     -0.11953      0.06808            0.1103           0.01433       -0.00328              0.06192
  Employed**                                                                                                                                                

                  (0.2235)       (0.308)      (0.0208)    (0.0241)    (0.2315)      (0.4966)           (0.2698)         (0.8863)      (0.9739)              (0.5364)

  **TANF Less     -0.01787       0.04729      -0.30744    0.15482     -0.05796      0.13314            0.33974          -0.00012      0.19187               0.02445
  Than 30**                                                                                                                                                 

                  (0.8586)       (0.6369)     (0.0017)    (0.1202)    (0.5628)      (0.1822)           (0.0005)         (0.9991)      (0.0534)              (0.8073)

  **Expenditure   -0.02282       -0.03175     0.25708     -0.75123    0.08557       -0.3265            -0.22966         0.03992       -0.1729               0.17734
  Per Case**                                                                                                                                                

                  (0.8199)       (0.7514)     (0.0091)    (\<.0001)   (0.3925)      (0.0008)           (0.0202)         (0.6904)      (0.0823)              (0.0746)

  **Population    1.0000         -0.09692     0.16726     0.09471     -0.12219      0.11429            -0.08807         0.02451       0.0107                0.14934
  Stability**                                                                                                                                               

                                 (0.3325)     (0.0929)    (0.3437)    (0.2212)      (0.2527)           (0.3787)         (0.8068)      (0.915)               (0.1341)

  **CE_DUM**      -0.09692       1.0000       0.0857      -0.04423    0.11396       -0.03569           0.03499          0.22176       0.13868               0.06762

                  (0.3325)                    (0.3918)    (0.659)     (0.2541)      (0.7217)           (0.727)          (0.0251)      (0.1645)              (0.4995)

  **Per Capita    0.16726        0.0857       1.0000      -0.00232    0.01367       0.08932            -0.19866         0.00334       -0.25043              0.04212
  Personal                                                                                                                                                  
  Income**                                                                                                                                                  

                  (0.0929)       (0.3918)                 (0.9815)    (0.8916)      (0.372)            (0.0453)         (0.9734)      (0.0111)              (0.6742)

  **Cases per     0.09471        -0.04423     -0.00232    1.0000      -0.13319      0.35635            0.10781          -0.01857      0.13208               -0.1243
  FTE**                                                                                                                                                     

                  (0.3437)       (0.659)      (0.9815)                (0.182)       (0.0002)           (0.2808)         (0.8531)      (0.1857)              (0.2132)

  **COL_DUM**     -0.12219       0.11396      0.01367     -0.13319    1.0000        -0.12796           -0.31417         0.51607       0.06618               0.14503

                  (0.2212)       (0.2541)     (0.8916)    (0.182)                   (0.1999)           (0.0013)         (\<.0001)     (0.5087)              (0.1458)
  --------------- -------------- ------------ ----------- ----------- ------------- ------------------ ---------------- ------------- --------------------- -------------

**Exhibit E.2: Correlations among Explanatory Variables Used in
Regressions (continued)**

  ------------------ -------------- ------------ ---------- ---------- ------------- ------------------ ---------------- ------------- --------------------- -------------
                     **Population   **CE_DUM**   **Per      **Cases    **COL_DUM**   **Judicial/Admin   **Universality   **CWO_DUM**   **IV-D/State-Wide**   **PEP_DUM**
                     Stability**                 Capita     per FTE**                Process**          Constant**                                           
                                                 Personal                                                                                                    
                                                 Income**                                                                                                    

                                                                                                                                                             

  **Judicial/Admin   0.11429        -0.03569     0.08932    0.35635    -0.12796      1.0000             0.05715          -0.05277      0.1353                -0.22125
  Process**                                                                                                                                                  

                     (0.2527)       (0.7217)     (0.372)    (0.0002)   (0.1999)                         (0.5683)         (0.5984)      (0.1752)              (0.0254)

  **Universality     -0.08807       0.03499      -0.19866   0.10781    -0.31417      0.05715            1.0000           -0.22586      -0.0039               -0.15444
  Constant**                                                                                                                                                 

                     (0.3787)       (0.727)      (0.0453)   (0.2808)   (0.0013)      (0.5683)                            (0.0225)      (0.969)               (0.1212)

