5/25/00 admnacs.doc

**MEETING STATE AND COMMUNITY NEEDS FOR**

**SOCIAL, ECONOMIC, AND HOUSING**

**INFORMATION:**

**The Why, What, and How of**

**Converting Program Records and Summarized Survey Data to State and
Community Information Systems**

# Cynthia M. Taeuber

# Jacob France Center, University of Baltimore

######### Bureau of the Census

# 

# Julia Lane

# Urban Institute 

# 

# David Stevens

# Jacob France Center, University of Baltimore

# 

# 

# Background prepared paper for:

# 

# Conference on "Developing Public Policy Applications with 

# Summarized Survey Data and Community Administrative Records"

**June 6-7, 2000**

**MEETING STATE AND COMMUNITY NEEDS FOR**

**SOCIAL, ECONOMIC, AND HOUSING INFORMATION:**

**The Why, What, and How of**

**Converting Program Records and Summarized Survey Data to**

**State and Community Information Systems**

## Cynthia M. Taeuber Julia Lane David Stevens

##### University of Baltimore Urban Institute University of Baltimore

**Census Bureau**

# 

### **Abstract**

*This paper addresses the why, what, and how of creating modern state
and community information systems, that is, multiple data sets that are
geographically based, to better inform those who make decisions about
program effectiveness and direction. Some states and communities have
developed their Management Information Systems (MIS) for secondary use
as a longitudinal data base for statistical analyses of the overall
characteristics of groups of clients, the effects of client groups on
other public services, and to evaluate the effectiveness (performance)
of program policies. A new information resource profiles the population
and housing of communities. That is the American Community Survey. It is
a new survey the Census Bureau is developing to provide demographic,
social, economic, and housing profiles of areas every year, eventually
down to the neighborhood level.*

*The dynamic picture multiple information sources provide can help
communities better understand the interactions between changes in a
community's population and other factors such as its industrial mix,
economic development potential, health profile, and physical
environment. With multiple sources of information, communities can move
beyond program administration and performance measures to strategic
decisionmaking. Communication of research results to those who make
decisions about community policies can improve through new technology
such as the layered mapping of Geographic Information Systems (GIS).*

*This paper describes administrative records as a source for statistical
analyses, the new American Community Survey, and other information
sources that describe a community's situation compared with other areas.
Using current information about population and housing trends from the
American Community Survey, we illustrate how administrative records
could be merged in econometric models with these summarized survey data
to provide improved estimates and probability statements of events. This
system protects individual privacy by using data sets matched to small
geographic levels rather than individual people. There are other useful
additions to econometric models such as the Economic Censuses to
describe the economic conditions of metropolitan areas and regions. We
address data quality issues in the various data sets and what needs to
be done to convert program records to files that are useful for
statistical analyses of communities.*

# Introduction

### 

***Need for current information at the community level to track
change***

Primary responsibility for government program strategies and results has
shifted from the federal level to state, county, and community
governments in recent years. At a minimum, state and local governments
are responsible for reporting measures of performance to indicate the
results of programs paid for with federal tax dollars (Government
Performance Requirements Act of 1996). A report from the Committee on
National Statistics of the National Academy of Sciences stated: "The
devolution of program responsibility ...has led to wide variation in
programs across states and within states....\[this\] imposes the need
for a significant data infrastructure ... \[that would\] capture state
variations in policies and outcomes...." \[Moffitt and Ver Ploeg, p.
2\]. Further, local governments say that federal reporting requirements
are the beginning, not the end -- they want solid information they can
use for strategic decisionmaking.

***We have a system of statistics for the nation as a whole***

The United States has a statistical system that provides current
information about demographic, social, economic, health, and housing
trends for the nation as a whole. This is a system of information based
on aggregated data from the decennial census and surveys with a
nationally representative design. Census and survey data are collected
through a decentralized statistical system of federal agencies
coordinated by the Statistical Policy Office of the Office of Management
and Budget (OMB). There is a concerted effort by OMB to consult the
Congress and other federal policy makers about their information needs
and to identify the parts of the data collection system that can most
efficiently address national policy questions. Additionally, for
statistical purposes only, a very limited group of researchers, who are
subject to severe legal restrictions that protect confidentiality, have
access to computerized records of individuals who participate in some
federal programs.

The national statistical system is designed to address *federal*
concerns and meet the requirements of federal laws and court cases.
National surveys follow, at best, only general population and economic
trends for states. States and sub-states areas can use publicly
available data, but the system is designed first and foremost to meet
federal data needs to administer programs and distribute funding.

### **There is no overall system of statistics for states or communities**

For geographic subunits of the nation, there is no coordinated "system"
to consider overall information needs at the community level or how to
meet them. The decennial census, the intercensal population estimates
program, and other data sets (usually at the county level) assembled by
various federal agencies are the only means the national statistical
system provides for examining population, economic, and housing trends
among and within communities.

Community policymakers sometimes compensate by using out-of-date census
results and current averages from national surveys as ratios to estimate
change in their area. Even when the nation is said to be "doing well"
economically, there are parts of the country that are not doing well.
The extent and distribution of trouble spots is not easy to determine
from the statistical system, however. Some liken the situation to
putting one's head in a freezer and feet in a fire and then saying, "on
average, I feel just fine." The problem is how to determine where the
fire is and where the freezer is -- that is, which areas need what and
when. Communities want to be proactive rather than reactive, to prevent
situations rather than try to mop up after the fact. Without an
information system that is current, of high quality, and comparable
across areas, it is difficult to adjust policies in time to meet needs,
establish priorities, and evaluate results.

***New opportunities -- data and technology***

There are new opportunities to develop community information systems
with multiple data sets that provide current and comparable information
for models, as well as new software that better organizes data for
analysis. This can provide research that better informs those making
federal, state, and community policies.

With enhanced models, new software, and the new data sets that are
becoming available, we are beginning to have the components for the type
of information system at the community level that a modern nation
demands. We have the potential to go far beyond traditional uses of
decennial census data, to a system that uses multivariate statistical
techniques with multiple sources of information to provide improved
current descriptions of geographic units as well as predictive models.
Time series analyses can be useful in evaluating the impact of a policy
change. Researchers have a powerful tool in Geographic Information
Systems (GIS) for communicating the results of their analyses to state
and community policymakers through the layered mapping of sets of
information.

***Basic concept of a state and community information system***

The basic concepts and a flowchart showing an enhanced state and
community information system are shown in Figure 1. The enhanced system
is a suite of tools that supplements community administrative records
with community profiles based on summarized survey data in models (such
as econometric or needs assessment models) to inform policy questions.
Among the tools is software that would help community groups with
limited resources. For example, such software might organize data for
analysis and include automated statistical techniques. Another tool
would be improved, inexpensive media for data dissemination (the
internet, CD-ROMs, DVDs) and mapping the data.

A system of community information would track the direction of
population and housing and compare situations among areas across the
nation. It would be able to "generate a profile of short- and long-term
outcomes" of programs, produce information about population subgroups at
risk of requiring assistance, the duration of episodes, and improve our
understanding of how the economic environment affects program success
\[David, pg. 212; Culhane and Metraux, pp. 345-6\].

Such a system would use distributions of the data that are
geographically based and aggregated. Rather than relying solely on
matching the records of individual people across multiple data files,
models would also use aggregated population distributions from different
data sets that are matched for small geographic areas. Matching
aggregated data at the small geographic level resolves the dilemma of
threats to individual confidentiality, as records are anonymous and used
at the group level rather than the individual level. Because access to
the confidential records of individuals would no longer be the only
means of sophisticated data analyses, more researchers could contribute
to the needs of communities for information.

To take advantage of the new data sets and technology, we need to adjust
our paradigms for the collection, organization, dissemination, and
analysis of the data. And, we need strong laws and protocols that
include coordinated statistical policies and using an array of
statistical techniques for protecting the confidentiality of information
about individual people.

###### 

**An Enhanced State and Community Information System**

There are various data sets that can be used as part of an enhanced
state and community information system. These include:

-   National surveys that produce limited state and sub-state data;

-   decennial censuses;

-   the American Community Survey the Census Bureau is developing to
    replace the decennial census long form;

-   surveys state or local governments conduct themselves (produces data
    that cannot be compared with other areas);

-   data sets collected by federal agencies on particular topics such as
    health or crime;

-   estimate programs such as for population counts, poverty, and
    unemployment;

-   lists of physical attributes and the location of facilities; and

-   administrative records collected for the management of programs to
    provide services.

We describe aspects of these data products below. Characteristics of
illustrative data sets are shown in Figure 2. Figure 3 compares surveys
and administrative records.

