The IV-A / IV-D Interface and Data Exchange:

A Report of Survey Findings

April 2004

**Table of Contents**

[I. Executive Summary 2](#executive-summary)

[A. Purpose 2](#purpose)

[B. Findings 3](#findings)

[II. Introduction 7](#introduction)

[A. Objectives 8](#objectives)

[B. Background 9](#background)

[C. Urban Academy 10](#urban-academy)

[D. Approach 10](#approach)

[III. Overview of Systems 11](#overview-of-systems)

[IV. Best Practice Automation Findings
12](#best-practice-automation-findings)

[A. Data Exchange 12](#data-exchange)

[States with Separate Systems 13](#states-with-separate-systems)

[States with Integrated Systems 14](#states-with-integrated-systems)

[Related Data Exchange Topics 14](#related-data-exchange-topics)

[B. Data Quality 15](#data-quality)

[Data Quality Issues 16](#data-quality-issues)

[Factors Influencing Data Quality 19](#factors-influencing-data-quality)

[Technical Solutions for Improving Data Quality
21](#technical-solutions-for-improving-data-quality)

[Training Solutions for Improving Data Quality
24](#training-solutions-for-improving-data-quality)

[C. Collaboration 25](#collaboration)

[Technical Staff Collaboration Practices
25](#technical-staff-collaboration-practices)

[Technical Solutions for Data Access
27](#technical-solutions-for-data-access)

[D. Other Related Findings 28](#other-related-findings)

[Distinctions Between Large and Small Jurisdictions
28](#distinctions-between-large-and-small-jurisdictions)

[Regulatory Requirements Concerning Data Collection and Sharing
29](#regulatory-requirements-concerning-data-collection-and-sharing)

[V. Identifying Recommendations and Best Practices
29](#identifying-recommendations-and-best-practices)

[VI. Appendix 32](#appendix)

[A. Appendix A - DCL 03-28 document
32](#appendix-a---dcl-03-28-document)

[B. Appendix B - AT 89-09 document 33](#appendix-b---at-89-09-document)

[C. Appendix C - Interview Questions and Follow-up Questions
34](#appendix-c---interview-questions-and-follow-up-questions)

[D. Appendix D - Systems Overview by State
36](#appendix-d---systems-overview-by-state)

[E. Appendix E - State Agency Program Organization
40](#appendix-e---state-agency-program-organization)

# Executive Summary

## Purpose

The Federal Office of Child Support Enforcement (OCSE) contracted with
the State Information Technology Consortium (SITC) to complete a study
examining the exchange of case-related data between the Child Support
Enforcement (CSE) and Temporary Assistance for Needy Families (TANF)
programs at the state level. The primary purpose of the task is to
inventory common data elements that are currently shared between the
programs and to better understand the electronic interfaces currently in
place that facilitate data sharing.

In July 2003, OCSE issued a Dear Colleague Letter to State IV-A and IV-D
Directors urging that effective linkages be established to ensure that
"regular interaction" occurred to "provide optimum customer service" and
achieve state performance measures. The letter included several examples
of collaborative strategies initiated by different state or local
governments that increased the level of planning and/or collaboration
between TANF intake staff and IV-D caseworkers.

DCL-03-28 addresses the topic of collaboration primarily in
non-technical terms. For instance, examples of best practices
highlighted in the letter include co-location of staff or joint
applicant interview processing. Interaction refers primarily to the
sharing of custodial parent and non-custodial parent demographic,
historical and support order data.

Recent studies prepared on this topic tend to focus mainly on the
programmatic issues and less on the technical issues. Some studies
include a brief discussion highlighting the technical factors involved.
However, none of the studies examine the interface from a primarily
technical perspective. So what of the technical supports in place
facilitating interaction and collaboration? The objective of this study
is to examine the data that is being shared and highlight collaborative
approaches within the context of information technology.

Child support officials from 12 states and territories were contacted
between October and December 2003 and asked to participate in the study;
ten states were ultimately interviewed. Six of the states participating
in the study use separate database systems for collecting and storing
IV-D and IV-A program data; four states use an integrated database
system for program data. In some cases, the IV-D and IV-A agencies are
organized within two different state agencies; in most other cases, the
programs are organized within the same agency.

Interviews with state-designated representatives included open
discussion as well as a series of prepared questions. All participants
were asked during the interview to submit a list of data elements
included in the interface between the IV-D and IV-A database(s).

State representatives participating in the survey were asked to describe
their experiences in terms of:

-   the data elements involved in the interface between IV-D and IV-A
    systems;

-   electronic mechanisms of information exchange;

-   technical solutions that facilitate or improve information exchange;
    and

-   technical collaboration.

## Findings

OCSE, in response to the Family Support Act of 1988, issued Action
Transmittal (AT) 89-09 detailing the minimum set of data elements to be
exchanged via an interface between certified Family Assistance
Management Information Systems (FAMIS) and CSE systems. Since 1989, the
year that AT 89-09 was issued, both the child support and public
assistance programs have undergone substantial programmatic
transformation. However, the modifications in program scope and purpose
that have emerged over the past 15 years have not curtailed the need for
information sharing. In fact, there is undoubtedly greater need for
collaboration today than in previous years.

Because AT 89-09 is limited to addressing the [minimum]{.underline} data
exchange requirements, it does not provide a complete picture of the
actual information shared between IV-A and IV-D systems. Therefore,
states were asked to provide a list of the data elements currently
shared between their two systems. States using two separate databases
linked by an interface provided a list of data elements involved in the
exchange. States using an integrated system supplied copies of the
screens that IV-D and IV-A staff use to view data.

Over 180 unique pieces of data besides the elements listed in AT 89-09
were found to be exchanged between the two programs or viewable by the
other program's staff. Based on the information provided by the
participants, states using separate databases to store IV-A and IV-D
data are uploading an average of 47 additional data elements from their
IV-A system to the IV-D system. These data elements are in addition to
what is specified in AT 89-09. The range includes a low of 30 additional
elements (Nebraska) to a high of 66 (Wisconsin). In contrast, the
average number of additional elements uploaded from the IV-D system to
the IV-A system was found to be 28 elements. The table below lists the
common data elements not listed in AT 89-09 that a majority of states in
this group upload from the IV-A system to the IV-D system.

+-----------------------+-----------------------+----------------------+
| **Common Data         |                       |                      |
| Elements Uploaded to  |                       |                      |
| the IV-D System by a  |                       |                      |
| Majority of States    |                       |                      |
| Using Separate        |                       |                      |
| Systems**             |                       |                      |
+-----------------------+-----------------------+----------------------+
| ***[Appl              | ***[Absent            | ***[Depen            |
| icant]{.underline}*** | P                     | dent]{.underline}*** |
|                       | arent]{.underline}*** |                      |
| Sex                   |                       | Name                 |
|                       | Sex                   |                      |
| Social Security       |                       | Sex                  |
| Number                | Telephone Number      |                      |
|                       |                       | Social Security      |
| Date of Birth         | Employer Name / ID    | Number               |
|                       |                       |                      |
| Telephone Number      | Employer Address      | Date of Birth        |
|                       |                       |                      |
| Cooperation / Good    | Employer Telephone    |                      |
| Cause Effective       |                       |                      |
| Date(s)               | Employment Start /    |                      |
|                       | Stop Dates            |                      |
| Cooperation / Good    |                       |                      |
| Cause Reason Code     | Separation / Divorce  |                      |
|                       | Date                  |                      |
+-----------------------+-----------------------+----------------------+

Two of the four states using an integrated database system for IV-A and
IV-D program activity provided copies of the system screens to which
workers from each program have access. Based on the data fields found on
the screens, these states are sharing an average of 51 additional
IV-D-related data elements with IV-A staff and an average of 59
additional IV-A-related data elements with IV-D staff.

Participants were asked if any data elements are currently collected by
one program but not shared with the other that would be useful for
meeting programmatic requirements. A majority of states indicated that
there were none. All states reported that IV-D staff are provided
restricted, read-only rights to IV-A system data. Likewise, most states
reported that IV-A staff have some type of restricted access to IV-D
system data.

States were then asked if there is data not currently collected that
would be useful for meeting programmatic requirements. More than half of
the respondents indicated that there was data that would help, however
each cited different data elements unique to their state. When asked if
regulatory restrictions hamper data sharing, most states indicated that
there are limits but nothing that impedes a program from meeting
programmatic objectives.

States have built a technical capacity to share information that exceeds
the 20 plus elements specified in AT 89-09. But providing a facility for
staff to record information and disseminate it to another program does
not guarantee that the quality of the data will be satisfactory, so
participants were asked for their thoughts concerning data quality. If
issues were cited, they were also asked for any technical solutions that
have been implemented or that are in the process of being developed that
serve to improve data quality.

When states were asked if the quality of the data being exchanged can be
improved, eight of the ten replied affirmatively. Respondents cited
several major issues impacting quality, including data collection
errors, minimal data collection, and data maintenance.

Almost all of the participants discussed problems arising from errors
made during the data collection process. Factors discussed included the
significance of imperfect clearance processing, inaccurate or skipped
data entry, and improper coding. Inaccurate clearance processing can
result in duplicate record creation and additional use of staff time to
resolve inconsistencies.

Another significant issue discussed by many participants involves the
extent of the data collection effort during the client interview
process. In general, participants citing this issue expressed
frustration that sometimes a minimal amount of data is collected.

Another interesting issue introduced by some of the participants
involves data maintenance. Maintenance refers to the process of updating
the database with new information about a custodial or non-custodial
parent as it becomes available. This is significant because the program
with the most current and accurate data source should theoretically have
the ability to update the database. Furthermore, since the enactment of
PRWORA, state IV-D programs have increased the number of interfaces with
other state and Federal databases that provide access to the most
current information.

Interview participants then identified and discussed several different
factors that often influence the degree of data quality. More often than
not, multiple factors simultaneously contribute to substandard data
collection. Data entry error accounts for some problems, and there
appears to be greater opportunity for data entry errors to occur when
information is entered into the system from a paper form as opposed to
direct entry into the system during the client interview.

Participants highlighted several different root causes for incomplete
data collection including: insufficient time for data collection;
insufficient staff resources; and a lack of understanding on the part of
TANF staff about why child support data is collected and how it is used,
or why it is their responsibility to collect it. A few states cited
technical issues that affected data quality.

Participants discussed about technically-based solutions that their
state employed to negate the affects of these factors. A variety of
approaches have been designed and implemented to counteract the issues
previously described. The particular approach that any single state
adopts will be influenced by circumstances such as the nature of the
problem, resources, time, and urgency. For instance, sometimes a
technical approach is favored, sometimes a training-based method is
preferred, and other times the process is reviewed and modified.

The table below provides an overview of the types of solutions states
reported in the survey.

+------------------------+--------------+--------------+--------------+
| **[                    | *            | **Training** | *            |
| Problem]{.underline}** | *Technical** |              | *[Process]{. |
|                        |              | **           | underline}** |
|                        | **           | [Solution]{. |              |
|                        | [Solution]{. | underline}** | **[Review]{. |
|                        | underline}** |              | underline}** |
+------------------------+--------------+--------------+--------------+
| Data Collection Errors | X            | X            | X            |
|                        |              |              |              |
| Clearance Process      | X            | X            | X            |
|                        |              |              |              |
| Skipped Data Entry     | X            |              |              |
|                        |              |              |              |
| Improper Coding        |              |              |              |
+------------------------+--------------+--------------+--------------+
| Minimal Data           | X            | X            |              |
| Collection             |              |              |              |
+------------------------+--------------+--------------+--------------+
| Data Maintenance       |              |              | X            |
+------------------------+--------------+--------------+--------------+

Since effective data exchange and integration of IV-A and IV- D systems
requires communication and cooperation on the part of both programs,
states were asked about the level of collaboration between TANF and CSE.