  **CWO_DUM**        0.02451        0.22176      0.00334    -0.01857   0.51607       -0.05277           -0.22586         1.0000        -0.00572              0.30117

                     (0.8068)       (0.0251)     (0.9734)   (0.8531)   \<.0001       (0.5984)           (0.0225)                       (0.9545)              (0.0021)

  **Iv-D/State       0.0107         0.13868      -0.25043   0.13208    0.06618       0.1353             -0.0039          -0.00572      1.0000                -0.208
  Wide**                                                                                                                                                     

                     (0.915)        (0.1645)     (0.0111)   (0.1857)   (0.5087)      (0.1752)           (0.969)          (0.9545)                            (0.0359)

  **PEP_DUM**        0.14934        0.06762      0.04212    -0.1243    0.14503       -0.22125           -0.15444         0.30117       -0.208                1.0000

                     (0.1341)       (0.4995)     (0.6742)   (0.2132)   (0.1458)      (0.0254)           (0.1212)         (0.0021)      (0.0359)              
  ------------------ -------------- ------------ ---------- ---------- ------------- ------------------ ---------------- ------------- --------------------- -------------

**Appendix F**

**Regression Results**

**Regression Results -- 1999**

**Dependent Variable: Cases with Orders**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 6 8058.83138 1343.13856 17.81 \<.0001

Error 38 2865.73422 75.41406

Corrected Total 44 10925

Root MSE 8.68413 R-Square 0.7377

Dependent Mean 63.82000 Adj R-Sq 0.6963

Coeff Var 13.60722

NOTE: Model is not full rank. Least-squares solutions for the parameters
are not unique. Some

statistics will be misleading. A reported DF of 0 or B means that the
estimate is biased.

NOTE: The following parameters have been set to 0, since the variables
are a linear combination

of other variables as shown.

CWO_DUM = Intercept

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept B 38.11457 48.19014 0.79 0.4339

STPOVRT 1 -1.72314 0.42118 -4.09 0.0002

PCTURB 1 -0.21085 0.06864 -3.07 0.0039

CASEFTE 1 -0.04117 0.01088 -3.78 0.0005

JUDADMIN 1 -1.05489 0.41867 -2.52 0.0161

pop_stab 1 1.07315 0.58016 1.85 0.0721

TANFNOW 1 -0.24246 0.17539 -1.38 0.1749

CWO_DUM 0 0 . . .

**Dependent Variable: Current Collections**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 8 2247.46729 280.93341 6.55 \<.0001

Error 36 1544.36561 42.89904

Corrected Total 44 3791.83290

Root MSE 6.54974 R-Square 0.5927

Dependent Mean 52.91422 Adj R-Sq 0.5022

Coeff Var 12.37803

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 -0.99442 36.94354 -0.03 0.9787

PERSINC 1 0.00080114 0.00037687 2.13 0.0404

M20_64 1 -0.95811 0.33731 -2.84 0.0074

PCTURB 1 -0.10495 0.07267 -1.44 0.1573

TANFLT30 1 -0.48680 0.19932 -2.44 0.0196

CASEFTE 1 -0.01677 0.00842 -1.99 0.0539

TANFNOW 1 -0.25107 0.13560 -1.85 0.0723

pop_stab 1 1.13452 0.45841 2.47 0.0182

COL_DUM 1 -6.79280 3.15975 -2.15 0.0384

**Dependent Variable: Cost Effectiveness**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 6 42.91440 7.15240 12.14 \<.0001

Error 38 22.39370 0.58931

Corrected Total 44 65.30810

Root MSE 0.76766 R-Square 0.6571

Dependent Mean 4.37978 Adj R-Sq 0.6030

Coeff Var 17.52746

NOTE: Model is not full rank. Least-squares solutions for the parameters
are not unique. Some

statistics will be misleading. A reported DF of 0 or B means that the
estimate is biased.

NOTE: The following parameters have been set to 0, since the variables
are a linear combination

of other variables as shown.

CE_DUM = Intercept

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept B -0.17285 4.20138 -0.04 0.9674

M20_64 1 -0.15836 0.04032 -3.93 0.0003

PCTURB 1 -0.01465 0.00593 -2.47 0.0181

TANFNOW 1 -0.07029 0.01596 -4.40 \<.0001

TANFLT30 1 -0.12811 0.02263 -5.66 \<.0001

EXPCAS 1 -0.00527 0.00155 -3.41 0.0016

pop_stab 1 0.20483 0.04860 4.22 0.0001

CE_DUM 0 0 . . .