### **Surveys: The American Community Survey and Census 2000**

### 

The American Community Survey is being developed by the Census Bureau to
replace, the decennial census "long form," that is, the demographic,
social, economic, and housing information required by hundreds of
federal laws and court cases. The data collection by the American
Community Survey will occur throughout the decade rather than just once
in ten years. Thus, eventually, the American Community Survey will
provide estimates, updated *every year*, of the distribution of
*characteristics* of the population and housing in small areas (such as
census tracts).

Between censuses, the American Community Survey will improve current
estimates of the *number* of people in small areas (such as census
tracts and school districts) by furnishing current demographic
distributions for use in the population estimate models. What
communities have not had previously to inform policy issues and evaluate
results, but what the American Community Survey will provide, are
estimates of population and housing *characteristics,* and measurements
of the level and direction of *change* among areas.

In addition to updated estimates for small geographic areas, the survey
will provide estimates for *small population groups* in states and
regions. Profiles for small population groups, such as specific Asian
and Hispanic nationality groups, children under age 5, people 85 and
older, and teenage mothers and whether they are in school or working or
unemployed, has previously been available only from the decennial
census. With the American Community Survey, we will be able to track
trends and the direction of change for population groups. Development of
the American Community Survey was started in 1996, and since 1999, is
being conducted in 31 diverse sites across the country. The Census
Bureau plans to fully implement the survey in every county in the nation
beginning in 2003. Beginning in 2004, and every year thereafter, the
American Community Survey plans to have population and housing profiles
for areas and population groups of 65,000 or more. For smaller areas and
population groups, it will take 3 to 5 years to accumulate information
to provide accurate estimates. Information for areas of 20,000 or more
people will be available (the 3-year average for the period 2003-05)
starting in 2006 and will be updated every year thereafter. Information
for areas below 20,000 in population first becomes available in 2008
(the 5-year average for years 2003-2007). After that, the United States
will finally have a community information resource that shows change
over time, even for neighborhoods and America's small rural areas.
Detailed information about the American Community Survey is on the
Census Bureau's web site at [www.census.gov](http://www.census.gov/) .
American Community Survey data releases to date are available through
the American FactFinder at the Census Bureau's web site. Select
"Subjects A-Z," then "A," and choose "American FactFinder." A free
CD-ROM is available with multiple years of data for sites where data has
been collected since 1996 (request by calling 1-888-456-7215 or by
e-mail at [www.census.gov/acs/www/](http://www.census.gov/acs/www/)) .

The data products from Census 2000 (see Attachment B) will be essential
resources for state and community researchers while they are waiting for
full implementation of the American Community Survey. The first data the
census releases tell us about the number of people in geographic areas.
By December 31, 2000, the Census Bureau will release the count of the
population of states. By April 1, 2001, data users will have counts of
the total population and the population 18 years and older tabulated by
63 categories of race -- six single race groups and 57 combinations of
race.

Throughout 2001 and 2002, various Census 2000 products will be released
that provide us with progressively more detailed information about the
population and housing characteristics of areas. As part of the
decennial census operations, the Census Bureau conducts tests and
evaluations and eventually releases information from those studies. One
test of interest involves a small national sample that collects
information that is essentially the same as the demographic, social,
economic, and housing questions on the census long form. This sample
provides the Census Bureau an early look at long-form results and
essential information about its operational feasibility as they prepare
to convert from the long form to the American Community Survey. The
data, which will start to become available in mid-2001, is for states,
large counties, and the remaining area within each state. Researchers
will be able to use this Census 2000 test data as they develop the
attributes of the policy-specific models they will use once the American
Community Survey is fully operational later in the decade.

Figure 2. Characteristics of Illustrative Data Sets

+--------+--------+--------+------+---------+---------+------+--------+
| **Data | **Sur  |        |      | **Mode  | *       |      | **Phys |
| set**  | veys** |        |      | l-based | *Admin. |      | ical** |
|        |        |        |      | esti    | Re      |      |        |
|        |        |        |      | mates** | cords** |      | **a    |
|        |        |        |      |         |         |      | ddress |
|        |        |        |      |         |         |      | of     |
|        |        |        |      |         |         |      | loca   |
|        |        |        |      |         |         |      | tions; |
|        |        |        |      |         |         |      | attrib |
|        |        |        |      |         |         |      | utes** |
+--------+--------+--------+------+---------+---------+------+--------+
|        | **Nati |        | **R  |         | **Natio | **I  |        |
|        | onally |        | esul |         | nally** | tems |        |
|        | comp   |        | ts** |         |         | un   |        |
|        | arable |        |      |         | **com   | ique |        |
|        | res    |        | **un |         | parable | to   |        |
|        | ults** |        | ique |         | items** | ar   |        |
|        |        |        | to   |         |         | ea** |        |
|        |        |        | ar   |         |         |      |        |
|        |        |        | ea** |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
|        | **S    | *      |      |         |         |      |        |
|        | tate** | *Sub-S |      |         |         |      |        |
|        |        | tate** |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| ## C   | 3-year | ###    |      |         |         |      | No     |
| urrent | avg    | NYC, L |      |         |         |      |        |
|  Pop.  | for    | A-Long |      |         |         |      |        |
| Survey | most   |  Beach |      |         |         |      |        |
|        |        |  metro |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| **Am.  | No     | Se     |      |         |         |      | No     |
| Hsg.   |        | lected |      |         |         |      |        |
| Su     |        | metro  |      |         |         |      |        |
| rvey** |        | areas  |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| **Dec  | Yes,   | Yes,   |      |         |         |      | No     |
| ennial | every  | most   |      |         |         |      |        |
| ce     | 10     | demogr |      |         |         |      |        |
| nsus** | years  | to     |      |         |         |      |        |
|        |        | block; |      |         |         |      |        |
|        |        | soci   |      |         |         |      |        |
|        |        | o-econ |      |         |         |      |        |
|        |        | to BG  |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| *      | Yes,   | Y      |      |         |         |      | No     |
| *Amer. | 2004+  | es[^1] |      |         |         |      |        |
| Com    |        |        |      |         |         |      |        |
| munity |        |        |      |         |         |      |        |
| Su     |        |        |      |         |         |      |        |
| rvey** |        |        |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| **Sta  |        |        | Curr |         |         |      |        |
| te-spo |        |        | ent; |         |         |      |        |
| nsored |        |        | s    |         |         |      |        |
| su     |        |        | mall |         |         |      |        |
| rvey** |        |        | sam  |         |         |      |        |
|        |        |        | ple; |         |         |      |        |
|        |        |        | high |         |         |      |        |
|        |        |        | nonr |         |         |      |        |
|        |        |        | esp. |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| **Popu |        |        |      | In      |         |      | No     |
| lation |        |        |      | cluding |         |      |        |
| estim  |        |        |      | co      |         |      |        |
| ates** |        |        |      | unties, |         |      |        |
|        |        |        |      | MCDs,   |         |      |        |
|        |        |        |      | incorp. |         |      |        |
|        |        |        |      | places  |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| *      |        |        |      | Incl.   |         |      | No     |
| *Local |        |        |      | metro,  |         |      |        |
| Area   |        |        |      | n       |         |      |        |
| Unemp  |        |        |      | onmetro |         |      |        |
| loy-** |        |        |      | labor   |         |      |        |
|        |        |        |      | m       |         |      |        |
| **Ment |        |        |      | arkets, |         |      |        |
| Statis |        |        |      | co      |         |      |        |
| tics** |        |        |      | unties, |         |      |        |
|        |        |        |      | med     |         |      |        |
|        |        |        |      | .cities |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| **Pov  |        |        |      | Co      |         |      | No     |
| erty** |        |        |      | unties, |         |      |        |
|        |        |        |      | school  |         |      |        |
| *      |        |        |      | di      |         |      |        |
| *Estim |        |        |      | stricts |         |      |        |
| ates** |        |        |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| **     |        |        |      |         | No      | Yes  | Yes    |
| School |        |        |      |         |         |      |        |
| b      |        |        |      |         |         |      |        |
| ldg.** |        |        |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| **A    |        |        |      |         | Yes     | Yes  | Y      |
| FDC/** |        |        |      |         |         |      | es[^2] |
|        |        |        |      |         |         |      |        |
| **     |        |        |      |         |         |      |        |
| TANF** |        |        |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| #      |        |        |      |         | Yes     | Yes  | Y      |
| ES-202 |        |        |      |         |         |      | es[^3] |
+--------+--------+--------+------+---------+---------+------+--------+
| # New  |        |        |      |         | Yes     | Yes  | Y      |
|  hires |        |        |      |         |         |      | es[^4] |
+--------+--------+--------+------+---------+---------+------+--------+
| #      |        |        |      |         | Yes     | Yes  | Y      |
|  Crime |        |        |      |         |         |      | es[^5] |
| , type |        |        |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| **     |        |        |      |         | Yes     | Yes  | No     |
| Health |        |        |      |         |         |      |        |
| condit |        |        |      |         |         |      |        |
| ions** |        |        |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+
| **Aba  |        |        |      |         | No      | Yes  | Yes    |
| ndoned |        |        |      |         |         |      |        |
| hou    |        |        |      |         |         |      |        |
| sing** |        |        |      |         |         |      |        |
+--------+--------+--------+------+---------+---------+------+--------+

***Other surveys***

As indicated in Figure 2, some national surveys provide sub-national
data. The American Housing Survey, for example, includes selected
metropolitan areas. The sample size of the Current Population Survey
(CPS) is sufficient for annual estimates of the characteristics of the
population at the national level and for the largest states. For most
states, however, the CPS sample is not large enough to provide annual
estimates for their state. Rather, they use 3-year averages from the CPS
that are updated each year for characteristics such as income and
poverty, educational attainment, patterns of migration, and age
distributions. The margin of error is often unacceptably large for
measuring year-to-year change such as poverty rates of children in the
smallest states.