All ten states reported that staff from both programs were involved in
the design and development of the integrated system or the interface
between separate systems. Furthermore, more than half of the states
stated that technical staff meet routinely to discuss IT problems
concerning the interface or other related technical issues. Sometimes
program staff participate in these technical meetings. All the states
felt that regularly scheduled contact improved communication and
collaboration.

Other methods implemented include: using a single IT vendor to design /
upgrade both systems; co-locating technical staff from each program;
regular use of user focus groups involving both IV-A and IV-D local
district staff; and designating liaisons at local district sites that
interact with system users and provide feedback to technical staff
located in the central office. Most of the states interviewed reported
that staff from each program have some level of restricted, read-only
access to the other's data.

Finally, States were asked if any distinctions exist between larger
jurisdictions and smaller to medium size jurisdictions. In terms of
technical capability, none of the states indicated any difference
between large and small areas. However, a majority of states reported
that significant operational differences can exist.

Several participants commented that smaller and medium sized
jurisdictions tend to have less staff turnover which means that the
workers collecting data have greater experience and knowledge. As a
result, lack of experience can lead to greater errors (for example, when
coding). Furthermore, new staff may not receive training about the IV-D
program or the electronic referral. Therefore, a greater number of staff
in larger jurisdictions potentially do not have a good understanding of
why they are collecting the data or the importance of the non-custodial
parent information for the child support process.

There also seems to be greater collaboration between IV-A and IV-D staff
in smaller jurisdictions. Caseworkers from TANF and CSE tend to share
case information and collaborate more on their respective cases. This
results in more accurate case data, which ultimately results in correct
and timely case handling.

Participants also pointed to the fact that staff in larger jurisdictions
tend to have bigger caseloads and therefore less time to spend with
individual clients, and that can hamper thorough data collection.

# Introduction

The Federal Office of Child Support Enforcement (OCSE) contracted with
the State Information Technology Consortium (SITC) to complete a study
examining the exchange of case-related data between the Child Support
Enforcement (CSE) and Temporary Assistance for Needy Families (TANF)
programs at the state level. The primary purpose of the task is to
inventory common data elements that are currently shared between the
programs and to better understand the electronic interfaces currently in
place that facilitate data sharing. The study also seeks to identify
impediments related to data exchange and cite specific examples of
technical solutions implemented by states to circumvent them.

## Objectives

In July 2003, OCSE issued a Dear Colleague Letter[^1] to State IV-A and
IV-D Directors urging that effective linkages be established to ensure
that "regular interaction" occurred to "provide optimum customer
service" and achieve state performance measures.[^2] The letter included
several examples of collaborative strategies initiated by different
state or local governments that increased the level of planning and/or
collaboration between TANF intake staff and IV-D caseworkers.

DCL-03-28 addresses the topic of collaboration primarily in
non-technical terms. For instance, examples of best practices
highlighted in the letter include co-location of staff or joint
applicant interview processing. Interaction refers primarily to the
sharing of custodial parent and non-custodial parent demographic,
historical and support order data.

Recent studies prepared on this topic tend to focus mainly on the
programmatic issues and less on the technical issues. Some studies
include a brief discussion highlighting the technical factors involved.
However, none of the studies examine the interface from a primarily
technical perspective. So what of the technical supports in place
facilitating interaction and collaboration? The objective of this study
is to examine the data that is being shared and highlight collaborative
approaches within the context of information technology.

State representatives participating in the survey were asked to describe
their experiences in terms of:

-   the data elements involved in the interface between IV-D and IV-A
    systems[^3];

-   electronic mechanisms of information exchange;

-   technical solutions that facilitate or improve information exchange;
    and

-   technical collaboration.

The results of this study will be made available to other states so that
they can benefit from their peers' experiences.

## Background

Title IV-D of the Social Security Act of 1975 authorized states to
create a Child Support Enforcement office, and since that time IV-D
programs have shared information with their IV-A counterparts.[^4]
Although both programs have undergone varying degrees of reform over
time, the necessity for collaboration has not diminished.

The Family Support Act of 1988 established minimum requirements for
comprehensive, statewide automated systems supporting IV-D program
activity. Furthermore, it required the system to automatically accept
and process referrals from the IV-A agency. [^5] Initially, IV-D
referral data was mainly processed using paper forms; now the
information is transmitted both electronically and via paper. In some
cases, states have discontinued the use of paper altogether. This trend
will most likely continue into the near future.

The enactment of the Personal Responsibility and Work Opportunity
Reconciliation Act of 1996 (PRWORA) introduced significant opportunities
and challenges for both programs, including new requirements for
additional databases and interfaces.[^6] One of the key concepts
introduced in the landmark legislation was promoting self-sufficiency.
With the introduction of the TANF 60-month benefit time limit, state
assistance programs were pressed to help clients achieve a sufficient
and stable level of income that would enable them to first reduce and
eventually eliminate dependence on a monthly assistance check. Because
many assistance cases involved single-parent households that received
little to no financial support from the absent parent, one of the
strategies to emerge from PRWORA was to increase the level of
collaboration between TANF and CSE programs.

Information technology provides a means to effectively and efficiently
increase information sharing and collaboration between two or more
parties. Given that local-level staff from each program are often times
not co-located, automation is the primary tool relied upon by
caseworkers to communicate key pieces of case data.

OCSE, in response to the Family Support Act legislation, issued Action
Transmittal (AT) 89-09 in May of 1989 detailing the minimum set of data
elements to be exchanged via an interface between certified Family
Assistance Management Information Systems (FAMIS) and CSE systems.
Fifteen years have passed since those data requirements were first
issued. Given that significant programmatic changes have occurred and
that State governments have become more technically savvy since 1989,
the data requirements in AT 89-09, along with some of the significant
issues related to their exchange, are examined in this study to discover
if any modifications to AT 89-09 are warranted.

## Urban Academy

The Office of Child Support Enforcement, in cooperation with the Office
of Family Assistance, will be conducting its first IV-A/IV-D Academy for
four urban jurisdictions with the largest caseload \-- New York City,
Houston, Philadelphia and Los Angeles. The purpose of the Academy is to
explore operational issues and exchange best practices offering
practical solutions which can be implemented or pilot-tested at these
sites.

A number of the issues identified by these jurisdictions are similar to
those reported by the states participating in this study. Some examples
are: the need to have payment and order screens available to IV-A
workers as well as IV-D; to notify IV-A workers when child support
obtains an order or when first payment is received and to have more
automated exchange of accurate custodial parent and non-custodial parent
information. Continuous training was also identified as a crucial need
for IV-A and IV-D workers for sharing information; learning new
technologies; and most importantly, for explaining the importance of how
IV-A/IV-D collaborative efforts can improve services for needy families
and children.

Improving IV-A and IV-D collaboration is a top federal priority to bring
about real change for the neediest families. The sites were selected
since they have a great potential for impacting large numbers of
families and children and for improving state performance/outcomes for
both programs. OCSE will invite a total of six IV-A/IV-D representatives
from each urban jurisdiction to participate in the inaugural Academy,
which will be held on June 14-15, 2004 in Washington, D.C.

## Approach

Child support officials from 12 states and territories[^7] were
contacted between October and December 2003 and asked to participate in
the study; ten states were ultimately interviewed. Six of the states
participating in the study use separate database systems for collecting
and storing IV-D and IV-A program data; four states use an integrated
database system for program data.[^8] In some cases, the IV-D and IV-A
agencies are organized within two different state agencies; in most
other cases, the programs are organized within the same agency.

Interviews with state-designated representatives included open
discussion as well as a series of prepared questions.[^9] All
participants were asked during the interview to submit a list of data
elements included in the interface between the IV-D and IV-A
database(s). Notes taken during the interview sessions were sent to each
state for confirmation.

An assessment of data elements and interview responses took place during
January 2004. Follow-up questions were prepared based on the results of
the review and delivered to the participants during the same month.
Responses are incorporated into the report.

The findings included in this report are based on the information
collected during the interviews and the responses participants provided
to the post-interview follow-up questions. The technical solutions
implemented to support better exchange of case-related data that are
presented in this report will be reviewed and discussed with members of
the IV-A / IV-D Workgroup at the ACF State Systems Summit in May 2004.
Workgroup members will be asked to formulate recommendations regarding
best practices and next steps during the meeting.

# Overview of Systems

The table in Appendix D provides an overview of the IV-D and IV-A
systems used by each state participating in the study. Technical
specifications for each system were taken from the Administration for
Children and Families (ACF) State Systems Profile website.[^10]

Approximately half of the IV-D systems were implemented prior to 1996
while the remaining systems were implemented in 1996 or after. The
oldest systems involved in this study were implemented in 1991. As
mentioned previously, four states have implemented an integrated
database to store IV-D and IV-A information. The remaining six states
implemented separate systems with a corresponding interface; four of
these states developed their IV-D and IV-A systems using the same
programming language, database management system, and operating system.
One state, Nevada, adopted the integrated system of another state
participating in this study (Rhode Island) and rewrote the code using a
different language.

The IV-A and IV-D systems primarily rely on batch processing to transfer
select data from one system/module to the other, although in some cases,
there is limited real-time exchange of specific data elements. Batch
processing is performed nightly in most cases.

States using an integrated database approach have no need for an
interface. Most of these states reported that each program has read-only
access to the other's data in addition to the screens used for data
entry. One state, Nevada, reported that IV-D and IV-A are in the process
of jointly developing screens to which both programs will have access.
Two states in this group talked about data ownership issues,
particularly location information for the non-custodial parent. It seems
that the program "owning" update rights to some data elements may not
necessarily have the most current information available. This is due in
part to accessibility to newly developed state databases such as new
hire repositories.

States using an integrated database approach tend not to use paper
referrals at all; the referral is sent electronically. In contrast,
states using separate systems mostly use a combination of paper and
electronic referrals. One state in this group, Washington, recently
eliminated paper referrals after determining that the electronic method
provided data as good or better than the paper method. Another state,
Wisconsin, reported that paper referrals are rarely used. Some states
using a combination of paper and electronic referrals have developed
automated or manual methods for ensuring a one-to-one match.

# Best Practice Automation Findings

## Data Exchange

Since 1989, the year that AT 89-09 was issued, both the child support
and public assistance programs have undergone substantial programmatic
transformation. However, the modifications in program scope and purpose
that have emerged over the past 15 years have not curtailed the need for
information sharing. In fact, there is undoubtedly greater need for
collaboration today than in previous years. Because AT 89-09 is limited
to addressing the [minimum]{.underline} data exchange requirements, it
does not provide a complete picture of the actual information shared
between IV-A and IV-D systems. Therefore, states were asked to provide a
list of the data elements currently shared between their two systems.
The list of elements that each state provided was compared to the list
of elements provided by other states in an effort to understand the
current breadth and depth of the data exchange process. Participating
states were also asked for feedback on two topics that come to mind when
thinking about data exchange: data accessibility and regulatory
restrictions governing data sharing.

States using two separate databases linked by an interface provided a
list of data elements involved in the exchange. States using an
integrated system supplied copies of the screens that IV-D and IV-A
staff use to view data.[^11] The elements were recorded in a spreadsheet
and linked with the appropriate state(s). Over 180 unique pieces of data
besides the elements listed in AT 89-09 were found to be exchanged
between the two programs or viewable by the other program's staff.[^12]
For discussion purposes, the elements were sorted into one of four
high-level categories and then grouped into one of several
subcategories. The table below lists the categories and subcategories
used.