**Dependent Variable: Collection on Arrears**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 5 3294.53507 658.90701 9.35 \<.0001

Error 39 2748.98321 70.48675

Corrected Total 44 6043.51828

Root MSE 8.39564 R-Square 0.5451

Dependent Mean 54.95600 Adj R-Sq 0.4868

Coeff Var 15.27702

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 65.59074 6.66817 9.84 \<.0001

STPOVRT 1 -1.19645 0.36306 -3.30 0.0021

TANFNOW 1 -0.24949 0.15728 -1.59 0.1207

JOBGRWTH 1 4.34290 1.16237 3.74 0.0006

PCTURB 1 -0.11958 0.06277 -1.91 0.0642

ARR_DUM 1 7.81109 4.06190 1.92 0.0618

**Dependent Variable: Paternity Statewide**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 5 2923.77378 584.75476 3.11 0.0357

Error 17 3195.44908 187.96759

Corrected Total 22 6119.22286

Root MSE 13.71013 R-Square 0.4778

Dependent Mean 88.80130 Adj R-Sq 0.3242

Coeff Var 15.43911

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 -196.97060 89.52135 -2.20 0.0419

PERSINC 1 0.00088009 0.00071107 1.24 0.2327

TANFNOW 1 -0.75988 0.42356 -1.79 0.0906

EXPCAS 1 0.05738 0.03104 1.85 0.0820

pop_stab 1 3.07175 1.12218 2.74 0.0140

PEP_DUM 1 10.59652 6.40171 1.66 0.1162

**Dependent Variable: Paternity IV-D**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 4 10989 2747.21859 4.11 0.0165

Error 17 11366 668.57553

Corrected Total 21 22355

Root MSE 25.85683 R-Square 0.4916

Dependent Mean 68.41909 Adj R-Sq 0.3719

Coeff Var 37.79183

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 286.43091 58.80865 4.87 0.0001

STPOVRT 1 -3.15780 1.95746 -1.61 0.1251

TANFLT30 1 -3.83554 1.18152 -3.25 0.0047

UNICONST 1 1.13697 0.52791 2.15 0.0459

PEP_DUM 1 -11.79717 14.62399 -0.81 0.4310

**Regression Results -- 2000**

**Dependent Variable: Cases with Orders**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 7 8182.66666 1168.95238 14.70 \<.0001

Error 37 2942.31918 79.52214

Corrected Total 44 11125

Root MSE 8.91752 R-Square 0.7355

Dependent Mean 65.20356 Adj R-Sq 0.6855

Coeff Var 13.67643

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 67.43889 49.03867 1.38 0.1773

STPOVRT 1 -2.12308 0.51548 -4.12 0.0002

PCTURB 1 -0.23829 0.07220 -3.30 0.0021

CASEFTE 1 -0.04560 0.01244 -3.67 0.0008

JUDADMIN 1 -0.83898 0.43343 -1.94 0.0606

pop_stab 1 0.67485 0.59123 1.14 0.2610

TANFNOW 1 -0.28561 0.20797 -1.37 0.1779

CWO_DUM 1 8.59857 9.53486 0.90 0.3730

**Dependent Variable: Current Collections**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 8 2844.56323 355.57040 10.15 \<.0001

Error 36 1260.82360 35.02288

Corrected Total 44 4105.38683

Root MSE 5.91801 R-Square 0.6929

Dependent Mean 55.20244 Adj R-Sq 0.6246

Coeff Var 10.72056

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 45.55787 36.95822 1.23 0.2257

PERSINC 1 0.00024425 0.00029627 0.82 0.4151

M20_64 1 -1.35673 0.34473 -3.94 0.0004

PCTURB 1 -0.08300 0.06254 -1.33 0.1928

TANFLT30 1 -0.42740 0.17910 -2.39 0.0224

CASEFTE 1 -0.01980 0.00816 -2.43 0.0204

TANFNOW 1 -0.11326 0.13873 -0.82 0.4196

pop_stab 1 0.91014 0.43117 2.11 0.0418

COL_DUM 1 -18.42370 4.23813 -4.35 0.0001

**Dependent Variable: Cost Effectiveness**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 7 22.17630 3.16804 5.16 0.0004