Some states and communities do their own surveys \[Moffitt and Ver
Ploeg, p.2\]. Advantages include obtaining information that is current
and for the total population of the community defined by the survey
design. Disadvantages are significant. Surveys are expensive and the
results cannot be compared with other areas. Most community surveys have
a relatively small sample. Thus, such surveys can provide information
only for the total population and broad demographic groups (e.g., "65
and older" rather than information about those with different needs and
resources such as the age groups 65-74, 75-84, and 85+). Because
classifications are broad and the sample is generally not large enough
to include explanatory characteristics (such as teenage mothers and
whether they are in school or working), it can be difficult to identify
trends that would direct policy choices.

One common way to reduce the cost of state and community surveys is to
use untested questionnaires and employ a random digit dialing (RDD)
design. RDD surveys are a less expensive way to conduct surveys by
telephone than designs that require a field staff and development of a
complete list of current households. People without telephones are not
included in RDD surveys even though they are often poor and the ones
many public policy programs are often meant to serve. Some conduct such
surveys in English only, another potential bias depending on the area
and the programs for which the information is to be used. Such surveys
tend to have high nonresponse rates, a potentially serious data quality
issue.

### **Other nationally comparable data sets**

Other nationally comparable data sets are produced by federal agencies
for geographic areas (states, counties, and sometimes communities) that
researchers can use. For example, at the county level, researchers can
use summarized information to describe patterns of school enrollment,
income and poverty, employment and unemployment, per capita personal
income, sources of earnings, Social Security recipiency, births and
deaths, the environment, the number and types of crimes reported,
transportation projects, and the incidence of health conditions (for
example, low birth weight, substance abuse, and cancer mortality). A
useful web site for finding many nationally-available information sets
is [www.fedstats.gov](http://www.fedstats/gov)

***Administrative records***

Administrative records that are geographically based and result from the
administration of federal, state, and local programs have enormous
promise as part of an enhanced state and community information system
\[Stevens, 2000\]. Figure 3 compares surveys and administrative records.

Administrative records are created for the management of a program, not
for statistical analyses. Often, variables are not comparable among
areas, especially across state lines. Nevertheless, states and
communities have begun to realize and act on their potential for
current, albeit incomplete information of varying quality, about
segments of the population \[for example, see Culhane and Metraux 1997;
Mueser, et. al., 1999\]. For example, there are records about an area's
housing, such as age of the house and property taxes. Such tracking
systems provide longitudinal information that researchers can use to
calculate prevalence rates, determine the characteristics of people who
use services over time, and measure the effectiveness of various
programs and how changes in one program affect the use of other programs
\[Culhane and Metraux, 1997, p. 343\]. As Martin David has pointed out,
the use of multiple sets of administrative records would better inform
us about "how government activities in several agencies relate to each
other, what government services cost, what groups are covered by
benefits, and what groups are excluded from benefits" \[pg. 214\].

Gaining access to publicly-available survey results is becoming easier
every year. As Culhane and Metraux say in a mild understatement,
"Gaining access to data from other agencies is often fraught with
difficulties...and in many cases will be impossible."

\[p. 351\]. The legal restrictions and compatibility issues are obvious
barriers. State agency heads determine whether the administrative
records of their agency can be used. They sometimes see research as a
"report card" that may cause them more problems than benefits.

As we discuss in the sections that follow, by increasing the ability to
use multiple sources of information with confidentiality protocols, the
nation could realize both a community information system and research
that is useful for planning and evaluation of programs. The significant
benefit of expanded research opportunities to answer policy questions is
counterbalanced by concerns for and the need to develop strict
protocols, both legal and technical, to protect the confidentiality and
safety of the individuals within the data systems. Along with addressing
confidentiality concerns, there are technical steps that should be taken
to make program files useful and of acceptable quality for statistical
analyses. This ideally includes, for example, standardization of
definitions across files and protocols for handling missing and
conflicting information.

Figure 3. Comparison of Surveys and Administrative Records

+------------------+--------------------------+------------------------+
| Characteristics  | **Surveys w/substate     | **Administrative       |
|                  | data**                   | records**              |
|                  |                          |                        |
|                  |                          | **(AR)**               |
+------------------+--------------------------+------------------------+
| #                | # Release varies         | #                      |
| Currency of data | (e.g., 6 months after da |  Release varies but ge |
|                  | ta collection year for t | nerally within a year  |
|                  | he American Community Su |                        |
|                  | rvey but 2 years for dec |                        |
|                  | ennial census long form) |                        |
+------------------+--------------------------+------------------------+
| # Time frame     | #                        | # Usually longitudinal |
| of survey design |  Usually cross-sectional |                        |
+------------------+--------------------------+------------------------+
| Universe         | Total population for     | Program participants;  |
|                  | federally sponsored      | no information about   |
|                  | surveys. State/local     | remainder of           |
|                  | sponsored often          | population of area. As |
|                  | telephone surveys to     | compared with surveys, |
|                  | reduce cost (bias of not | greater risk of double |
|                  | including people without | counting. Does not     |
|                  | phones; English language | include those eligible |
|                  | only; nonresponse often  | but not receiving      |
|                  | high).                   | services.              |
+------------------+--------------------------+------------------------+
| *                | Public-use summary and   | Varies according to    |
| *Confidentiality | sample of micro-data     | state laws and         |
| protocols**      | files regularly          | practices. Profiles    |
|                  | prepared. Access to full | for small areas not    |
|                  | micro data files         | generally available or |
|                  | strictly limited. Social | comparable across      |
|                  | Security Number (SSN)    | areas. SSN usually     |
|                  | generally not collected. | available as           |
|                  |                          | identifier across      |
|                  |                          | files.                 |
+------------------+--------------------------+------------------------+
| **Margin of      | Increases the smaller    | Not applicable as all  |
| error from       | the geographic area;     | members of subgroup    |
| sampling**       | often must use           | receiving services are |
|                  | multi-year averages to   | represented.           |
|                  | obtain reliable data.    |                        |
|                  | May be unacceptably      |                        |
|                  | large for measuring      |                        |
|                  | year-to-year change.     |                        |
+------------------+--------------------------+------------------------+
| **Comparability  | Federal surveys          | Only a few nationally  |
| across areas**   | comparable across areas. | comparable AR files    |
|                  | State surveys unique to  | (e.g., food stamps;    |
|                  | area.                    | AFDC/TANF)             |
+------------------+--------------------------+------------------------+
| **Geo-coding of  | Census Bureau provides   | Conventions and        |
| addresses**      | consistent sampling      | quality vary although  |
|                  | frame and geocoding for  | this is key to correct |
|                  | most federal surveys     | assignment of          |
|                  |                          | characteristics to     |
|                  |                          | specific areas.        |
+------------------+--------------------------+------------------------+
| **Data           | -   Interviewers         | -   Interviewers       |
| collection and   |     formally trained on  |     trained to collect |
| processing       |     questionnaire        |     information for    |
| procedures**     |                          |     program            |
|                  | -   Information usually  |     management, not    |
|                  |     from self report of  |     statistics         |
|                  |     respondent           |                        |
|                  |                          | -   Information        |
|                  | -   Statistical          |     usually            |
|                  |     techniques used for  |     observation of     |
|                  |     item nonresponse and |     interviewer mixed  |
|                  |     inconsistent         |     with self reports  |
|                  |     information          |                        |
|                  |                          | -   Nonresponse and    |
|                  | -   Standards for data   |     inconsistencies in |
|                  |     entry errors         |     data set           |
|                  |                          |                        |
|                  |                          | -   Definitional       |
|                  |                          |     differences across |
|                  |                          |     data sets          |
|                  |                          |     significant        |
+------------------+--------------------------+------------------------+
| **Detail of      | Usually broad profiles   | Rich detail on topic   |
| information**    |                          | of data set, Usually   |
|                  |                          | includes dates,        |
|                  |                          | duration of service    |
|                  |                          | use, some personal     |
|                  |                          | characteristics of     |
|                  |                          | clients.               |
+------------------+--------------------------+------------------------+
| **Cost**         | Relatively expensive as  | Data required for      |
|                  | compared with            | program management;    |
|                  | administrative records   | cost in converting to  |
|                  |                          | statistical files      |
|                  |                          | (e.g., geocoding       |
|                  |                          | addresses, edits,      |
|                  |                          | software)              |
+------------------+--------------------------+------------------------+

### **Filling data gaps with both surveys and administrative records**

The great promise for developing a statistical system for states and
communities lies in taking advantage of the characteristics of
administrative records and surveys. Together, surveys and administrative
records fill gaps that cannot be met when researchers can use only one
or the other. For example, school enrollment records provide information
about students aged 5 to 18 years. School officials have a difficult
time projecting where to locate future schools because they have no
current information about the numbers or characteristics of the
population under age 5 or the age ranges or the demographic and economic
characteristics of children moving in and out of the school district.
That information will be provided by the American Community Survey, as
described above.