+-------------+-------------+-------------+-------------+------------+
| ***         | **Applicant | **Absent    | **Dependent | **Order    |
| Category*** | / Head of   | Parent /    | / Child**   | of**       |
|             | Household / | Absent      |             |            |
|             | Custodian** | Relative**  |             | *          |
|             |             |             |             | *Support** |
+=============+=============+=============+=============+============+
| ***Subca    | Ide         | Ide         | Ide         | (None)     |
| tegories*** | ntification | ntification | ntification |            |
|             | /           | /           | /           |            |
|             | Demographic | Demographic | Demographic |            |
|             |             |             |             |            |
|             | Household   | Household   | Household   |            |
|             | Status      | Status      | Status      |            |
|             |             |             |             |            |
|             | Good Cause  | Education / | Education   |            |
|             |             | Employment  |             |            |
|             | Education / |             | Health      |            |
|             | Employment  | Health      | Coverage    |            |
|             |             | Coverage    |             |            |
|             | Health      |             |             |            |
|             | Coverage    | Veteran /   |             |            |
|             |             | Government  |             |            |
|             | TANF        | Status      |             |            |
|             | Eligibility |             |             |            |
|             | / Benefits  | Court /     |             |            |
|             |             | Criminal    |             |            |
|             |             | Status      |             |            |
|             |             |             |             |            |
|             |             | Other       |             |            |
+-------------+-------------+-------------+-------------+------------+

###  {#section .unnumbered}

### States with Separate Systems  {#states-with-separate-systems .unnumbered}

All six states using separate systems to record and store IV-A and IV-D
data provided information about the data elements currently exchanged
between their systems.[^13] Based on the information in these lists,
states in this category are uploading an average of 47 additional data
elements from their IV-A system to the IV-D system. These data elements
are in addition to what is specified in AT 89-09. The range includes a
low of 30 additional elements (Nebraska) to a high of 66 (Wisconsin).
Only two states in this group provided information about the data
elements uploaded from the IV-D system to the IV-A system. The average
number of additional elements uploaded to the IV-A system is 28.

The table below lists the common data elements not listed in AT 89-09
that a majority of states in this group upload from the IV-A system to
the IV-D system.[^14] Additional identification / demographic type data
for the applicant and dependent is exchanged by a majority of states.
Good cause information about the applicant and relationship information
about the dependent is frequently exchanged. Data pertaining to the
identification / demographic and employment of the absent parent is also
frequently passed.

+-----------------------+-----------------------+----------------------+
| **Common Data         |                       |                      |
| Elements Uploaded to  |                       |                      |
| the IV-D System by a  |                       |                      |
| Majority of States    |                       |                      |
| Using Separate        |                       |                      |
| Systems**             |                       |                      |
+-----------------------+-----------------------+----------------------+
| ***[Appl              | ***[Absent            | ***[Depen            |
| icant]{.underline}*** | P                     | dent]{.underline}*** |
|                       | arent]{.underline}*** |                      |
| Sex                   |                       | Name                 |
|                       | Sex                   |                      |
| Social Security       |                       | Sex                  |
| Number                | Telephone Number      |                      |
|                       |                       | Social Security      |
| Date of Birth         | Employer Name / ID    | Number               |
|                       |                       |                      |
| Telephone Number      | Employer Address      | Date of Birth        |
|                       |                       |                      |
| Cooperation / Good    | Employer Telephone    |                      |
| Cause Effective       |                       |                      |
| Date(s)               | Employment Start /    |                      |
|                       | Stop Dates            |                      |
| Cooperation / Good    |                       |                      |
| Cause Reason Code     | Separation / Divorce  |                      |
|                       | Date                  |                      |
+-----------------------+-----------------------+----------------------+

[]{.mark}

As mentioned above, an average of 47 additional elements are uploaded
from IV-A systems to IV-D systems. Of these 47 data elements, 17 pertain
to the applicant / custodial parent, 15 elements relate to the absent
parent, 13 elements are related to the dependent, and two elements are
related to support orders.

### States with Integrated Systems  {#states-with-integrated-systems .unnumbered}

Two of the four states using an integrated database system for IV-A and
IV-D program activity provided copies of the system screens to which
workers from each program have access. Based on the data fields found on
the screens, these states are sharing an average of 51 additional
IV-D-related data elements with IV-A staff and an average of 59
additional IV-A-related data elements with IV-D staff.

It wasn't possible to determine if common elements are shared between
IV-A and IV-D programs in this group because of the limited number of
states providing the necessary information.

### Related Data Exchange Topics {#related-data-exchange-topics .unnumbered}

Participants were asked if any data elements are currently collected by
one program but not shared with the other that would be useful for
meeting programmatic requirements. A majority of states (seven out of
ten) indicated that there were none. All states reported that IV-D staff
are provided restricted, read-only rights to IV-A system data. Likewise,
most states reported that IV-A staff have some type of restricted access
to IV-D system data. Information regarding methods used by staff to
access data is discussed further in the Collaboration section of this
report in the subsection entitled *Technical Solutions for Data Access*.

States were then asked if there is data not currently collected that
would be useful for meeting programmatic requirements. More than half of
the respondents indicated that there was data that, if collected, would
help. Data cited include paternity information, absent parent driver's
license, absent parent American Indian indicator, additional insurance
information, medical insurance for the dependent, dependent birth state,
and out-of-state orders of support. The six states responding yes to the
question each cited different data elements unique to their state.

When asked if regulatory restrictions hamper data sharing, most states
indicated that there are limits but nothing that impedes a program from
meeting programmatic objectives. Several states indicated that financial
information obtained by IV-D from the Internal Revenue Service cannot be
shared with IV-A. One state, Washington, stated that it would like to
share Financial Institution Data Match information because it could be
of value during eligibility determination. Another state, Rhode Island,
indicated that it could not share Vital Statistic information collected
for IV-D purposes, primarily because the existing law that enables Child
Support to obtain the data (it is written specifically for IV-D) does
not include language that permits them to share the data.

Officials from Wisconsin IV-D believe that it is unnecessary to require
the support order information from TANF as outlined in AT 89-09. They
feel that the custodial parent and non-custodial parent demographic data
are the key pieces of data required from IV-A staff, and that this
information, when complete and accurate, facilitates the collection of
support data by the IV-D caseworker.

## Data Quality

It was learned, in the previous section, that states are going beyond
the minimum data exchange requirements. It was also discovered that
states have built a technical capacity to share information that exceeds
the 20 plus elements specified in AT 89-09. But providing a facility for
staff to record information and disseminate it to another program does
not guarantee that the quality of the data will be satisfactory.[^15] So
participants were asked for their thoughts concerning data quality. If
issues were cited, we also asked for any technical solutions that have
been implemented or that are in the process of being developed that
serve to improve data quality.

### Data Quality Issues {#data-quality-issues .unnumbered}

When participants were asked if the quality of the data being exchanged
can be improved, eight of the ten replied affirmatively. Participants
raised several issues, including data collection errors, minimal data
collection, and data maintenance.[^16]

Almost all of the participants discussed problems arising from errors
made during the data collection process. Topics discussed included the
significance of imperfect clearance processing, inaccurate or skipped
data entry, and improper coding.

A majority of the participants discussed the effect on the IV-D process
when the clearance procedure fails to detect that an applicant is known
to the system.[^17] In most states, the clearance process is used to
check the prior history of the custodial parent (i.e., the applicant).
In some states, a system check is also performed on the non-custodial
parent if the appropriate search criteria is available.

States reported that inaccurate clearance processing can result in
duplicate record creation and additional use of staff time to resolve
inconsistencies. Florida stated that the current process requires manual
review of the data and unfortunately allows for the creation of
duplicate individuals or cases when the review is less than
satisfactory. Correction of the duplicative case or individual is
complicated and can require programming staff to correct the errors.
Florida IV-D is continuing work on identifying fixes to the client
registration and clearance process on FLORIDA (the integrated IV-A /
IV-D system).

In Maryland, CARES (the IV-A system) is searched for records pertaining
to the custodial parent and non-custodial parent. Officials reported
that existing case information about an absent parent known to CARES is
not always detected during the TANF clearance process. And although IV-D
staff are normally able to discover that an absent parent is actually
known to CARES, it happens after some time has elapsed and staff
resources have been used.

In Nevada, when an application for TANF is entered into the integrated
system known as NOMADS, the clerical staff are to determine if any
parties currently exist in the system. If one or more of the parties
exist, the staff are supposed to determine if the application would
result in a new case due to a change in participants, or if the
application refers to all parties in an existing case. Rather than do
this person search/resolution process, clerical staff are creating
pseudo cases which then require a merge later on.

Wisconsin IV-D stated the desire for TANF workers to be more consistent
while validating custodial parent social security numbers when staff
seek to determine if the applicant is known to the IV-A system, CARES.
TANF staff also may collect the non-custodial parent social security
number. A positive identification provides important case history data
that enables child support to determine support order status. It also
helps to prevent duplicate records for the same custodial or
non-custodial parent.

Clearance process issues are not the only concern during data
collection. For example, if TANF staff record IV-D information on a
paper form, and then later data enter the information into the system,
errors can also occur. Louisiana considers that the amount of data found
on the paper referral is superior to the electronic referral and also
more reliable because TANF staff either make data entry errors or they
skip entering data found on the paper form. Montana and Pennsylvania
reported experiencing similar problems.

A study conducted by Pennsylvania IV-D found that many data collection
errors were a result of worker mistakes, the two most common being
incorrect data / missing data and incorrect coding on the part of IV-A
intake staff. Pennsylvania was not the only state to cite the coding
problem; most states also indicated that they currently experience or
previously experienced problems with inaccurate coding.[^18] Washington,
for example, mentioned that both IV-A and IV-D staff can make errors
when coding.

Another significant issue discussed by many participants involves the
extent of the data collection effort during the client interview
process. In general, participants citing this issue expressed
frustration that sometimes a minimal amount of data is collected. For
example, Maryland has found that many times TANF staff only collect a
minimum amount of information about the non-custodial parent, enough to
generate the electronic referral. Nebraska also reported that sometimes
minimal data is collected about the non-custodial parent. Rhode Island
cited insufficient non-custodial parent data collection, but not on the
part of TANF workers ("they have been doing it longer") but rather when
Medicaid staff collect the data.

Washington IV-D reported that, prior to the redesign of its interface,
TANF workers were reluctant to enter child support data that duplicated
the paper referral form used for data collection and did not follow or
match the associated data entry fields in ACES (the IV-A system).

Florida IV-D stated that even though the data fields exist, public
assistance staff rarely complete the screens related to child support
obligations. Public assistance staff might not take the opportunity to
ask the questions provided on the system that would contribute to child
support's ability to obtain an order for support.

Another interesting issue introduced by some of the participants
involves data maintenance. Maintenance refers to the process of updating
the database with new information about a custodial or non-custodial
parent as it becomes available. This is significant because the program
with the most current and accurate data source should theoretically have
the ability to update the database. Furthermore, since the enactment of
PRWORA, state IV-D programs have increased the number of interfaces with
other state and Federal databases that provide access to the most
current information.

Rhode Island IV-A and IV-D are currently talking about this important
data quality issue in regards to custodial parent information. In
particular, IV-D is requesting update rights from IV-A for the custodial
parent address. IV-D has access to the most current address information
because of an interface with the state's new hire database (whose data
is updated three times a week). TANF currently has sole ownership of the
address data, but IV-D is talking with IV-A officials about instituting
dual ownership of custodial parent address data.