Error 37 22.70836 0.61374

Corrected Total 44 44.88466

Root MSE 0.78342 R-Square 0.4941

Dependent Mean 4.15622 Adj R-Sq 0.3984

Coeff Var 18.84921

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 1.50479 4.30287 0.35 0.7285

M20_64 1 -0.09694 0.04241 -2.29 0.0281

PCTURB 1 -0.01526 0.00609 -2.51 0.0167

TANFNOW 1 -0.02716 0.01914 -1.42 0.1643

TANFLT30 1 -0.07066 0.02314 -3.05 0.0042

EXPCAS 1 -0.00375 0.00135 -2.78 0.0085

pop_stab 1 0.09588 0.04942 1.94 0.0600

CE_DUM 1 2.21300 0.87449 2.53 0.0158

**Dependent Variable: Collection on Arrears**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 5 1021.89538 204.37908 2.47 0.0491

Error 39 3230.61302 82.83623

Corrected Total 44 4252.50840

Root MSE 9.10144 R-Square 0.2403

Dependent Mean 59.62667 Adj R-Sq 0.1429

Coeff Var 15.26404

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 82.68194 8.41862 9.82 \<.0001

STPOVRT 1 -0.88484 0.45177 -1.96 0.0573

TANFNOW 1 -0.11058 0.19469 -0.57 0.5733

JOBGRWTH 1 2.15906 1.42211 1.52 0.1370

PCTURB 1 -0.14487 0.07072 -2.05 0.0473

ARR_DUM 1 -6.74237 5.61029 -1.20 0.2367

**Dependent Variable: Paternity Statewide**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 5 1747.13856 349.42771 1.30 0.3116

Error 17 4584.33190 269.66658

Corrected Total 22 6331.47046

Root MSE 16.42153 R-Square 0.2759

Dependent Mean 93.73130 Adj R-Sq 0.0630

Coeff Var 17.51979

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 -25.00667 102.43068 -0.24 0.8101

PERSINC 1 0.00076102 0.00077996 0.98 0.3429

TANFNOW 1 -0.63777 0.57307 -1.11 0.2812

EXPCAS 1 0.04935 0.03128 1.58 0.1330

pop_stab 1 1.24653 1.32069 0.94 0.3585

PEP_DUM 1 -10.57115 18.12064 -0.58 0.5673

**Dependent Variable: Paternity IV-D**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 4 14406 3601.42062 4.18 0.0154

Error 17 14639 861.13252

Corrected Total 21 29045

Root MSE 29.34506 R-Square 0.4960

Dependent Mean 82.39227 Adj R-Sq 0.3774

Coeff Var 35.61628

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 343.54272 65.05518 5.28 \<.0001

STPOVRT 1 -0.32810 2.57848 -0.13 0.9002

TANFLT30 1 -5.06439 1.46204 -3.46 0.0030

UNICONST 1 0.64169 0.59600 1.08 0.2967

PEP_DUM 1 -15.59723 13.03936 -1.20 0.2480

**Regression Results -- Combined 1999 and 2000 Data**

**Dependent Variable: Cases with Orders**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 7 16172 2310.26840 32.00 \<.0001

Error 82 5920.74271 72.20418

Corrected Total 89 22093

Root MSE 8.49730 R-Square 0.7320

Dependent Mean 64.51178 Adj R-Sq 0.7091

Coeff Var 13.17171

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 46.32065 33.59320 1.38 0.1717

STPOVRT 1 -1.85119 0.31065 -5.96 \<.0001

PCTURB 1 -0.22225 0.04791 -4.64 \<.0001

CASEFTE 1 -0.04350 0.00791 -5.50 \<.0001

JUDADMIN 1 -0.96685 0.29015 -3.33 0.0013

pop_stab 1 0.87550 0.39952 2.19 0.0313

TANFNOW 1 -0.24927 0.12911 -1.93 0.0570

CWO_DUM 1 9.77286 8.75448 1.12 0.2675

**Dependent Variable: Current Collections**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 8 4877.05749 609.63219 15.74 \<.0001

Error 81 3137.97136 38.74039

Corrected Total 89 8015.02885

Root MSE 6.22418 R-Square 0.6085

Dependent Mean 54.05833 Adj R-Sq 0.5698

Coeff Var 11.51382

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 10.42757 25.40170 0.41 0.6825