Using administrative records with survey information that can be
compared among areas can be useful for both state and federal policy
decisions about funding allocations and the direction of policy. For
example, the Jacob France Center at the University of Baltimore is
working with a consortium of university research centers in six urban
areas to analyze welfare-related transitions. Each center uses
confidential administrative records (such as welfare, employment, and
earnings records) that have been converted to use as statistical files
for use in econometric models to inform policy. Once they can add
current population trends from the American Community Survey, the
researchers can incorporate the current demographic distributions into
their econometric models and hereby broaden the types of questions they
can address. For example, as welfare caseloads drop nationally, they can
better consider the heterogeneity among areas to judge what types of
areas follow national trends and which do not. They can ask questions
such as, "Are the working poor and welfare recipients more likely to
combine households to cope with their economic situations and is that
correlated with changes in the welfare system?" or "Do housing and
transportation subsidies help the working poor remain in the workforce?"

Another use of multiple data sets is to obtain information at the
community level that would be unreasonably expensive if collected by a
survey alone. For example, HUD has initiatives to encourage inner city
economic development. Likewise, the Department of Agriculture has
programs to encourage economic development in rural areas. The
decisionmakers they work with to meet the objectives of the programs are
frustrated by the lack of information about consumer expenditure
patterns to describe community buying power. Standard sales forecasting
models do not work for inner city markets, for example, and need to be
adapted to show whether a neighborhood can support retail stores such as
grocery stores and pharmacies. The information that would help is
similar to that found in a national-level Consumer Expenditure Survey.
Replicating it at the community level would be a prohibitive cost and
burden on the public. It is conceivable, however, that indicators from
the American Community Survey (e.g., age distributions, educational
attainment, home ownership, and employment) in combination with data
businesses collect (such as sales in a similar area), could provide a
sufficiently reliable model of buying power to inform the strategy of
those seeking to develop the economy of a particular inner city.

Traditionally, the input for models is a record of information for
individual people collected from one or more sets of administrative
records. While it is cost-efficient for researchers to have access to
individual records to evaluate interaction effects among programs,
American society places high value on protection of individual
confidentiality and our legal system reflects that. For example, sworn
Census employees (including with Special Sworn Status) are subject to
fines up to \$5,000 and five years in prison if they reveal any
individual information from the American Community Survey, the decennial
census, the Economic Censuses, and other surveys protected by Title 13
of the U.S. Code. The confidentiality of individual information in
administrative records is also protected under various federal and state
laws. This severely limits the number of researchers who can use these
sources for complex questions.

There is a substantial new tool for providing an enhanced information
system for states and communities. It is supplementing the information
from administrative records (whether at the unit level or aggregated)
with aggregated summaries derived from publicly available survey data
for small geographic areas as input for models. Under this method, it is
not necessary to match individuals across files. Therefore, many
researchers, rather than only a few, can use aggregated summaries to
work on questions important to communities. This approach does not
answer all questions, but it will afford researchers new opportunities
to understand the interactive effects of events and for policymakers to
consider preventive actions. A second expectation is that such research
goes beyond the traditional methods of historical descriptions to
significantly improve our ability to predict the course of population
and economic events through probabilistic modeling.

A system that uses multiple data sets furnishes insight into the
interactions between changes in an area's population and industrial mix,
and other factors such as health conditions \[Craney, Padgett, and
Lorimer\], service systems \[Culhane and Metraux, p. 342\], and the
environment. Examples from federal programs include the Census Bureau's
Small Area Income and Poverty Estimates Program (SAIPE) and program to
make population estimates between censuses, and the Local Area
Unemployment Statistics (LAUS) program of the Bureau of Labor Statistics
(see Attachment A for more information on both programs). Basically, the
concept is to merge aggregated data systems on population and housing
characteristics, metropolitan-area economic situations, and locally
available computerized records on clients who use various community
programs into models to answer policy questions (see Figure 1). This
methodology allows predictive modeling and provides estimates of
population characteristics that are an improvement over those available
from any one data system alone. The results can help meet community
needs for social, economic, and housing information for making policy
decisions and evaluating program progress and direction. The improved
estimates and methods can help communities make projections of their
outlook that could help them as they look towards investments in their
future.

The models do need to be benchmarked and updated. In particular, it is
important to compare the summary information derived from the merged
aggregate data with that which can be derived from merged micro data.
How might this be done? Such micro data exist at the Census Bureau, but
the sensitive nature of the data dictates that access is extremely
restricted -- the data are within a firewall behind a firewall behind a
firewall. However, validation of models is certainly feasible for
approved Title 13 projects and researchers with Special Sworn Status at
Census Bureau headquarters in Suitland, MD.

Along with modified models, new software that better organizes data for
analysis is rapidly being developed. There is need for easier access to
data and improved displays of research results. For example, a difficult
issue is how to help decisionmakers understand the limits of the data
they are using by including information about the size of sample errors
from surveys in reports, charts, and communication media such as GIS.
The American Community Survey reports the 90-percent confidence interval
in its data products to alert data users to the reliability provided by
the sample size for the subject matter detail and geographic area of
interest. Leitner and Buttenfield \[2000\] suggest graphical guidelines
that should be incorporated as defaults in GIS software.

Another need is for a "more accurate nationwide shared geographic
database that can alternately use both address and geographic coordinate
as entry points or match keys ...\[because of\] the increasing need to
integrate environmental, health, and population databases at local and
regional and state levels..." \[Sperling and Sharp, p. 38\]

One powerful new tool is the ability to overlay maps of data on
different topics with the software, Geographic Information Systems
(GIS). Nancy LaVigne (National Institute of Justice) has pointed out
that GIS can serve as a data integrator, an analysis tool, and a
presentation vehicle. She says, "As a data integrator, GIS enables
researchers to merge community-level data from disparate sources based
on common geographic references. The analysis capabilities of GIS offer
a powerful means of examining spatial clustering, correlation, and
adjacency effects. In addition to these analysis functions, the power of
the visual display of data in map form makes GIS an ideal tool for
communicating research findings to policymakers and practitioners"
(LaVigne, June 7, 2000\]. Culhane, Lee, and Wachter, for example, used
multivariate analyses to examine housing and neighborhood conditions,
such as housing code violations and crime, in relation to the last home
addresses of people who had subsequently become homeless. Dynamic models
of neighborhood change (population characteristics, housing stock,
physical condition) could inform those designing, targeting, and siting
prevention programs or suggest whether other strategies, such as
targeting high crime areas to reduce residential instability, would be
effective \[Culhane, Lee, and Song\].

**Examples of the types of policy questions communities could answer
with multiple data sets merged in econometric models:**

Generally, a community information system could provide:

-   Time series data for analyzing community trends.

-   Estimates of the size and composition of population groups.

-   Evaluations of the effectiveness of programs and policies.

-   Improved understanding of the effect of population group dynamics on
    service systems (public and private) and related service systems
    (for example, the effect of welfare leavers on service systems for
    the homeless population, and on the health care and justice systems)

-   Predictors of events, population changes, prevalence, and relative
    risks of events.

-   Neighborhood and other small-area indicators.

Some specific examples of the types of policy questions communities
could answer with multiple data sets merged in econometric models are
shown below:

-   Economic development:

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-   Which population groups benefit the most from the types of jobs
    being created (or lost) in an area? Who are the winners and losers?

-   To what extent do jobs follow people and people follow jobs?
    Causality?

-   As employers move from an area, what types of employees leave? Stay?
    Likewise, when employers move into an area or expand, what
    population groups are drawn into the area?