Nevada reported a similar issue involving the non-custodial parent
address. Nevada CSE receives current address data via several interfaces
with various statewide databases. However, because TANF and Food Stamps
collect this address information from the custodial parent during client
contact, the three programs were continually in the process of verifying
the most up-to-date data. They resolved the issue by establishing rules
governing data ownership. If the person at issue is a non-custodial
parent on a IV-D case, the IV-D worker is given update access to the
data. If the person at issue is a child or a custodial parent on a TANF
case, the TANF workers have update authority to the information. If a
caseworker who does not have update ability has information he/she feels
is important to the case, he/she can make a contact entry in the case
and alert the worker to the new information.

Finally, data quality issues are not solely associated with the TANF
program. Although not the focus of this study, it is worthwhile to
mention that Medicaid and Child Welfare programs also collect IV-D data
depending on case circumstances. Rhode Island remarked that it is
seeking better information about the non-custodial parent when collected
by the Medicaid program staff during intake and eligibility
determination. It explained that both TANF and Medicaid are required to
collect data regarding absent parents. The difference is that IV-A has
been collecting this data for a longer period of time (Medicaid only
began collecting it with the onset of PRWORA) and that part of the
problem is a learning curve. The other part of the problem is related to
system requirements. The TANF program requires a category for
eligibility (absence, death, incapacity or unemployment of the
non-custodial parent) and the INRHODES system cannot determine
eligibility unless both parents are accounted for. In the case of
Medicaid, such categories are not required and eligibility is primarily
income based and is determined for each individual in the family.
Therefore, data collection on the non-custodial parent is required by
regulation but not by the system.

Florida IV-D reported that it receives very limited automated updates on
IV-E foster care cases. Currently, a case is automatically created, but
child support does not have immediate access to information on the
public assistance side. The actual management of the foster care case is
done on a separate system that does not interface with FLORIDA (the
integrated TANF and CSE system). Any change to the child's status is
documented and maintained on this separate system and updates to FLORIDA
are performed manually by the IV-E caseworker. Information regarding the
child's residence, status of any judicial dependency actions, or status
of the parental rights is not readily available and many times the IV-D
case has remained open when it should have been closed.

### Factors Influencing Data Quality {#factors-influencing-data-quality .unnumbered}

Interview participants identified and discussed several different
factors that often influence the degree of data quality. More often than
not, multiple factors simultaneously contribute to substandard data
collection.

It was previously stated that data entry error accounts for some
problems. Some states opt to use purely electronic forms of data
collection during the client interview, bypassing paper forms
altogether. Most states in the survey continue to use paper forms to
collect client information and subsequently enter the data into the
system after the interview. Data entry is sometimes performed by the
caseworker and other times by data entry staff. There appears to be
greater opportunity for data entry errors to occur when information is
entered into the system from a paper form as opposed to direct entry
into the system during the client interview. [^19] Three of the four
states that use direct entry as the sole or primary referral method (see
section below entitled *Technical Solutions for Improving Data Quality)*
did not report data entry error problems.

Participants highlighted several different root causes for incomplete
data collection. Insufficient time was frequently cited as a factor.
Local district TANF staff in particular are often under pressure to meet
all the data collection requirements for eligibility determination and
run out of time for thorough IV-D data collection. Louisiana remarked
that there are multiple demands on IV-A workers, and subsequently they
do not always give IV-D data collection priority. Another cause closely
related to deficient time is insufficient staff resources. Nebraska
staff, for example, stated the opinion that the biggest hurdle regarding
quality data collection in their state seems to be time and resource
constraints. This problem can be particularly acute in offices located
in the larger urban centers or jurisdictions where there tends to be a
higher staff turnover rate. Inadequate staffing levels normally result
in larger caseloads per caseworker thereby reducing time available per
case.

A common factor often repeated by participants suggests that TANF staff
simply do not understand why child support data is collected and how it
is used, or why it is their responsibility to collect it. Florida
remarked that TANF staff do not consider the collection of information
and data for child support purposes as a IV-A activity. Pennsylvania
observed that most TANF staff do not receive any training about the
interface, PACSES (the IV-D system), or child support in general and
that many times IV-A staff simply do not understand the importance of
some of the information in terms of being able to process a referral.
Sometimes they do not know they are responsible for collecting some of
the data required for case processing. Pennsylvania IV-D instituted a
training program for TANF staff that has helped to reduce the error
rate. Louisiana suggested that more training that explains the benefits
of IV-D data collection and how the data collected from the IV-A
applicant is used is required to improve data quality. They are
currently developing materials for this purpose.

A few states cited technical issues that affected data quality. Maryland
staff suggested that the clearance process problem in their state could
be improved if the CARES (IV-A module) indexing function used by TANF
staff to detect if an absent parent is known to the system was upgraded.
Louisiana reported that, in the past, system modifications to the IV-A
system (L'AMI) have caused problems with some of the data exchanged
between the programs. Child support did not become aware of the problem
until months after the system change.

Washington IV-D found, during the redesign of its referral and interface
functionality, that minor technical issues had to be resolved including
data definitions, file upload logistics, and document imaging. IV-A
scans important documents that are used by IV-D staff; IV-A and IV-D use
different imaging technologies, so access rights had to be considered
and resolved.

Finally, potential for coding errors exists if the system does not
adequately support code definition lookup functionality. Pennsylvania
explained that the IV-A system, CIS, has field level help, but not for
all fields and an error message is provided only if an invalid code is
used, not necessarily an incorrect code. And although CIS provides
lookup capability for codes, it only provides a definition of the code
and not a description. Tools are available to help staff find a more
thorough description but they involve jumping out of the application and
going to an online manual, etc.

### Technical Solutions for Improving Data Quality {#technical-solutions-for-improving-data-quality .unnumbered}

States identified a number of significant factors that hinder the
exchange of quality data and cited some of the causes for them. But what
about solutions that have been implemented or are in the process of
being developed to ensure that an acceptable level of data quality is
maintained?

Participants talked about technically-based solutions that their states
have employed to negate the affects of these factors. A variety of
approaches have been designed and implemented to counteract the issues
previously described. The particular approach that any single state
adopts will be influenced by circumstances such as the nature of the
problem, resources, time, and urgency. For instance, sometimes a
technical approach is favored, sometimes a training-based method is
preferred, and other times the process is reviewed and modified. Some of
the solutions that individual states have initiated are described in the
paragraphs below.

Multiple approaches have been devised by states to improve data
collection and entry. Several states talked about the negative results
that occur when the clearance process fails to detect that an applicant
is known to the IV-A system. Nebraska stated that N-FOCUS, the IV-A
system, incorporates resolution functionality that assists TANF workers
to determine if the applicant already exists in the system. Nevada IV-D
discussed that it is currently in the process of creating a new person
search / person resolution function to improve clearance process
results. Wisconsin IV-A is also in the process of designing new client
lookup functionality in CARES (the IV-A system); the goal is to improve
staff ability to determine if the applicant is known to the system.

Rhode Island developed a potential duplicate case report that is issued
monthly. The report identifies cases where non-custodial parents who
have more than one social security number associated with the same
custodial parent, have last names that are phonetically the same, have
first initials that are equal and are the same sex. Systems staff then
merge the cases or make corrections as needed. The system also
automatically pops up a potential match screen when the name and other
identifying information potentially matches with someone already in the
system (both on the TANF/Medicaid/Child Care side and on the IV-D side).

Reducing the reliance on a dual system of paper and electronic referrals
may help improve data collection and shrink data entry errors.
Washington redesigned its referral module and ultimately eliminated
paper forms because the data quality of the electronic method met or
surpassed the paper method. For a period of six months after
implementing the E-Referral module, IV-D technical staff compared the
data quality of the paper form with the e-referral. It was discovered
that, over time, approximately 65% of the e-referrals provided as good
or better data than the paper referrals, so it was decided that paper
was no longer required. IV-A and IV-D continue to explore strategies to
improve the quality of data further.

When Florida implemented its integrated system in 1991, the use of paper
referrals was discontinued. Currently, IV-D data is entered during the
TANF interactive interview with the applicant. In Nebraska, TANF staff
also have the option of collecting data from the applicant using an
interactive interview that involves direct data entry into N-FOCUS or by
recording information on paper forms and data entering later; the method
of data entry used varies by location. Wisconsin IV-D reports that the
referral process is almost exclusively electronic; paper referrals are
almost never used. IV-A staff collect information from the applicant and
enter the data directly into CARES.[^20]

Pennsylvania IV-D, in an effort to reduce coding-related errors,
revisited the set of codes in use and discovered that there were
duplicates in terms of the "net effect" (i.e., an indicator designating
if the non-custodial parent is present in the household). They were able
to reduce the number of codes that workers need to use since IV-D was
only concerned about non-custodial parent household status indicator.
Louisiana, using a similar approach with the state's Medicaid agency,
reduced the number of codes used to indicate the child's legal status.

Montana is currently working on a system enhancement to TEAMS (the IV-A
system) that will address the issue concerning inaccurate or miscoded
information. After the enhancement is implemented, TEAMS will verify the
accuracy of case codes based on other information that is collected from
the client.

Florida CSE has developed a batch program that compares the IV-D case
type to the status of the public assistance case. For instance, if the
CSE case is coded as a non-TANF case, the system will search the public
assistance databases and determine if there was a TANF grant during a
particular month. If a discrepancy is found, the system will
automatically change the IV-D case type to a TANF case type.

INRHODES, the integrated TANF and CSE system used in Rhode Island, was
designed with online help for data fields. The help system provides
information about the data field and also about available codes if
applicable. Furthermore, both IV-A and IV-D staff have access to policy
information at any time online.

Some of the participants talked about system or process redesign efforts
that were previously implemented or are being considered. Last year,
Washington implemented a redesign of the electronic referral interface
between the IV-A and IV-D systems. The primary reason for this redesign
was that the IV-A system, ACES, came from another state whose program
requirements differed from those in Washington. ACES had to be
significantly modified to encompass Washington's IV-A program, including
the referral module to the IV-D agency. For example, many of the data
fields in ACES were not mandatory nor even used by the IV-D system,
SEMS, so there would be no place to download the data. Furthermore, the
number of ACES screens and data fields for the IV-D referral that
existed at the time created a significant workload issue. In the end,
the referral redesign reduced the fields from multiple screens to one
screen including the federally mandated fields as specified in OCSE AT
89-09.

Florida reported future plans for a project that will automate up-front
applicant IV-D cooperation determination at the time of the public
assistance interview. Child support hopes to implement a checklist that
the TANF worker will use while collecting information from the applicant
about the non-custodial parent. The enhancement would also permit IV-D
to automatically update the client cooperation indicator on the TANF
screen.

Nebraska is in the process of making enhancements to its IV-A to IV-D
referral. One new feature will include a pop-up window pertaining to the
non-custodial parent address that is triggered when the non-custodial
parent is added to the IV-A system, N-FOCUS, due to his/her role in the
IV-D referral. Data entry will not be mandatory. However, it is hoped
that the window will increase the chance that the worker will provide
the information if it is known. They are also adding a text field so the
worker can add the names of other alleged fathers or other pertinent
data to alert the IV-D worker to watch for the required paperwork.
Nebraska also remarked that other potential enhancements to the N-FOCUS
system would facilitate the TANF worker's ability to collect and record
information, such as modifications to screen flows or the sequence in
which data is collected on an individual screen.