PERSINC 1 0.00057535 0.00022367 2.57 0.0119

M20_64 1 -0.99100 0.23168 -4.28 \<.0001

PCTURB 1 -0.09525 0.04659 -2.04 0.0442

TANFLT30 1 -0.44638 0.13347 -3.34 0.0013

CASEFTE 1 -0.02098 0.00570 -3.68 0.0004

TANFNOW 1 -0.21156 0.09531 -2.22 0.0292

pop_stab 1 1.09400 0.30958 3.53 0.0007

COL_DUM 1 -10.08189 2.38688 -4.22 \<.0001

**Dependent Variable: Cost Effectiveness**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 7 61.75004 8.82143 14.59 \<.0001

Error 82 49.56720 0.60448

Corrected Total 89 111.31724

Root MSE 0.77748 R-Square 0.5547

Dependent Mean 4.26800 Adj R-Sq 0.5167

Coeff Var 18.21654

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 0.30544 3.04429 0.10 0.9203

M20_64 1 -0.13110 0.02909 -4.51 \<.0001

PCTURB 1 -0.01486 0.00424 -3.50 0.0008

TANFNOW 1 -0.05144 0.01205 -4.27 \<.0001

TANFLT30 1 -0.10012 0.01621 -6.18 \<.0001

EXPCAS 1 -0.00473 0.00098584 -4.80 \<.0001

pop_stab 1 0.14968 0.03465 4.32 \<.0001

CE_DUM 1 1.71274 0.81446 2.10 0.0385

**Dependent Variable: Collection on Arrears**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 5 3662.58564 732.51713 8.64 \<.0001

Error 84 7124.28140 84.81287

Corrected Total 89 10787

Root MSE 9.20939 R-Square 0.3395

Dependent Mean 57.29133 Adj R-Sq 0.3002

Coeff Var 16.07467

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 74.06576 5.37450 13.78 \<.0001

STPOVRT 1 -1.14122 0.29205 -3.91 0.0002

TANFNOW 1 -0.22366 0.12920 -1.73 0.0871

JOBGRWTH 1 2.93334 0.88114 3.33 0.0013

PCTURB 1 -0.15171 0.04882 -3.11 0.0026

ARR_DUM 1 4.65317 3.31266 1.40 0.1638

**Dependent Variable: Paternity Statewide**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 5 4214.88192 842.97638 3.96 0.0052

Error 40 8515.31775 212.88294

Corrected Total 45 12730

Root MSE 14.59051 R-Square 0.3311

Dependent Mean 91.26630 Adj R-Sq 0.2475

Coeff Var 15.98674

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 -100.19997 65.34693 -1.53 0.1331