-   Given an area's mix of industries and population, what are the
    options for economic development strategies? What types of
    industries are hiring high school dropouts, how well do the jobs pay
    and what is the duration of such employment? What happens as
    unemployment levels go up (down)?

-   Population effects are interrelated with the business conditions in
    the area, but what are the interrelationships? Because of
    confidentiality and competitive business interests, economic data
    about businesses are presented only at high geographic levels or as
    basic information for smaller geographic areas such as that from
    County Business Patterns. Additionally, population and business
    censuses have been conducted on different schedules. The coming
    availability of yearly population summaries from the American
    Community Survey will allow us to look at population and business
    effects in time periods that coincide. Econometric models will be
    able to provide predictions of population-business interactions for
    smaller areas based on the relative population and business
    conditions within a region.

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-   Transitions in welfare to work:

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-   As caseloads drop nationally, what is happening at the local level?
    Demonstrate the heterogeneity among sites in changes in local
    economic conditions and caseloads. What types of areas follow
    national trends and which do not?

-   What has happened to those who have left the welfare system but who
    are not working in the area? Where are they (e.g., in school, in
    prison, homeless, moved from area and working elsewhere)? What are
    the patterns among areas?

-   Is there resource sharing? Are there changing household demographics
    among the poor with children correlated with changes in the welfare
    system? For example, are more households being combined among
    welfare recipients than among the working poor?

-   What population groups are eligible for program services but not
    enrolled as expected? What is the relationship between changes in
    poverty rates and caseloads? How does this differ among types of
    areas?

-   What supports are needed to keep low-wage workers on the job? Are
    transportation and housing subsidies needed to encourage entry and
    remaining in the workforce at relatively low wages? What level of
    cost can low-wage workers afford themselves? What are the tradeoffs
    between subsidies for housing versus transportation to reduce the
    effect of the mismatch between residence and job location of
    low-wage workers?

-   Educational outcomes \-- what is the earnings stream of those who
    have vocational training or community college? Does this differ
    among types of areas?

-   Child well-being -- What is their stability of residence and
    household composition? Childcare? Foster care?

-   Youth -- What are the school-work-welfare transitions among
    adolescents and how do they differ among different types of areas?

-   What are the differential effects of barriers such as prison
    records, lack of education, and use of a language other than English
    to employment and income among different demographic groups (such as
    age, marital status, race/ethnicity)?

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-   Health needs assessments:

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-   How do changes in population and industry mix affect health needs
    and ability to pay?

-   What can you predict about future needs given an area's migration
    patterns and changes in demographic composition?

-   What is the impact of different environmental conditions on health
    needs among different demographic groups (e.g., race/ethnic groups,
    age, economic status)?

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-   Projecting future needs for community facilities such as schools and
    hospitals:

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-   Use population surveys to determine migration patterns, demographic
    group (e.g., age, gender, economic status, educational attainment)
    with school enrollment records (or hospital use records) to project
    future needs for location of facilities and types of facilities
    (such as temporary trailers or long-term school buildings; geriatric
    care or prenatal care facilities).

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-   Neighborhood development and housing policy:

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-   What is the likely effect on the affordability of housing and the
    composition of the population?

-   What are the socio-economic effects of living in particular types of
    neighborhoods relative to family types? What is the duration of
    residence in neighborhoods with concentrated poverty? How does
    residence in such areas affect employment opportunities?

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-   Safety:

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-   How does the incidence of crimes (by type of crime) change in
    neighborhoods with changes in migration patterns into and out of
    those neighborhoods and changes in the local economy?

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-   Energy and the environment:

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-   What are the predicted changes in energy demand or environmental
    quality as the mix of population and industry changes?

# 

# Example of How Multiple Information Sources Might Be Used

How could a county economic development council, in the year 2000,
approach policy questions using existing multiple data sources in an
econometric model? The Census Bureau, in preparation for the full
implementation in 2003, is asking for feedback on which tabulations best
respond to public policy questions. This provides a window of
opportunity for the research community to design inputs into the
decisionmaking process. In order to illustrate an example of how this
might be done, we pose the following illustrative policy question:

Given a county's mix of industries and population, what are some options
for economic development strategies that focus on improving the
situation for the county's low skill workers? What types of industries
are hiring low skill workers, and how well do the jobs pay? What happens
as unemployment levels go up (or down)? What if there is a downturn in
the economy?

Although these are core economic development issues, the data available
for describing these outcomes econometrically are scarce and not
available in any one data set. Ideally, one might want to look at a
model that either structurally describes the demand and supply for low
skilled workers, or sets up a reduced form model of the type:

Employment~low\ skill,\ industry,\ time~ = Xβ + Zδ + CC λ+ ε

Earnings~low\ skill,\ industry,\ time~ = X α+ Zφ + CC ϕ + μ

where:

X reflects the characteristics of the available labor pool of workers
(such as age, race, sex, education, prior work history, welfare
recipiency),

Z reflects the characteristics of the firms in each industry (such as
the number and average size of firms, total employment in the industry,
average payroll, turnover rates, and growth rates), and

CC reflects county conditions, such as the local unemployment rate, or
statewide economic factors.

Once this model is estimated, the effect of structural demand side
changes in the economy can be modeled by simulating different changes in
Z. Changing CC can simulate the effect of changing economic conditions.

The first step for every researcher is to find the data sets that are
adequate to address the policy issues. A stocktaking of available
sources would reveal that there might be several relevant data sets
available: the 1997, 1998, and 1999 American Community Survey; the 1990
census; school enrollment records (every year through 1999); AFDC/TANF
records; and Unemployment Insurance (UI) wage records.

How might these be used in the simple model described above? The
dependent variables - the employment and earnings of low skill workers -
could be derived by year and industry from both the 1990 census and each
year of the American Community Survey. The workforce characteristics -
educational attainment of the population, as well as the prior work
history, age, race and sex distributions - could be derived from both
the 1990 census as well as each year of the American Community Survey.
Administrative records on TANF and AFDC recipiency could be aggregated
to derive the proportion of welfare recipients in the population. In
addition, if the recipiency were matched to UI data, it could even be
used to reveal which types of industries hired welfare recipients, and
this information could be included in the vector of Z variables.

Finally, the variables on the firm side, such as the average size of
firm, payroll, turnover rates, and growth rates of firms in each
industry could be derived from UI wage records and ES-202 files for each
quarter of 1990-99.

These can then be used to simulate changes in the demand for low skilled
workers as a given industry grows or contracts. If, for example, firms
in health services were projected to become an important segment of the
economy, the effect of this increased growth on the employment and
earnings of unskilled workers could be simulated, assuming, of course,
that other things were held constant!

The point of this exercise was to determine what the key tabulations are
likely to be. In this case, the key tabulations from the American
Community Survey are employment and earnings by type of worker and by
industry; age, race, gender, and educational experience, by county.
These can be directly inserted into a regression model, together with
similar tabulations from administrative data. Although different
applications might well be envisaged, these kinds of tabulations would
certainly enhance the ability of researchers to model important, policy
relevant, questions.

**Preparing For A Multiple Choice Future**

We have discussed a vision of developing a coordinated state and
community information system for strategic decisionmaking that makes
efficient use of technology yet honors and protects confidentiality. The
potential for such information systems is huge. The ability to do such
research could affect, for example, how public programs are "funded,
targeted, and evaluated," and would be useful in designing programs for
high risk groups and assessing needs and resources \[Culhane and
Metraux, p. 347\].

There are significant barriers, however, that require sustained and
coordinated effort to overcome. For example:

-   The American Community Survey must be fully implemented nationwide
    with a sample that is large enough to provide updated estimates of
    demographic and housing characteristics for census tracts.

-   Statistical policy should be coordinated among the multiple data
    sets of administrative records to standardize, to the extent
    possible, definitions, ways to ask demographic questions (for
    example, age and race), processing and editing rules such as for
    missing or inconsistent data, and the coding of addresses and
    subjects such as industry and occupation. Where possible, it would
    be advantageous for the standardization to be consistent with the
    conventions used in national surveys such as the American Community
    Survey.

-   Data quality issues in administrative records need to be evaluated
    and addressed. For example, the people who collect information from
    program participants think of its use in terms of a management
    information system, not as statistical files for research. Training
    such staff about the extended uses of the data helps them understand
    why it is important to gather the information according to standard
    practices and to try to obtain all the information requested. We
    have an example of the effectiveness of this approach from the
    National Center for Health Statistics. It trains funeral directors
    about the uses of the information requested on death certificates
    and has thereby improved the quality of that data.