In regards to the problem of minimal data collection, some states
discussed interesting technical approaches that potentially offer
relief. In Maryland, the IV-A system CARES is designed so that TANF
staff are required to data enter a minimum set of IV-D-related
information for the electronic referral to be generated and the TANF
eligibility determination process to proceed.

Similarly, Montana IV-D is currently working on an enhancement to TEAMS
with IV-A technical staff that will address the issue of missing data by
requiring that the IV-A worker enter a minimum set of IV-D data prior to
TANF benefit issuance. If some or all of the data is missing, benefits
cannot be issued, although eligibility determination can still be
conducted.

When the referral process involves both the electronic and paper form
method, problems can arise if, for any individual case, there isn't a
corresponding paper form for the electronic referral, and vice versa.
Several states have developed methods to either better understand the
scope of the problem or safeguard against it.

Pennsylvania IV-D developed a database that tracked all referrals coming
from IV-A. After being logged in the database, the IV-D staff would
attempt to process each referral in PACSES. The disposition of each
referral logged in the database was accounted for. Referrals that were
processed successfully were noted in the database. Referrals that were
not successfully processed were marked and the problem noted. Some
referrals in the database were deleted, but only after being approved by
the Commonwealth PACSES staff .

In Nebraska, the N-FOCUS system creates comparison reports that identify
active TANF cases without a IV-D referral to ensure that there is a
corresponding referral for each applicable TANF case. IV-A sends an
alert to the TANF worker to remind him/her if the IV-D referral has not
been sent. And the Washington e-referral daily file is brought into a
web-based interim system that IV-D staff use to manually cross reference
and modify existing cases in SEMS or to create new cases.

### Training Solutions for Improving Data Quality {#training-solutions-for-improving-data-quality .unnumbered}

Some states have implemented a training approach that targets data
collection and entry staff instead of, or in addition to, incorporating
a technical solution. Pennsylvania IV-D developed a training program for
the County Assistance Offices Income Maintenance staff in an effort to
reduce errors. The training includes a PowerPoint presentation and Word
document that provide information about the interface, the data being
collected, and the reason for the data collection (i.e., how it helps
the IV-D process). The training also includes a real-time, hands-on
demonstration with PACSES so that TANF workers can get a feel for the
system.

Florida believes that training IV-A staff on the importance of
collecting and entering correct information is critical. They offer
training for staff in both IV-A and IV-D programs to reaffirm the
importance of entering correct data into the system the first time and
every time. Washington IV-D technical staff developed a training class,
in conjunction with IV-A training staff, that targets specific offices
or staff that experience data quality issues. Training has been targeted
for both IV-A and IV-D staff regarding issues such as Good
Cause/Domestic Violence and how to code this, and the appropriate
actions to take on cases where these issues are in play.

Training can also be a useful tool for increasing awareness about the
importance of data collection efforts. Pennsylvania noted that sometimes
IV-A staff do not know they are responsible for collecting some of the
data required for IV-D case processing, and remarked that the training
program has helped to reduce the error rate. Nebraska acknowledged that
IV-A staff in some jurisdictions would probably improve on their data
collection efforts if they receive additional training regarding how
IV-D data is used and explanations regarding coding, etc. As mentioned
previously, Florida continues to provide training that stresses the
importance of data collection. And Rhode Island IV-D has provided
training for its staff that addresses, for example, why paternity data
is needed; the offer to train TANF and Medicaid staff on the topic has
been extended to program officials.

## Collaboration

One of the participants in the study offered the opinion that the
requirements mandated in PRWORA were in fact a catalyst for greater
collaboration between the programs in his state. He explained that
PRWORA had a significant impact on the need for communication between
IV-D and IV-A, as well as with other departments. Issues such as data
reliability, medical support, and the TANF five-year maximum, as well as
better defined regulations regarding cooperation, good cause, domestic
violence, and data sharing necessitated a higher level of communication
than what existed at the time.

The topic of TANF and CSE collaboration routinely involves discussion
citing strategies and methods that have been implemented to facilitate
interaction between program staff at the state or local level, or
interaction between the caseworker and client. Many papers and
conferences highlight strategies that states have implemented to improve
program collaboration such as co-locating staff at local district sites,
cross-training staff, creating coordinator positions, etc. When thinking
about collaboration in the context of information technology, a couple
of topics come to mind. First, how do technical staff from each program
collaborate to ensure that the software development life cycles are
synchronized and that programmatic needs are adequately supported?
Second, what technical strategies have been adopted to facilitate and
enhance the sharing of information between programs?

### Technical Staff Collaboration Practices {#technical-staff-collaboration-practices .unnumbered}

All ten states reported that staff from both programs were involved in
the design and development of the integrated system or the interface
between separate systems. More than half of the states stated that
technical staff meet routinely to discuss IT problems concerning the
interface or other related technical issues. Sometimes program staff
participate in these technical meetings. All the states felt that
regularly scheduled contact improved communication and collaboration.

Montana commented that CSE and TANF technical staff hold regular
informational meetings where issues involving the interface and
collaboration are discussed and resolved. During the informational
meetings, the best solution to the problem is determined and
responsibility for managing the task is assigned. Wisconsin described a
similar communication process but with the addition of program
representatives. Technical and programmatic staff from both IV-D and
IV-A meet routinely to highlight changes in program requirements or
planned system upgrades. Wisconsin reported that the IV-A / IV-D Tech
Group meetings help to increase the level of planning and collaboration
between the two programs. Likewise, Florida IV-D technical staff conduct
regularly scheduled formal meetings with TANF program and systems staff
to discuss technical issues of interest to both programs.

Nebraska IV-A and IV-D conduct organized focus groups that meet
routinely, approximately once a month. During the focus groups, which
include local-district TANF and CSE staff, participants are asked to
identify problems and potential enhancements to either N-FOCUS or
CHARTS. Subsequently, ideas for improving the interface are generated
from these sessions.

Washington IV-D has designated \"IV-A Liaisons\" in each of the field
offices who serve as the local system experts. These staff help to
identify problems at the local level and do an assessment before passing
it along to headquarters IT staff. These liaisons also help to
disseminate information about problems or enhancements that are
proposed. Often these liaisons are involved in work groups for proposed
enhancements (doing a pilot in one of the offices before implementing
statewide).

Rhode Island thought that implementing an integrated system was the most
significant strategy that increased technical collaboration. As an
example, it commented that child support voluntarily provides all of its
new hire data to IV-A, as allowed through PRWORA regulations, to help
the TANF program combat welfare fraud. The exchange of data was easy to
facilitate because the systems are integrated and the required system
changes straightforward.

Maryland, which also uses an integrated system, reports several
interesting strategies that improve collaboration. Like other states,
IV-A and IV-D staff meet routinely to discuss changes to the Client
Information System (CIS). However, technical staff responsible for each
of the IV-A and IV-D modules in CIS are also co-located, thus fostering
day-to-day communication. Technical staff have also established an
interface workgroup that meets quarterly to discuss issues and
solutions. Maryland also commented that IV-D and IV-A programs provided
at least one representative to participate on CSES (the IV-D module) and
CARES (the IV-A module) design teams. Program representatives were
required to approve the system specifications before the development
phase could begin.

Maryland also utilized the services of a single vendor to design and
program both CARES and CSES. This provided for the opportunity for some
of the programming staff of the CARES module development team to also
participate in the CSES module team. And like Rhode Island, a single
vendor is utilized for system maintenance and upgrade services for CIS.
Finally, both IV-A and IV-D staff participate in any system maintenance
involving the electronic referral.

Finally, Florida is one of two states in the survey whose IV-A and IV-D
programs are organized within different agencies. Previously, the
programs were organized within a single agency. Florida commented that
communication and collaboration can sometimes be a challenge,
particularly if an issue affecting one program does not have the same
degree of importance for the other program. Rhode Island's programs were
also once organized within a single agency but now are separated. It
reported that initially, after the separation, collaboration was
hindered until lines of communication and operational collaboration
specifics were resolved in Memorandums of Agreement established between
the two agencies. Appendix E provides information on IV-D and IV-A
program organization for the ten states interviewed.

### Technical Solutions for Data Access {#technical-solutions-for-data-access .unnumbered}

The basic method of electronic data exchange is the system interface
required by the Family Support Act legislation and discussed in AT
89-09. The interface is just one of many requirements that has to be met
for FAMIS and CSE system certification. But other data exchange methods
exist, and most of the states interviewed reported that staff from each
program have some level of restricted, read-only access to the other's
data.

In states like Maryland or Florida, integrated IV-A / IV-D systems
provide access to screens used by the other program; entry is regulated
by some type of system security module that grants access rights based
on a number of factors. In Rhode Island, for example, access to various
screens and modules is limited on a need to know basis through various
system security measures (who the individual is, where he/she works,
his/her role in the system, etc). In Nevada, IV-A and IV-D currently
view the information in the integrated database using different screens,
each designed exclusively for use by a particular program. Nevada TANF
and CSE staff are in the process of jointly developing screens to which
both IV-A and IV-D will have access.

Several different methods are employed by program staff in states using
separate systems to view the other's data. In Nebraska, IV-A staff have
restricted access to CHARTS (the IV-D system) case data using interface
screens developed for N-FOCUS (the IV-A system) users as well as other
staff such as Clerks of the District Courts and Title Companies. In
Pennsylvania and Washington, IV-D staff are provided query access to the
TANF system data.[^21] In Montana, child support staff have read-only
access to TEAMS, the TANF system; IV-D workers can view nearly all of
the TEAMS screens. IV-D workers are granted access to the TANF system
based upon security guidelines and specifications. Once a IV-D position
is identified as needing access to IV-A data and has been granted
rights, the worker logs onto the mainframe system then selects the TEAMS
system from a customized menu.

In an interesting use of web-based technology, Pennsylvania discovered
that a portal developed to provide child support clients on-line access
to case-specific data also offers utility to local district TANF
workers. A limited number of local district TANF workers are granted
access rights to the IV-D system to ensure sufficient system response
time and because 80% of the IV-D caseload is non-TANF. However, TANF
clients periodically come in with an issue concerning child support, and
access to IV-D case information (such as financial information) is
required to resolve the issue. Limiting the number of staff with system
access presents a challenge when a client raises such an issue and staff
with access rights are not available, thus inhibiting the worker's
ability to respond adequately. To solve this dilemma, IV-D provided all
IV-A staff access to the web-based portal. TANF staff now use the
portal, known as the Child Support Web Site, to gain access to necessary
client information and are better able to respond to IV-D-related issues
in a more timely manner.

## Other Related Findings

### Distinctions Between Large and Small Jurisdictions {#distinctions-between-large-and-small-jurisdictions .unnumbered}

Counties with large populations, particularly those with sizeable urban
centers, tend to have bigger IT budgets. But does this mean they have
greater technical capacity and therefore more sophisticated data
collection or exchange mechanisms in place than other jurisdictions?
States were asked if any distinctions exist between larger jurisdictions
and smaller to medium size jurisdictions.

In terms of technical capability, none of the states indicated any
difference between large and small areas. Maryland reported that all
jurisdictions operate on the same statewide system. Montana stated that
it does not distinguish between large, medium, or small jurisdictions;
all jurisdictions are treated the same. Most states responded similarly.
However, a majority of states reported that significant operational
differences can exist.

Several participants commented that smaller and medium sized
jurisdictions tend to have less staff turnover which means that the
workers collecting data have greater experience and knowledge. Maryland
stated that there is greater turnover of TANF staff in larger
jurisdictions so there is less experience, on average, among workers as
compared to staff in smaller to medium size jurisdictions. As a result,
lack of experience can lead to greater errors (for example, when
coding). Furthermore, new staff do not receive training about the IV-D
program or the electronic referral. Therefore, a greater number of staff
in larger jurisdictions generally do not have a good understanding of
why they are collecting the data or the importance of the non-custodial
parent information for the child support process. Several other states
also mentioned that lack of training about IV-D hindered the data
collection process.