PERSINC 1 0.00073698 0.00049836 1.48 0.1470

TANFNOW 1 -0.68997 0.32361 -2.13 0.0392

EXPCAS 1 0.04996 0.02071 2.41 0.0205

pop_stab 1 1.99819 0.83049 2.41 0.0208

PEP_DUM 1 5.03522 5.81134 0.87 0.3914

**Dependent Variable: Paternity IV-D**

Analysis of Variance

Sum of Mean

Source DF Squares Square F Value Pr \> F

Model 4 25688 6422.06442 8.99 \<.0001

Error 39 27859 714.33549

Corrected Total 43 53547

Root MSE 26.72706 R-Square 0.4797

Dependent Mean 75.40568 Adj R-Sq 0.4264

Coeff Var 35.44435

Parameter Estimates

Parameter Standard

Variable DF Estimate Error t Value Pr \> \|t\|

Intercept 1 315.59308 42.03853 7.51 \<.0001

STPOVRT 1 -2.34203 1.47281 -1.59 0.1199

TANFLT30 1 -4.30330 0.89319 -4.82 \<.0001

UNICONST 1 0.89976 0.38263 2.35 0.0238

PEP_DUM 1 -16.44151 8.91646 -1.84 0.0728

**Appendix G**

**Spearman Correlations**

**Spearman Correlation Coefficient**

**Cases with Orders**

2 Variables: CWOACT CWOADJ

Simple Statistics

Variable N Mean Std Dev Median Minimum Maximum

CWOACT 45 65.20356 15.90096 66.29000 26.20000 92.59000

CWOADJ 45 65.20356 11.94367 66.62279 32.06021 91.67899

Spearman Correlation Coefficients, N = 45

Prob \> \|r\| under H0: Rho=0

CWOACT CWOADJ

CWOACT 1.00000 0.89736

\<.0001

CWOADJ 0.89736 1.00000

\<.0001

**Spearman Correlation Coefficient**

**Current Collections**

2 Variables: COLACT COLADJ

Simple Statistics

Variable N Mean Std Dev Median Minimum Maximum

COLACT 45 55.20244 9.65941 55.81000 35.15000 76.64000

COLADJ 45 55.20244 7.68945 54.57951 34.13689 76.77304

Spearman Correlation Coefficients, N = 45

Prob \> \|r\| under H0: Rho=0

COLACT COLADJ

COLACT 1.00000 0.73715

\<.0001

COLADJ 0.73715 1.00000

\<.0001

**Spearman Correlation Coefficient**

**Cost Effectiveness**

2 Variables: CEACT CEADJ

Simple Statistics

Variable N Mean Std Dev Median Minimum Maximum

CEACT 45 4.15622 1.01000 4.11000 1.31000 6.95000

CEADJ 45 4.15622 1.00850 4.05120 1.52836 6.81825

Spearman Correlation Coefficients, N = 45

Prob \> \|r\| under H0: Rho=0

CEACT CEADJ

CEACT 1.00000 0.56140

\<.0001

CEADJ 0.56140 1.00000

\<.0001

**Spearman Correlation Coefficient**

**Collection on Arrears**

2 Variables: ARRACT ARRADJ

Simple Statistics

Variable N Mean Std Dev Median Minimum Maximum

ARRACT 45 59.62667 9.83097 57.81000 33.17000 82.66000

ARRADJ 45 59.62667 8.88538 60.05794 36.11089 80.54704

Spearman Correlation Coefficients, N = 45

Prob \> \|r\| under H0: Rho=0

ARRACT ARRADJ

ARRACT 1.00000 0.84769

\<.0001

ARRADJ 0.84769 1.00000

\<.0001

[^1]: The 1975 law based incentives on collections for public assistance
    cases. A 1984 law expanded the incentive formula to include
    collections for non-public assistance cases.

[^2]: Incentives for non-TANF collections were capped at 115 percent of
    the amount paid for the TANF collections.

[^3]: Fishman, Michael E., Kristin Dybdal, and John Tapogna. 1999.
    *State Financing of Child Support Enforcement Programs: Final
    Report.* Prepared for the Assistant Secretary for Planning and
    Evaluation, DHHS. Washington DC.

[^4]: The 1975 law based incentives on collections for public assistance
    cases. A 1984 law expanded the incentive formula to include
    collections for non-public assistance cases.

[^5]: Incentives for non-TANF collections were capped at 115 percent of
    the amount paid for the TANF collections.

[^6]: Fishman, Michael E., Kristin Dybdal, and John Tapogna. 1999.
    *State Financing of Child Support Enforcement Programs: Final
    Report.* Prepared for the Assistant Secretary for Planning and
    Evaluation, DHHS. Washington DC.

[^7]: See Fishman Michael, John Tapogna, Kristin Dybdal, and Stephanie
    Laud. March 2000. *Preliminary Assessment of the Associations
    between State Child Support Enforcement Performance and Financing
    Structure.* Prepared for the Assistant Secretary for Planning and
    Evaluation and the Office of Child Support Enforcement. Washington,
    DC.

[^8]: As described below, we estimate linear equations relating
    performance to the demographic, economic, and programmatic factors
    expected to affect performance. The relationship between the
    performance measure Y and the explanatory variables X is assumed to
    be of the form Y~i\ =~ β~0~ + β~1~X~1i~ + β~2~X~2i~ + \.... +
    β~n~X~ni~ + ε~i~. Regression analysis provides estimates of the
    values of the ß terms.

[^9]: See Sorensen, Elaine and Ariel Halpern. December 1999. *Child
    Support Enforcement: How well is it doing?* Discussion Paper 99-11.
    The Urban Institute. Washington, DC.