-   Much has been written about the difficult balance between individual
    rights to privacy and the confidential use of administrative records
    to achieve efficient, fair government \[Stevens; Warren and
    Brandeis, p. 193; Hatch, p. 1; National Academy of Sciences; Human
    Rights Commission\]. Stevens notes that "privacy" refers to the
    right of individuals to control information that is about them while
    "confidentiality" refers to how information is handled and what the
    statutory responsibilities are \[Stevens, p.7\]. Protocols are
    necessary to protect the confidentiality of the physical records as
    well as avoiding the disclosure of an individual's identity and
    attributes when information is released in a statistical file.

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-   Confidentiality is protected through laws and regulations,
    statistical policy, and statistical techniques. It is especially
    important that laws and regulations distinguish between
    administrative and statistical uses of information about an
    individual. Administrative uses explicitly affect an individual,
    such as providing or denying benefits or identifying illegal
    activities. Statistical analyses are uninterested in an individual
    once the person's characteristics are collected and then aggregated
    with those of others to identify patterns. Current legal language is
    often open to interpretation as to whether and how third party
    researchers use administrative records \[Stevens, pg. 4\].

-   Statistical policies to protect individual confidentiality may
    include, for example, control over who has access to individual
    records, an institutionalized expectation and ethic that
    confidentiality is everyone's responsibility, a disclosure review
    board, and the requirement that every staff member sign a statement
    every six months that (s)he knows and understands the laws and
    practices to secure information. A significant problem is that, with
    multiple agencies, executive responsibilities, philosophies, and
    roles among legal counsel, the lack of an overall policy results in
    ad hoc and ever changing practices \[Stevens, pg. 4\]. Uniform data
    sharing agreements that cover multiple data sets would be useful.

-   Statistical techniques for disclosure avoidance address issues such
    as the minimum number of cases required before data are shown for an
    area, the detail that is provided in cross tabulations in relation
    to a population size, switching of households among areas so one can
    never be certain of identity, and techniques that avoid disclosure
    in data sets from Year 1 to Year 2 when a city annexes a few blocks
    (that is, information can be gained about the annexed blocks by
    subtraction). Advanced techniques for disclosure avoidance and
    disclosure detection are needed for use with Geographic Information
    Systems (GIS).

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-   We need to further develop software to make data access easier for
    staff in community organizations who are not trained demographers or
    economists. They also need software that better organizes the data
    for predetermined analyses and statement of results.

-   We need to improve the geographic coding of addresses on
    administrative records and further develop software such as GIS for
    better communication of research results to community organizations
    and policymakers.

#### References

Bollinger, Christopher R. and Martin H. David. 2000. *"I Didn't Tell and
I Won't Tell": Dynamic Response Error in the SIPP.* Report for the
National Science Foundation.

Craney, James, with Paige Padgett and Ronald Lorimer. 1998. The Level of
Community Well-Being and Selected Maternal and Child Health Indicators:
The Houston MSA. See website of Healthy Communities Project at the
University of Texas-Houston School of Public Health at:
\<www.sph.uth.tmc.edu/chis/background.htm\>

Coulton, Claudia J. 1999. Public Assistance Records: A Source for
Neighborhood Indicators. Washington, D.C.: National Neighborhood
Indicators Partnership, The Urban Institute.

\_\_\_\_\_\_\_\_\_, with Lisa Nelson and Peter Tatian. 1997. Catalog of
Administrative Data Sources: For Neighborhood Indicator Systems.
Washington, D.C.: National Neighborhood Indicators Partnership, The
Urban Institute.

Culhane, Dennis P. and Stephen Metraux. 1997. Where to from Here? A
Policy Research Agenda Based on the Analysis of Administrative Data. In
*Understanding Homelessness: New Policy and Research Perspectives*, ed.
Dennis P. Culhane and Steven P. Hornburg, 341 -- 380.

\_\_\_\_\_\_\_\_\_\_, Chang-Moo Lee, and Dayoung Song. 1997.
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Minority Segregation. Unpublished paper. University of Pennsylvania.

\_\_\_\_\_\_\_\_\_\_, Chang-Moo Lee, and Susan M. Wachter. 1997. Where
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In *Understanding Homelesseness: New Policy and Research Perspectives,*
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David, Martin H. 2000. "Monitoring Income for Social and Economic
Development," in Burt S. Barnow, Thomas A. Kaplan, and Robert A. Moffitt
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Works Program.* Albany, NY: Rockefeller Institute Press. (Previously
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Hatch, Orrin G. 1983. Balancing Freedom of Information With
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Kingsley, G. Thomas (ed.). 1999. *Building and Operating Neighborhood
Indicators Systems: A Guidebook.* National Neighborhood Indicators
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conference, "Developing Public Policy Applications with The American
Community Survey and Community Administrative Records," Jacob France
Center, University of Baltimore.

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Moffitt, Robert A. and Michele Ver Ploeg, eds. 1999. *Evaluating Welfare
Reform: A Framework and Review of Current Work*. Committee on National
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Welfare Caseload, Economic Growth, and Welfare to Work Policies: An
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University of Baltimore.

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2000. Welfare-to-Work Transitions in Five Urban Areas: Initital Results
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Effort to Enhance TIGER.* URISA Journal (Summer), pp. 35-42.

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Pitfalls. Baltimore, MD: The Jacob France Center. 61 pp.

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_. 1996. Towards an "All Purpose"
Confidentiality Agreement: Issues and Proposed Language. Report for the
Office of the Assistant Secretary, Maryland Department of Labor,
Licensing, and Regulation.

Warren, Samuel D. and Louis D. Brandeis. 1890. The Right to Privacy,
*Harvard Law Review*, IV:5 (December).

###### Attachment A

**Examples of Federal Programs for Estimating Distributions from
Multiple Data Sets in Models**

**Small Area Income and Poverty Estimates (SAIPE)**

An example of the concept of making estimates for small areas by
combining survey data for states with county-level program records is
the Census Bureau's program for making estimates of poverty for small
areas, the Small Area Income and Poverty Estimates (SAIPE) program. The
SAIPE program uses intercensal estimates of the population of states and
counties by age and group quarter status. It uses income and poverty
estimates from the 1990 census for states and counties and from the
Current Population Survey for states (that is, aggregated distributions
for each state). These aggregated data are combined with program records
available for all counties in the nation in statistical models to
produce estimates of poverty for school-age children for counties and
school districts. The program records SAIPE uses are the number of food
stamp and Social Security recipients, summarized data from tax records,
and estimates of personal income. Estimates of poverty and income are
made using survey and administrative data that are not available until
two years after the year to which they refer. Thus, income year 1997
estimates cannot be produced until the year 2000. Detailed information
about the SAIPE program is available on the Census Bureau's web page at:
\<www.census.gov/hhes/www/saipe/techdoc\>

# Population Estimates Between Censuses: Concepts and Geography

### The information below is extracted from the Census Bureau's website and it was last revised on March 20, 2000. For more information contact U.S. Census Bureau, Population Division, Population Estimates Branch. 

### 

### What is a population estimate? 

The Census Bureau\'s Population Estimates Program (PEP) produces July 1
estimates for years after the last published decennial census (1990), as
well as for past decades. Existing data series such as births, deaths,
federal tax returns, Medicare enrollment, and immigration, are used to
update the decennial census base counts. PEP estimates are used in
federal funding allocations, in setting the levels of national surveys,
and in monitoring recent demographic changes. A methodology reference
accompanies most of our population estimates offerings.

### Revisions to estimates and geographic detail 

With each new issue of July 1 estimates, PEP revises estimates for years
back to the last census. Previously released estimates become
superseded. Revisions to estimates are usually due to input data
updates, changes in methodology, or legal boundary changes. The
frequency of estimates and availability of demographic detail vary by
geographic level.

### Why does the Census Bureau produce estimates? 

The legal requirement for the Census Bureau to produce subnational
population estimates is given in Title 13 of the U.S. Code. Title 13
states that: \"During the intervals between each census of population
required under section 141 of this title, the Secretary, to the extent
feasible, shall annually produce and publish for each State, county, and
local unit of general purpose government of fifty thousand or more,
current data on total population and population characteristics and, to
the extent feasible, shall biennially produce and publish for units of
general purpose government current data on total population.\" The
reason for producing estimates is given in Section 183 of Title 13:
\"Except as provided in subsection (b), for the purpose of administering
any law of the United States in which population or other population
characteristics are used to determine the amount of benefit received by
State, county, or local units of general purpose government, the
Secretary shall transmit to the President for use by the appropriate
departments and agencies of the executive branch the data most recently
produced and published under this title.\"

In other words, the Census Bureau produces subnational estimates for use
in the allocation of funds to state, county, and local governments. For
this reason, the Census Bureau produces population estimates for
general-purpose functioning governments. These governments have elected
officials who can provide services and raise revenue. In addition to
states and counties, incorporated places and minor civil divisions also
serve as general- purpose functioning governmental units.