There also seems to be greater collaboration between IV-A and IV-D staff
in smaller jurisdictions. Florida reported that whenever TANF and CSE
offices are co-located in the smaller rural areas, there is more
interaction between the TANF and CSE staff. Caseworkers from TANF and
CSE tend to share case information and collaborate more on their
respective cases. This results in more accurate case data, which
ultimately results in correct and timely case handling.

Participants also pointed to the fact that staff in larger jurisdictions
tend to have bigger caseloads and therefore less time to spend with
individual clients, and that can hamper thorough data collection. One
state mentioned that there seemed to be more commitment among staff in
smaller jurisdictions. Another state expressed the belief that staff in
smaller localities were more consistent and accurate.

### Regulatory Requirements Concerning Data Collection and Sharing {#regulatory-requirements-concerning-data-collection-and-sharing .unnumbered}

Some states expressed comments or opinions related to data exchange
requirements during the interview. Two states, Wisconsin and Louisiana,
remarked that they have been unable to find legislation that specifies
the data elements that IV-D or IV-A are required to provide. Wisconsin
noted that staff have been unable to locate legislative, regulatory, or
system certification requirements concerning the data TANF agencies must
provide to IV-D.

Louisiana IV-D expressed particular interest in obtaining any
regulations specifying data sharing requirements because it is involved
in the analysis of a "One-Stop" concept that would implement an
integrated approach to the delivery of the state's primary human
services programs. One topic recently researched concerns the extent to
which programs can share data with each other. IV-D has been unable to
find any legislation that provides specific data elements that IV-A
requires to administer its program. The only data elements clearly
identified that have been found to date are specified in AT 89-09, the
certification guide, and those related to FPLS. Program officials also
mentioned that they have restrictions on sharing information obtained
from other state agencies and some Federal data sources. The information
shared with the IV-D agency is restricted and may be used only for the
purposes of establishing and enforcing support.

The legislative authority for child support can be found in 42 USC 652
Duties of the Secretary, which establish standards for State programs
and 42 USC 654(4), which outlines state Child Support Enforcement plan
requirements (Note: the requirements specifically reference TANF
referrals to child support).

Relevant regulations include 45 CFR 303.2(a)(2) & 3(b) which addresses
state AFDC/TANF cases referred to the state child support agency, 45 CFR
307.10(b)(14) which addresses the use of automated processes to assist
the state in meeting state plan amendments, and 45 CFR 302.85 addresses
mandatory computerized support enforcement systems.

Also available is the OCSE guideline entitled "Automated Systems for
Child Support Enforcement: A Guide for States".

Finally, OCSE, in its response to comments to the final rules on
computerized support enforcement systems, Federal Register August 21,
1998 (Volume 63, Page 44795-44817 ) provided a chart detailing who has
access to FPLS information, for what purposes, how to gain access, what
information is available and any exceptions. OCSE is currently
developing a notice of proposed rulemaking on data safeguarding
regulations for child support and the NPRM will also include an updated
chart of the authorized uses of child support data.

# Identifying Recommendations and Best Practices

The preceding sections of this report provide information about the data
shared between state TANF and CSE programs and highlight important
issues concerning the data collection and exchange processes. State
participants have shared valuable information about the challenges
they've confronted and technical solutions they've implemented to reduce
error rates and improve data quality.

In May 2004, a workgroup comprised of State-level TANF and Child Support
Enforcement representatives will convene to discuss the report findings
and make recommendations concerning Best Practices and next steps.
Identifying Best Practices is not an easy task and requires careful
discussion and reflection. For example, some consideration should be
given the fact that several factors influencing data collection that
states highlighted will most likely not disappear in the near future.
Staff turnover, a significant concern for larger jurisdictions, will
likely remain an issue. Although turnover rates may decrease during
economic downswings, the fact that social service positions are highly
stressful and on the lower end of the pay scale will continue to ensure
that staff turnover will be a factor.

Program resources, another factor, will most likely not increase
significantly in the future. In fact, programs will probably face the
threat of decreased resources if state budget deficits continue. High
worker caseload levels will also continue to be a factor until staff
turnover decreases and resources improve. And training programs,
although worthwhile, can be a drain on limited resources and
logistically challenging to administer. As a result, training may not
always be a viable option. Ultimately, workgroup members will be asked
to select ideas that have the best potential to improve, in the long
run, the data exchange process and achievement of state performance
measures after considering the factors highlighted in this report and
elsewhere. Some of the questions that the workgroup will be asked to
consider are identified here.

-   Do the data element requirements defined in AT 89-09 need to be
    modified? Given the feedback provided by program officials, the data
    elements identified in AT 89-09 seem to meet current IV-D program
    needs. Is more consideration needed or is AT89-09 satisfactory ?

```{=html}
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```
-   What level of communication between IV-A and IV-D technical and
    programmatic staff is best? An established forum to discuss
    programmatic changes and system modifications seems to foster
    synchronization between programs and synchronization between
    programmatic change and corresponding system modifications. Should
    States consider implementing focus groups involving local district
    staff similar to the Nebraska model previously mentioned?

-   What recommendations, if any, should be made regarding the use of
    training to improve data collection efforts and reducing error
    rates? Some states seem to focus on training, while others do not.
    Are there circumstances which clearly favor the use of training
    solutions, as opposed to technical solutions, to improve the data
    collection and sharing process?

```{=html}
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```
-   Should states consider utilizing methods that foster greater
    technical commonality between their IV-A and IV-D systems? For
    example, Maryland approached the development of its integrated
    system using a common vendor to design, build, and maintain the TANF
    and CSE modules. The ability of any state to implement this would
    seem to depend on a host of factors including procurement rules,
    existing levels of program collaboration, technical considerations,
    resources, etc. Can efficiencies be realized when greater
    commonality is achieved between systems?

-   Some states have moved toward an electronic-only referral format; is
    this a practice that other states should work toward? States will
    need to consider multiple factors like existing technical capacity
    and process design, and may require long-term planning because of
    resource and technical limitations. Benefits seem to include the
    reduction of a two-step process that fosters errors and greater
    system-based control of the data collection process. If both paper
    and electronic referral methods are used simultaneously, should
    states adopt quality controls like ones implemented by Nebraska,
    Washington, or Pennsylvania to ensure a one-to-one match?

-   What recommendations, if any, should be made regarding the ability
    of IV-A and IV-D staff to have access to the other's data?

-   How can the problem associated with miscoding be best addressed?
    Miscoding is a common occurrence in part because there are too many
    codes to manage. Should States consider the Pennsylvania approach of
    looking at the "net effect" to determine what codes are really
    necessary?

-   Would the amount of data collected by IV-A affect IV-D program
    performance? More data does not necessarily mean that program
    outputs and outcomes will improve. Wisconsin suggested that less
    data could be collected by IVA staff and program goals could still
    be achieved.

-   Do system controls have the potential to improve the data collection
    process? States such as Maryland and Montana are establishing a
    minimum level of data collection that suspends the TANF eligibility
    or benefit issuance process if the worker fails to enter all the
    required information. Should these methods be recommended to other
    states who are looking for ways to improve data collection results?

-   What recommendations should be made concerning the TANF clearance
    and certification process? Theoretically, both programs could
    benefit if technical improvements that increase the likelihood that
    person searches will be successful can be developed. States like
    Wisconsin and Nevada are currently designing and implementing
    solutions.

# Appendix

## ![](media/image1.wmf)Appendix A - DCL 03-28 document

![](media/image1.wmf)

## ![](media/image1.wmf)Appendix B - AT 89-09 document

![](media/image1.wmf)

![](media/image1.wmf)

## ![](media/image1.wmf)  {#section-1 .unnumbered}

## ![](media/image1.wmf)  {#section-2 .unnumbered}

## ![](media/image1.wmf)  {#section-3 .unnumbered}

## Appendix C - Interview Questions and Follow-up Questions

**[IV-A / IV-D Interface Interview Questions]{.underline}**

1.  What data in addition to the minimum data elements specified in AT
    89-09 do you exchange / share? (review data elements in 89-09 with
    participant)

2.  Is there CP or NCP data collected but not currently shared by IV-A
    that would enhance your ability to achieve program goals?

3.  Is there CP or NCP data not currently collected that would enhance
    your ability to achieve program goals?

4.  Are there regulatory restrictions that limit the data that can be
    shared between the programs?

5.  Can the quality of data exchanged with IV-A be improved?

6.  What impediments do you need to resolve in order to improve the
    quality of data exchanged with IV-A? Have you planned or enacted any
    [technical]{.underline} solutions to reduce or eliminate the effect
    of these impediments?

7.  What [technical]{.underline} solutions have you planned or enacted
    that improved the level of collaboration between IV-A and IV-D?

8.  Have the solutions you have implemented been successful? How?

9.  Have you implemented any technical solutions that have increased the
    number of IV-A families receiving child support or the amount of
    support that the IV-A families receive? Was the technical solution a
    result of programmatic change or process re-engineering? Has the
    solution reduced the number of IV-A cases?

10. Some of the problems previously identified with IV-D / IV-A data
    exchange include:

-   Inadequate training or on-site information for IV-A staff regarding
    why data is collected or the benefits of collaboration;

-   IV-A workers do not collect all the information needed from the CP
    regarding the NCP;

-   Incorrect coding.

Have you encountered any of these problems? Has your agency implemented
any technical solutions to reduce or eliminate their effect?

11. Did your agency design the IV-D system interface in collaboration
    with IV-A program staff?

> Or

Is your IV-D system integrated with the IV-A system? Did staff from both
programs collaborate on the design?

12. Are there any differences in the issues concerning IV-A and IV-D
    data exchange or collaboration between large jurisdictions and small
    jurisdictions in your state?

**[IV-A / IV-D Interface Interview Follow-Up Questions]{.underline}**

1\. Describe technical (I.e., Information Technology based) issues and
corresponding solutions implemented by your state that have improved
IV-A / IV-D data sharing and / or data quality. Types of technical
solutions might involve enhancements to hardware, software, software
development methods, etc. Some examples of improved data sharing / data
quality might include: real-time electronic notification of TANF status,
electronically updating NCP/CP contact information, system-guided data
collection, etc.

2\. Most states have cited non-technical (e.g., people oriented) issues
concerning IV-D data collected by large jurisdictions versus small to
medium jurisdictions. But what about the technical factors governing
data exchange - are there technical issues or solutions that distinguish
large jurisdictions from small to medium jurisdictions in your state?

3\. Do IV-D staff have some method of access to IV-A data (other than
the data elements cited in AT 89-09)? If yes, how do they access this
information?

4\. Can the quality of the IV-D-related data collected by IV-A be
improved? Explain.

5\. Are there any issues that arise for IV-D as a result of the IV-A
registration / clearance process?

6\. Estimate the portion of all TANF-related IV-D referrals received
during a typical month that involve CPs already known to the IV-A
system: Low (0% to 25%), Medium (26% to 50%), Medium High (51% to 75%),
High (76% to 100%) ?

7\. Estimate the portion of all TANF-related IV-D referrals received
during a typical month that involve NCPs already known to the IV-A
system: Low (0% to 25%), Medium (26% to 50%), Medium High (51% to 75%),
High (76% to 100%) ?