[^10]: See Garfinkel, Irwin; Theresa Heintze and Chien-Chung Huang.
    December 2000. *Child Support Enforcement: Incentives and
    Well-Being.* Prepared for the Conference on Incentive Effects of Tax
    and Transfer Policies. Washington, DC.

[^11]: Current collections, collections on arrears, cost-effectiveness,
    and paternity establishment.

[^12]: Paternity establishment, cases with orders, collections on
    arrears, cost-effectiveness.

[^13]: After the 1996 welfare reform law was enacted (Personal
    Responsibility and Work Opportunity Reconciliation Act) states no
    longer had to pass through the first \$50 of child support
    collections.

[^14]: This section draws from U.S. Department of Health and Human
    Services. January 1997. *Incentive Funding Work Group: Report to the
    Secretary of Health and Human Services.*

[^15]: For example, it has been only within this decade that states
    measured the percentage of children on their caseloads for whom they
    had established paternity.

[^16]: In both FYs 1999 and 2000, 25 states and the District of Columbia
    submitted the statewide measure and 25 states submitted the IV-D
    measure. Guam, Puerto Rico, and the Virgin Islands submitted the
    statewide measure but were not part of the analysis.

[^17]: We used 1997-1999 data for the FY 1999 regressions and 1998-2000
    data for the FY 2000 regressions. We used three-year averages rather
    than single year averages because averaging poverty rates over
    several years improves the estimates' reliability.

[^18]: We tested a similar measure that captured non-employment for
    males aged 20 to 44, which typically exhibited less robust and
    stable results.

[^19]: This measure is based on Census data. At the time of this
    report's publication, 2000 Census data on urbanicity was not yet
    available.

[^20]: The Supplemental Survey provides an early look at a number of
    characteristics of the population in 2000, including economic,
    social, and housing characteristics. The results are available for
    50 states, the District of Columbia, and counties and cities with
    populations greater than 250,000.

[^21]: Many states are cutting IV-D staff due to budgetary constraints,
    thus the cases per FTE variable does not necessarily reflect OCSE
    preferences.

[^22]: See Lyon, Matthew. May 1999. *Characteristics of Families Using
    Title IV-D Services in 1995.* US Department of Health and Human
    Services, Assistant Secretary for Planning and Evaluation.

[^23]: California, Indiana, Kansas, Nevada, Ohio and Pennsylvania.

[^24]: A single model can be used if the coefficients for the
    interaction terms are the same for both paternity measures and are
    not significantly different from zero. This was not the case. Thus,
    we used two separate models.

[^25]: The Job Training Partnership Act was a federally funded program
    that provided training and other employment-related services to
    disadvantaged youth and adults and dislocated workers. JTPA operated
    between 1982 and 2000 and was one of the first federal programs to
    have an extensive performance management system in place.

[^26]: See Social Policy Research Associates (1999). *Guide to JTPA
    Performance Standards for Program years 1998 and 1999.* Menlo Park,
    CA: Social Policy Research Associates, p. III-1.

[^27]: Another use of performance adjustments is to provide incentives
    to the units of government to change their behavior. For example,
    OCSE might wish to encourage states to perform better on certain
    incentive measures. One method of encouraging such behavior is to
    reward states that take desired actions by adjusting their
    performance up and/or by lowering the measured performance of those
    states that do not undertake the desired actions. The concept of
    making adjustments based on policy concerns was suggested as a
    possibility for the JTPA program. See Burt S. Barnow and Jill
    Constantine (1988). *Using Performance Management to Encourage
    Services to Hard-to-Serve Individuals in JTPA.* Washington, D.C.:
    National Commission for Employment Policy.

[^28]: The same 6 states that were dropped from the regression analysis
    are not included here (California, Indiana, Kansas, Nevada, Ohio,
    and Pennsylvania).

[^29]: National Child Support Enforcement Association (1999).
    *Interstate Roster and Referral Guide*. Washington, D.C.: NCSEA.

[^30]: 1997 unpublished document.

[^31]: Canada Department of Justice (2000). *Expedited Child Support: An
    Overview of the Commonwealth Countries' and United States'
    Procedures for Establishing and Modifying Child Support.* On-line:
    http://www.canada.justice.gc.ca/en/ps/sup/cspo/expedited2.html

[^32]: Williams, V. (1993). *A Comparative Analysis of Minnesota's
    Administrative Process*. Denver: Policy Studies, Inc.