### For what geographic areas does the Census Bureau produce estimates? 

In addition to the Nation, the 50 states, and the District of Columbia,
PEP produces estimates for the following geographic entities:

Counties (and equivalents)

Counties are the primary legal divisions of most states. Most counties
are functioning governmental units, whose powers and functions vary from
state to state. In Louisiana, these primary divisions are known as
parishes. In Alaska, the county equivalents consist of legally organized
boroughs or \"census areas\" delineated for statistical purposes by the
State of Alaska and the Census Bureau (since 1980). In four states
(Maryland, Missouri, Nevada, and Virginia), one or more cities are
independent of any county organization and thus constitute primary
divisions of their states; the Census Bureau refers to these places as
\"independent cities\" and treats them as the equivalents of counties
for estimates purposes. The District of Columbia has no primary
divisions and the entire area is considered to be the equivalent of a
county and in Puerto Rico, municipios are the primary divisions and
treated as county equivalents for estimates purposes. Legal changes to
county boundaries or names are typically infrequent. Changes that have
occurred since the 1990 Census are documented at:
<http://www.census.gov/population/www/estimates/geonotes/>

These notes also include information on changes to the areas described
below.

Minor Civil Divisions

Legally defined county subdivisions are referred to as minor civil
divisions (MCDs.) MCDs are the primary divisions of a county. They
comprise both governmentally functioning entities - - that is, those
with elected officials who provide services and raise revenues \-- and
nonfunctioning entities that exist primarily for administrative
purposes, such as election districts. Twenty-eight states and Puerto
Rico have MCDs. However, the MCDs function as general purpose
governmental units in all or part of only twenty states. Within these
twenty states, PEP produces estimates for all governmentally functioning
MCDs and for nonfunctioning MCDs in counties that contain at least one
functioning MCD.

The legal powers and functions of MCDs vary from state to state. Most of
the MCDs in twelve states (Connecticut, Maine, Massachusetts, Michigan,
Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode
Island, Vermont, and Wisconsin) serve as general-purpose local
governments. In the remaining eight states for which PEP produces MCD
level estimates (Illinois, Indiana, Kansas, Missouri, Nebraska, North
Dakota, Ohio, and South Dakota) the MCDs, for the most part, perform
less of a governmental role and are less well known locally, even though
they are active governmental units.

MCDs primarily are known as towns (in New England, New York, and
Wisconsin), townships, and districts, but also include a variety of
other entities. In Maine and New York, American Indian reservations are
not part of any other MCD and therefore, the Census Bureau treats them
as MCDs. PEP does not produce separate estimates for American Indian
Reservations regardless of their MCD status. In some states, all or some
incorporated places are subordinate to the MCDs in which they are
located. Therefore, a place may be either independent of or dependent
upon MCDs. In one state (Ohio), a multi-county place may be treated
differently from county to county. The District of Columbia defined no
MCDs for the 1990 census, so the District itself serves as the
equivalent of an MCD for data presentation purposes. No functioning MCDs
exist in Puerto Rico.

Incorporated Places

The legal designations, powers, and functions of incorporated places
vary from state to state. Incorporated places include cities, towns
(except in New England, New York, and Wisconsin where the Census Bureau
recognizes towns as MCDs for census purposes), boroughs (except in
Alaska, where the Census Bureau recognizes boroughs as equivalents of
counties, and New York, where the Census Bureau recognizes the five
boroughs that constitute New York City as MCDs) and villages.
Incorporated places can cross both county and MCD boundaries. When this
occurs, the place name is followed by the designation \"pt\" (which
stands for part). The PEP produces estimates of the

unincorporated \"balance of county\" area for counties that are not
entirely composed of incorporated places. Another way to understand this
is to think of the \"balance of county\" as the county population minus
the county population resident within incorporated places.

Consolidated Cities

Consolidated cities are a unit of government for which the functions of
an incorporated place and its county or MCD have merged. The legal
aspects of this action may result in both the primary incorporated place
and the county or MCD continuing to exist as legal entities, even though
the county or MCD performs few or no governmental functions. Where one
or more other incorporated places within the consolidated government
continue to function as separate governmental units, the primary
incorporated place is referred to as a \"consolidated city.\"

Estimates are not shown for consolidated cities. Rather, estimates are
displayed for the consolidated city \"remainder,\" which is the
consolidated city minus the semi-independent incorporated places located
within the consolidated city. Consolidated cities include: Butte-Silver
Bow, MT; Athens-Clark County, GA, Augusta-Richmond County, GA, Columbus,
GA; Indianapolis, IN; Jacksonville, FL; Milford, CT; and
Nashville-Davidson, TN. Estimates also are produced for the
semi-independent places which together with the \"remainder record,\"
sums to the entire territory of the consolidated city.

### Additional Information on Geographic Entities 

A more complete narrative treatment of these areas is found in the
Geographic Areas Reference Manual:
http://www.census.gov/geo/www/garm.html authored by the Census Bureau\'s
Geography Division. This manual provides a comprehensive description of
all the geographic entities recognized and reported in the Census
Bureau\'s various Censuses and Surveys.

**Local Area Unemployment Statistics (LAUS) \-- Overview**

The Bureau of Labor Statistics (BLS) has a Local Area Unemployment
Statistics (LAUS) program. Unemployment statistics for local areas are
model based and use multiple, nationally available data sets. BLS is
conducting research so that once the American Community Survey is fully
implemented, they hope to use those current demographic estimates to
replace the historical information from the previous decennial census.
The information below describes the current LAUS program and is
excerpted from the website of the Bureau of Labor Statistics. The
address is: [www.bls.gov/lauov.htm](http://www.bls.gov/lauov.htm)

The Local Area Unemployment Statistics (LAUS) program is a Federal-State
cooperative effort in which monthly estimates of total employment and
unemployment are prepared for approximately 6,800 areas:

-   Census regions and divisions

-   States

-   Metropolitan areas (primary metropolitan statistical areas and
    metropolitan statistical areas)

-   Nonmetropolitan labor market areas

-   Counties and county equivalents

-   Cities of 25,000 population or more

-   Cities and towns in New England regardless of population

These estimates are key indicators of local economic conditions. The
Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is
responsible for the concepts, definitions, technical procedures,
validation, and publication of the estimates that State employment
security agencies prepare under agreement with BLS.

The concepts and definitions underlying LAUS data come from the Current
Population Survey (CPS), the household survey that is the official
measure of the labor force for the nation. Annual average data for all
States, the District of Columbia, New York City, and the Los
Angeles-Long Beach metropolitan area are derived directly from the CPS.

Monthly estimates for these areas are produced using estimating
equations based on regression techniques. These models combine current
and historical data from the CPS, the Current Employment Statistics
(CES) program, and State unemployment insurance (UI) systems. Estimates
for substate labor market areas (other than the two areas

mentioned above) are produced through a building-block approach known as
the \"Handbook method.\" This procedure also uses data from several
sources, including the CPS, the CES program, State UI systems, and the
decennial census, to create estimates that are adjusted to the statewide
measures of employment and unemployment.

Below the labor market area level, estimates are prepared using
disaggregation techniques based on inputs from the decennial census,
annual population estimates, and current UI data.

###### 

**Attachment B: CENSUS 2000 DATA PRODUCTS**

######### 

Census 2000 data products are designed to meet a variety of data needs
for different segments of the data user community. The data products
described here provide a summary of the general tabulation and
publication program for the 50 states, the District of Columbia, and
Puerto Rico (which is treated as a state equivalent for each data
product). Please note that constraints with staffing and budget, federal
guidelines regarding the tabulation of data by race and ethnicity, data
processing, or other considerations may result in changes to the types
of data products prepared or the timing of their release.