## Appendix D - Systems Overview by State

+-------+--------+-----------+----------+-----------------------------+
| **[   | *      | **[System | **[Im    | **[Technical                |
| State | *[Prog | N         | plemente | Sp                          |
| ]{.un | ram]{. | ame]{.und | d]{.unde | ecifications]{.underline}** |
| derli | underl | erline}** | rline}** |                             |
| ne}** | ine}** |           |          |                             |
+=======+========+===========+==========+=============================+
| *     | IV-D   | Florida   | 1991     | Type of Application:        |
| *Flor |        | On-Line   |          | Multiple Database           |
| ida** |        | Recipient |          |                             |
|       |        | I         |          | Type of Platform: Other     |
|       |        | ntegrated |          |                             |
|       |        | Data      |          | Hardware Platform: IBM      |
|       |        | Access    |          | System 390                  |
|       |        | system    |          |                             |
|       |        | (FLORIDA) |          | Operating System: OS390     |
|       |        |           |          |                             |
|       |        |           |          | Languages: COBOL; Telon;    |
|       |        |           |          | Assembler; Easytrieve       |
|       |        |           |          |                             |
|       |        |           |          | DBMS: IDMS                  |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | Florida   | 1991     | Type of Application:        |
|       |        | On-Line   |          | Multiple Database           |
|       |        | Recipient |          |                             |
|       |        | I         |          | Type of Platform: Other     |
|       |        | ntegrated |          | Hardware Platform: IBM      |
|       |        | Data      |          | System 390                  |
|       |        | Access    |          |                             |
|       |        | system    |          | Operating System: OS390     |
|       |        | (FLORIDA) |          |                             |
|       |        |           |          | Languages: COBOL; Telon;    |
|       |        |           |          | Assembler; Easytrieve       |
|       |        |           |          |                             |
|       |        |           |          | DBMS: IDMS                  |
+-------+--------+-----------+----------+-----------------------------+
| **L   | IV-D   | Louisiana | 1994     | Type of Application:        |
| ouisi |        | Automated |          | Multiple Database           |
| ana** |        | Support   |          |                             |
|       |        | En        |          | Type of Platform: Single    |
|       |        | forcement |          | Platform                    |
|       |        | System    |          |                             |
|       |        | (LASES)   |          | Hardware Platform: IBM --   |
|       |        |           |          | CMOS Processor              |
|       |        |           |          |                             |
|       |        |           |          | Operating System: OS/390    |
|       |        |           |          |                             |
|       |        |           |          | Languages: Natural          |
|       |        |           |          | (Software AG)               |
|       |        |           |          |                             |
|       |        |           |          | DBMS: ADABAS (Software AG)  |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | Louisiana | 1993     | Type of Application:        |
|       |        | Automated |          | Multiple Database           |
|       |        | M         |          |                             |
|       |        | anagement |          | Type of Platform: Single    |
|       |        | In        |          | Platform                    |
|       |        | formation |          |                             |
|       |        | (LAMI)    |          | Hardware Platform: IBM --   |
|       |        |           |          | CMOS Processor              |
|       |        |           |          |                             |
|       |        |           |          | Operating System: OS/390    |
|       |        |           |          |                             |
|       |        |           |          | Languages: Natural          |
|       |        |           |          | (Software AG)               |
|       |        |           |          |                             |
|       |        |           |          | DBMS: Adabas (Software AG)  |
+-------+--------+-----------+----------+-----------------------------+
| **    | IV-D   | Client    | 1994     | Type of Application: Single |
| Maryl |        | In        |          | Database                    |
| and** |        | formation |          |                             |
|       |        | System /  |          | Type of Platform:           |
|       |        | Child     |          | Distributed                 |
|       |        | Support   |          |                             |
|       |        | En        |          | Hardware Platform: IBM      |
|       |        | forcement |          | S(390) G5 Mainframe, 6 IBM  |
|       |        | System    |          | 2 way P660s                 |
|       |        | (CIS)     |          |                             |
|       |        |           |          | Operating System: OS/390,   |
|       |        |           |          | AIX Windows 2000, Linux,    |
|       |        |           |          | Novel, Websphere            |
|       |        |           |          |                             |
|       |        |           |          | Languages: COBOL, J2EE,     |
|       |        |           |          | Perl, REXX                  |
|       |        |           |          |                             |
|       |        |           |          | DBMS: DB2, UDB, Access      |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | Client    | 1994     | Type of Application: Single |
|       |        | In        |          | Database                    |
|       |        | formation |          |                             |
|       |        | System /  |          | Type of Platform: Single    |
|       |        | Client    |          | Platform                    |
|       |        | Automated |          |                             |
|       |        | Resource  |          | Hardware Platform: IBM      |
|       |        | and       |          | S(390) G5 Mainframe, 6 IBM  |
|       |        | El        |          | 2 way P660s                 |
|       |        | igibility |          |                             |
|       |        | System    |          | Operating System: OS/390,   |
|       |        | (CIS)     |          | AIX Windows 2000, Linux,    |
|       |        |           |          | Novel, Websphere            |
|       |        |           |          |                             |
|       |        |           |          | Languages: COBOL, J2EE,     |
|       |        |           |          | PERL, REXX                  |
|       |        |           |          |                             |
|       |        |           |          | DBMS: DB2, UDB, Access      |
+-------+--------+-----------+----------+-----------------------------+
| *     | IV-D   | System    | 1993     | Type of Application: Single |
| *Mont |        | for the   |          | Database                    |
| ana** |        | En        |          |                             |
|       |        | forcement |          | Type of Platform: Client    |
|       |        | and       |          | Server                      |
|       |        | Recovery  |          |                             |
|       |        | of Child  |          | Hardware Platform:          |
|       |        | Support   |          | MVS/OS-operating platform   |
|       |        | (SEARCHS) |          | MVS COBOL-language          |
|       |        |           |          |                             |
|       |        |           |          | Operating System: Mainframe |
|       |        |           |          | 3270                        |
|       |        |           |          |                             |
|       |        |           |          | Languages: COBOL, CULPRIT   |
|       |        |           |          |                             |
|       |        |           |          | DBMS: IDMS                  |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | The       | 1991     | Type of Application: Single |
|       |        | Economic  |          | Database                    |
|       |        | A         |          |                             |
|       |        | ssistance |          | Type of Platform: Client    |
|       |        | M         |          | Server                      |
|       |        | anagement |          |                             |
|       |        | System    |          | Hardware Platform: IBM      |
|       |        | (TEAMS)   |          |                             |
|       |        |           |          | Operating System: Mainframe |
|       |        |           |          | 3270                        |
|       |        |           |          |                             |
|       |        |           |          | Languages: COBOL, CULPRIT   |
|       |        |           |          |                             |
|       |        |           |          | DBMS: IDMS                  |
+-------+--------+-----------+----------+-----------------------------+
| Neb   | IV-D   | Children  | 1998     | Type of Application: Single |
| raska |        | Have A    |          | Database                    |
|       |        | Right To  |          |                             |
|       |        | Support   |          | Type of Platform: Client    |
|       |        | (CHARTS)  |          | Server                      |
|       |        |           |          |                             |
|       |        |           |          | Hardware Platform: 9672 R   |
|       |        |           |          | 36 IBM processors           |
|       |        |           |          |                             |
|       |        |           |          | Operating System: NT 4.0 -- |
|       |        |           |          | client Windows 2000 servers |
|       |        |           |          |                             |
|       |        |           |          | Languages: PowerBuilder -   |
|       |        |           |          | On-line Visual Basic -      |
|       |        |           |          | On-line COBOL - batch       |
|       |        |           |          |                             |
|       |        |           |          | DBMS: DB2 - EnterpriseDB2   |
|       |        |           |          | v. 6 OX/390Client Server -  |
|       |        |           |          | DB2 v. 5.2 (in field)       |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | Nebraska  | 1996     | Type of Application: Single |
|       |        | Family    |          | Database                    |
|       |        | Online    |          |                             |
|       |        | Client    |          | Type of Platform: Client    |
|       |        | User      |          | Server                      |
|       |        | System    |          |                             |
|       |        | (N-FOCUS) |          | Hardware Platform: IBM      |
|       |        |           |          |                             |
|       |        |           |          | Operating System: OS390     |
|       |        |           |          | CICS                        |
|       |        |           |          |                             |
|       |        |           |          | Languages: COBOL;           |
|       |        |           |          | Assembler; C; Java (some    |
|       |        |           |          | online code generated by CA |
|       |        |           |          | COOL:Gen and CA AION tools) |
|       |        |           |          |                             |
|       |        |           |          | DBMS: DB2                   |
+-------+--------+-----------+----------+-----------------------------+
| N     | IV-D   | Nevada    | 1999     | Type of Application: Single |
| evada |        | O         | (CSE     | Database                    |
|       |        | perations | non-TANF |                             |
|       |        | of        | data     | Type of Platform: Single    |
|       |        | Multi-    | entry)   | Platform                    |
|       |        | Automated |          |                             |
|       |        | Data      |          | Hardware Platform:          |
|       |        | Systems   |          | Mainframe                   |
|       |        | (NOMADS)  |          |                             |
|       |        |           |          | Operating System: OS/390    |
|       |        | Note: NV  |          |                             |
|       |        | adopted   |          | Languages: CSP              |
|       |        | INRHODES  |          |                             |
|       |        | and       |          | DBMS: DB2                   |
|       |        | rewrote   |          |                             |
|       |        | the code  |          |                             |
|       |        | using a   |          |                             |
|       |        | different |          |                             |
|       |        | language  |          |                             |
|       |        | (CSP).    |          |                             |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | Nevada    | 2000     | Type of Application: Single |
|       |        | O         |          | Database                    |
|       |        | perations |          |                             |
|       |        | of        |          | Type of Platform: Single    |
|       |        | Multi-    |          | Platform                    |
|       |        | Automated |          |                             |
|       |        | Data      |          | Hardware Platform:          |
|       |        | Systems   |          | Mainframe                   |
|       |        | (NOMADS)  |          |                             |
|       |        |           |          | Operating System: OS/390    |
|       |        |           |          |                             |
|       |        |           |          | Languages: CSP              |
|       |        |           |          |                             |
|       |        |           |          | DBMS: DB2                   |
+-------+--------+-----------+----------+-----------------------------+
| Pe    | IV-D   | Pen       | 1999     | Type of Application:        |
| nnsyl |        | nsylvania |          | Multiple Database           |
| vania |        | Automated |          |                             |
|       |        | Child     |          | Type of Platform: Single    |
|       |        | Support   |          | Platform                    |
|       |        | En        |          |                             |
|       |        | forcement |          | Hardware Platform: Unisys   |
|       |        | System    |          | Mainframe WAN/LAN           |
|       |        | (PACSES)  |          |                             |
|       |        |           |          | Operating System: OS-2000;  |
|       |        |           |          | Windows NT ; Windows 2000XP |
|       |        |           |          | 5.0 SP.3                    |
|       |        |           |          |                             |
|       |        |           |          | Languages: ACOB -- ASCII    |
|       |        |           |          | COBOL                       |
|       |        |           |          |                             |
|       |        |           |          | DBMS: UDSC; DMS-2200;       |
|       |        |           |          | RDMS-2200; UREP, IRU        |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | Client    | 1992     | Type of Application:        |
|       |        | In        |          | Multiple Database           |
|       |        | formation |          |                             |
|       |        | System    |          | Type of Platform: Single    |
|       |        | (CIS)     |          | Platform                    |
|       |        |           |          |                             |
|       |        |           |          | Hardware Platform: UNISYS   |
|       |        |           |          | Clear Path Enterprise       |
|       |        |           |          | Server                      |
|       |        |           |          |                             |
|       |        |           |          | Operating System: OS 2200   |
|       |        |           |          |                             |
|       |        |           |          | Languages: COBOL            |
|       |        |           |          |                             |
|       |        |           |          | DBMS: Universal Data System |
|       |        |           |          | (UDS)                       |
+-------+--------+-----------+----------+-----------------------------+
| Rhode | IV-D   | I         | 1991     | Type of Application: Single |
| I     |        | ntegrated |          | Database                    |
| sland |        | Rhode     |          |                             |
|       |        | Island    |          | Type of Platform: Single    |
|       |        | System    |          | Platform                    |
|       |        | (         |          |                             |
|       |        | INRHODES) |          | Hardware Platform: IBM      |
|       |        |           |          |                             |
|       |        |           |          | Operating System: OS/390;   |
|       |        |           |          | VTAM; TSO; JES2; NCP        |
|       |        |           |          |                             |
|       |        |           |          | Languages: Natural; CICS;   |
|       |        |           |          | CICS COBOL                  |
|       |        |           |          |                             |
|       |        |           |          | DBMS: ADABAS                |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | I         |          | Type of Application: Single |
|       |        | ntegrated |          | Database                    |
|       |        | Rhode     |          |                             |
|       |        | Island    |          | Type of Platform: Single    |
|       |        | System    |          | Platform                    |
|       |        | (         |          |                             |
|       |        | INRHODES) |          | Hardware Platform: IBM      |
|       |        |           |          |                             |
|       |        |           |          | Operating System: OS/390;   |
|       |        |           |          | VTAM; TSO; JES2; NCP        |
|       |        |           |          |                             |
|       |        |           |          | Languages: Natural; CICS;   |
|       |        |           |          | CICS COBOL                  |
|       |        |           |          |                             |
|       |        |           |          | DBMS: ADABAS                |
+-------+--------+-----------+----------+-----------------------------+
| Washi | IV-D   | Support   |          | Type of Application:        |
| ngton |        | En        |          | Multiple database           |
|       |        | forcement |          |                             |
|       |        | M         |          | Type of Platform: Multiple  |
|       |        | anagement |          | Platform                    |
|       |        | System    |          |                             |
|       |        | (SEMS)    |          | Hardware Platform: UNISYS   |
|       |        |           |          | Mainframe; File, web, and   |
|       |        |           |          | database servers.           |
|       |        |           |          |                             |
|       |        |           |          | Operating System: OS 2200,  |
|       |        |           |          | Win2000                     |
|       |        |           |          |                             |
|       |        |           |          | Languages: COBOL, Visual    |
|       |        |           |          | Basic                       |
|       |        |           |          |                             |
|       |        |           |          | DBMS: DMS 1100, SQL         |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | Automated | 1997     | Type of Application:        |
|       |        | Client    |          | Multiple Database           |
|       |        | El        |          |                             |
|       |        | igibility |          | Type of Platform: Single    |
|       |        | System    |          | Platform                    |
|       |        | (ACES)    |          |                             |
|       |        |           |          | Hardware Platform:          |
|       |        |           |          | Mainframe and Server        |
|       |        |           |          |                             |
|       |        |           |          | Operating System: OS 390;   |
|       |        |           |          | MS Windows NT; MS Windows   |
|       |        |           |          | 2000; IBM Websphere         |
|       |        |           |          |                             |
|       |        |           |          | Languages: Cobol; Java;     |
|       |        |           |          | Java script; DYL280;        |
|       |        |           |          | Assembler                   |
|       |        |           |          |                             |
|       |        |           |          | DBMS: IMS; DB2 OS/390; DB2  |
|       |        |           |          | UDB                         |
+-------+--------+-----------+----------+-----------------------------+
| Wisc  | IV-D   | Kids      | 1996     | Type of Application:        |
| onsin |        | In        |          | Multiple Database           |
|       |        | formation |          |                             |
|       |        | Data      |          | Type of Platform: Single    |
|       |        | System    |          | Platform                    |
|       |        | (KIDS)    |          |                             |
|       |        |           |          | Hardware Platform:          |
|       |        |           |          | Mainframe CPU-Amdahl        |
|       |        |           |          | G52098E                     |
|       |        |           |          |                             |
|       |        |           |          | Operating System: OS/390;   |
|       |        |           |          | CICS                        |
|       |        |           |          |                             |
|       |        |           |          | Languages: Cobol for MVS;   |
|       |        |           |          | SAS                         |
|       |        |           |          |                             |
|       |        |           |          | DBMS: DB2                   |
+-------+--------+-----------+----------+-----------------------------+
|       | IV-A   | Client    | 1994     | Type of Application: Single |
|       |        | A         |          | Database                    |
|       |        | ssistance |          |                             |
|       |        | for       |          | Type of Platform: Single    |
|       |        | Re-e      |          | Platform                    |
|       |        | mployment |          |                             |
|       |        | &         |          | Hardware Platform: Amdahl   |
|       |        | Economic  |          |                             |
|       |        | Support   |          | Operating System: OS/390    |
|       |        | (CARES)   |          |                             |
|       |        |           |          | Languages: IMS; TSO; EOS;   |
|       |        |           |          | COBOL 2; VTAM; SAS; RACF;   |
|       |        |           |          | MVS; Netview FTP; Finalist; |
|       |        |           |          | Quick Start; CICS; Network  |
|       |        |           |          | Data Mover                  |
|       |        |           |          |                             |
|       |        |           |          | DBMS: DB2                   |
+-------+--------+-----------+----------+-----------------------------+

## Appendix E - State Agency Program Organization

+------------+-----------+---------------------------------------------+
| **[        | **[Prog   | **[Agency Organized Within]{.underline}**   |
| State]{.un | ram]{.und |                                             |
| derline}** | erline}** |                                             |
+------------+-----------+---------------------------------------------+
| Florida    | IV-D      | Department of Revenue                       |
|            |           |                                             |
|            | IV-A      | Department of Children and Families         |
+------------+-----------+---------------------------------------------+
| Louisiana  | IV-D      | Department of Social Services, Office of    |
|            |           | Family Support                              |
|            | IV-A      |                                             |
|            |           | Department of Social Services, Office of    |
|            |           | Family Support                              |
+------------+-----------+---------------------------------------------+
| Maryland   | IV-D      | Department of Human Resources               |
|            |           |                                             |
|            | IV-A      | Department of Human Resources               |
+------------+-----------+---------------------------------------------+
| Montana    | IV-D      | Department of Public Health & Human         |
|            |           | Services                                    |
|            | IV-A      |                                             |
|            |           | Department of Public Health & Human         |
|            |           | Services                                    |
+------------+-----------+---------------------------------------------+
| Nebraska   | IV-D      | Health & Human Services                     |
|            |           |                                             |
|            | IV-A      | Health & Human Services                     |
+------------+-----------+---------------------------------------------+
| Nevada     | IV-D      | Department of Human Resources               |
|            |           |                                             |
|            | IV-A      | Department of Human Resources               |
+------------+-----------+---------------------------------------------+
| Pe         | IV-D      | Department of Public Welfare                |
| nnsylvania |           |                                             |
|            | IV-A      | Department of Public Welfare                |
+------------+-----------+---------------------------------------------+
| Rhode      | IV-D      | Department of Administration, Division of   |
| Island     |           | Taxation                                    |
|            | IV-A      |                                             |
|            |           | Department of Human Services                |
+------------+-----------+---------------------------------------------+
| Washington | IV-D      | Department of Social and Health Services    |
|            |           |                                             |
|            | IV-A      | Department of Social and Health Services    |
+------------+-----------+---------------------------------------------+
| Wisconsin  | IV-D      | Department of Workforce Development         |
|            |           |                                             |
|            | IV-A      | Department of Workforce Development         |
+------------+-----------+---------------------------------------------+

[^1]: OCSE-DCL-03-28. See Appendix A for the complete text.

[^2]: The five state performance measures are: Paternity Establishment;
    Support Order Establishment; Current Collections; Arrearage
    Collections; and Cost Effectiveness. Refer to 45 CFR Ch. III, Part
    305.2 Performance Measures (Federal Register, 10-1-2001 Edition) for
    more information.

[^3]: Action Transmittal OCSE-AT-89-09 *Revised Minimum Data Elements
    Required for Certifiable FAMIS and CSE Systems Interface* was used
    as the baseline for this analysis. Refer to Appendix B for the
    complete text.

[^4]: Farrel, Mary, Asaph Glosser, and Karen Gardiner. *Child Support
    and TANF Interaction: Literature Review*. The Lewin Group, under
    subcontract to Manpower Demonstration Research Corporation, for the
    U.S. Department of Health and Human Services, Office of the
    Assistant Secretary for Planning and Evaluation, April 11, 2003

[^5]: Turestsky, Vicki. *Child Support Computer Systems: A Summary of
    Current and Proposed Federal Requirements.* Center for Law and
    Social Policy. August 1996.

[^6]: Ibid.

[^7]: States and territories invited to participate in the survey
    include Florida, Louisiana, Maryland, Montana, Nebraska, Nevada, New
    York, Pennsylvania, Puerto Rico, Rhode Island, Washington, and
    Wisconsin.

[^8]: Participating states using an integrated database system include
    Florida, Maryland, Nevada, and Rhode Island.

[^9]: Refer to Appendix C for the interview questions and follow-up
    questions.

[^10]: Refer to <http://www.acf.hhs.gov/nhsitrc/> for more information.

[^11]: Eight states provided information regarding data elements by the
    time this report was prepared.

[^12]: Data refers to either a single element such as social security
    number, or it can refer to a group of like elements such as "name"
    which is comprised of first name, middle initial, last name data
    elements. Data elements used for records management or to establish
    relationships in a household were not included in this study.
    Examples of such elements might include sequence number, case
    identification number, member number, etc.

[^13]: All six states in this group provided information about the data
    elements that are uploaded from the IV-A system to the IV-D system.
    Only two states provided information about data elements that are
    uploaded from the IV-D system to the IV-A system.

[^14]: States in this group did not exchange many of the data elements
    classified as support order elements.

[^15]: The term "quality" refers to data accuracy and completeness, and
    how current the data is. These characteristics were extracted from
    the definition of "reliable data" defined in 45 CFR Ch. III, Part
    305.1 Definitions (Federal Register 10-1-2001 Edition).

[^16]: Data maintenance refers to the process of updating the database
    with the most current information available.

[^17]: During TANF application processing, a search is conducted in the
    IV-A database to determine if the applicant is known to the system
    (i.e., finding system records associated with the applicant). Some
    states will also check the database for records related to the
    non-custodial parent. Search criteria normally includes social
    security number, last name, and / or first name. The activity is
    generally known as the clearance process.

[^18]: Coding refers to the IV-D status assigned to the case by the TANF
    worker; it is important because it often triggers the IV-D process.
    For example, a specific code may indicate there is an absent parent
    and cause an electronic referral to be generated.

[^19]: Statistics supporting this statement were not available. However,
    it is reasonable to expect that there is less opportunity for entry
    errors to occur when the recording process has one step (i.e.,
    directly into the system) as opposed to two steps (i.e., record data
    on paper, then into the system).

[^20]: Note that some states, Montana for instance, have discussed
    eliminating the paper referral but cannot because of legal and
    process barriers preventing it from happening. In Montana's case,
    the paper referral is a requirement because it records the client's
    signature verifying agreement to assign their rights to all support
    collected from the non-custodial parent to the state.

[^21]: Note that select IV-D staff in Pennsylvania have query access to
    the TANF system.