For more information on Census 2000 data products, please contact Louisa
Miller (Population Division) on 301-457-2073 or by e-mail at
\<lmiller@census.gov\>

+------------+-------------------------------------------+-------------+
| #          | **100-Percent Data Products**             | **Lowest    |
| # Planned  |                                           | Level**     |
|            |                                           |             |
| ## Re      |                                           | **          |
| lease Date |                                           | Geography** |
+------------+-------------------------------------------+-------------+
| # MAR      | ###### Ce                                 | Blocks      |
|  -- APR 1, | nsus 2000 Redistricting Data Summary File |             |
|            |                                           |             |
| # 2001     | -   State population counts for           |             |
|            |     legislative redistricting             |             |
|            |                                           |             |
|            | ##### *Media: Internet, CD-ROM*           |             |
+------------+-------------------------------------------+-------------+
| JUN -- SEP | ###### Demographic Profile                | Places      |
| 2001       |                                           |             |
|            | ###### Popul                              |             |
|            | ation totals and selected population and  |             |
|            | housing characteristics in a single table |             |
|            |                                           |             |
|            | *Media: Internet, CD-ROM, paper*          |             |
+------------+-------------------------------------------+-------------+
| JUN -- SEP | ###### Co                                 | Co          |
| 2001       | ngressional District Demographic Profile  | ngressional |
|            |                                           | Districts   |
|            | -   Population totals and selected        | of the      |
|            |     population and housing                |             |
|            |     characteristics in a single table for | 106^th^     |
|            |     Congressional Districts only          | Congress    |
|            |                                           |             |
|            | *Media: Internet, CD-ROM, paper*          |             |
+------------+-------------------------------------------+-------------+
| # JUL 2001 | **Race and Hispanic or Latino Summary     | G           |
|            | Table CD-ROM**                            | overnmental |
|            |                                           | units       |
|            | ######## Medium: CD-ROM                   |             |
+------------+-------------------------------------------+-------------+
| *At the    | **Summary File 1 (SF 1)**:                | Blocks      |
| state      |                                           |             |
| level:*    | -   Population counts for 63 race         | Census      |
|            |     categories and Hispanic or            | tracts      |
| JUN -- SEP |     Latino.....                           |             |
| 2001       |                                           | Bl          |
|            | -   Population counts for many detailed   | ocks/Census |
| *At the    |     race and Hispanic or Latino           | tracts      |
| national   |     categories, and American Indian and   |             |
| level:*    |     Alaska Native tribes.............     |             |
|            |                                           |             |
| MAY -- JUN | -   Selected population and housing       |             |
| 2002       |                                           |             |
|            |  characteristics......................... |             |
|            |                                           |             |
|            | \[National file includes urban and rural  |             |
|            | data\]                                    |             |
|            |                                           |             |
|            | *Media: Internet, CD-ROM*                 |             |
+------------+-------------------------------------------+-------------+
| *At the    | **Summary File 2 (SF 2)**:                | Census      |
| state      |                                           | tracts      |
| level:*    | -   Population and housing                |             |
|            |     characteristics iterated for many     |             |
| OCT -- DEC |     detailed race and Hispanic or Latino  |             |
| 2001       |     categories, and American Indian and   |             |
|            |     Alaska Native tribes                  |             |
| *At the    |                                           |             |
| national   | \[National file includes urban and rural  |             |
| level:*    | data\]                                    |             |
|            |                                           |             |
| JUN -- JUL | *Media: Internet, CD-ROM*                 |             |
| 2002       |                                           |             |
+------------+-------------------------------------------+-------------+
| *At the    | ###### Quick Tables                       | Census      |
| state      |                                           | tracts      |
| level:*    | -   Table shells with population and      |             |
|            |     housing characteristics where the     |             |
| APR -- DEC |     user can specify a geographic area    |             |
| 2001       |     and a population group                |             |
|            |                                           |             |
| *At the    | *Medium: Internet*                        |             |
| national   |                                           |             |
| level:*    |                                           |             |
|            |                                           |             |
| MAY -- JUL |                                           |             |
| 2002       |                                           |             |
+------------+-------------------------------------------+-------------+
| *At the    | ###### Geographic Comparison Tables       | Places of   |
| state      |                                           | 1,000 or    |
| level:*    | ###### Population and hous                | more        |
|            | ing characteristics for a list of geograp | population  |
| APR -- OCT | hic areas (e.g., all counties in a state) |             |
| 2001       |                                           |             |
|            | *Medium: Internet*                        |             |
| *At the    |                                           |             |
| national   |                                           |             |
| level:*    |                                           |             |
|            |                                           |             |
| JUN -- JUL |                                           |             |
| 2002       |                                           |             |
+------------+-------------------------------------------+-------------+
| SEP -- DEC | ###### Advanced Query Function            | User        |
| 2001       |                                           | defined     |
|            | -   User specifies contents of            | down to     |
| (Release   |     tabulations from full microdata file  | block       |
| subject to |                                           | groups      |
| policy     | -   Includes safeguards against           |             |
| decisions  |     disclosure of identifying information |             |
| on access  |     about individuals and housing units   |             |
| and        |                                           |             |
| confid     | *Medium: Internet*                        |             |
| entiality) |                                           |             |
+------------+-------------------------------------------+-------------+
| # JAN -    | Census 2000: Summary Population and       | # Places    |
| - NOV 2002 | Housing Characteristics                   |             |
|            |                                           |             |
|            | *Media: Internet, paper (printed report)* |             |
+------------+-------------------------------------------+-------------+
| 2003       | ####### Census                            | Places      |
|            |  2000: Population and Housing Unit Counts |             |
|            |                                           |             |
|            | *Media: Internet, paper (printed report   |             |
|            | with selected historical counts)*         |             |
+------------+-------------------------------------------+-------------+

Continued Attachment B: Census 2000 Data Products (2)

+--------------+------------------------------------------+------------+
| ## Planned   | ###                                      | **Lowest   |
|              |                                          | Level      |
| ##           | ### **Sample Data Products**             | G          |
| Release Date |                                          | eography** |
+--------------+------------------------------------------+------------+
| DEC 2001 --  | ###### Demographic Profile               | Places     |
| MAR 2002     |                                          |            |
|              | -   Demographic, social, economic, and   |            |
|              |     housing characteristics presented in |            |
|              |     three separate tables                |            |
|              |                                          |            |
|              | ######## Media: Internet, CD-ROM, paper  |            |
+--------------+------------------------------------------+------------+
| DEC 2001 --  | ###### Co                                | Con        |
| MAR 2002     | ngressional District Demographic Profile | gressional |
|              |                                          | Districts  |
|              | -   Demographic, social, economic, and   | of the     |
|              |     housing characteristics presented in | 106^th^    |
|              |     three separate tables for            | Congress   |
|              |     Congressional Districts only         |            |
|              |                                          |            |
|              | *Media: Internet, CD-ROM, paper*         |            |
+--------------+------------------------------------------+------------+
| AUG -- DEC   | **Summary File 3 (SF 3)**:               | Census     |
| 2002         |                                          | tracts     |
|              | -   Population counts for ancestry       |            |
|              |     groups.............................. | Block      |
|              |                                          | groups/    |
|              | -   Selected population and housing      |            |
|              |     characteristics....................  | Census     |
|              |                                          | tracts     |
|              | *Media: Internet, CD-ROM*                |            |
+--------------+------------------------------------------+------------+
| DEC 2002 --  | **Summary File 4 (SF 4)**:               | Census     |
| MAR 2003     |                                          | tracts     |
|              | -   Population and housing               |            |
|              |     characteristics iterated for many    |            |
|              |     detailed race and Hispanic or Latino |            |
|              |     categories, American Indian and      |            |
|              |     Alaska Native tribes, and ancestry   |            |
|              |     groups                               |            |
|              |                                          |            |
|              | ######## Media: Internet, CD-ROM         |            |
+--------------+------------------------------------------+------------+
| AUG 2002 --  | **Quick Tables**                         | Census     |
| MAR 2003     |                                          | tracts     |
|              | -   Table shells with population and     |            |
|              |     housing characteristics where the    |            |
|              |     user can specify a geographic area   |            |
|              |     and a population group               |            |
|              |                                          |            |
|              | ######## Medium: Internet                |            |
+--------------+------------------------------------------+------------+
| SEP 2002 --  | **Geographic Comparison Tables**         | Places of  |
| JAN 2003     |                                          | 1,000 or   |
|              | ###### Population and housin             | more       |
|              | g characteristics for a list of geograph | population |
|              | ic areas (e.g., all counties in a state) |            |
|              |                                          |            |
|              | *Medium: Internet*                       |            |
+--------------+------------------------------------------+------------+
| *For         | **Public Use Microdata Sample (PUMS)     | Public use |
| 5-percent    | Files**                                  | microdata  |
| sample:*     |                                          | areas      |
|              | -   5-percent sample (information for    | (PUMAs)    |
| APR -- JUL   |     state and sub-state areas)           |            |
| 2002         |                                          |            |
|              | -   1-percent sample (information for    |            |
| ######       |     metropolitan areas)                  |            |
| ## For 1-per |                                          |            |
| cent sample: | *Medium: CD-ROM*                         |            |
|              |                                          |            |
| AUG -- DEC   |                                          |            |
| 2002         |                                          |            |
+--------------+------------------------------------------+------------+
| DEC 2002 --  | ###### Advanced Query Function           | User       |
| MAR 2003     |                                          | defined    |
|              | -   User specifies contents of           | down to    |
| (Release     |     tabulations from full microdata file | census     |
| subject to   |                                          | tracts     |
| policy       | -   Includes safeguards against          |            |
| decisions on |     disclosure of identifying            |            |
| access and   |     information about individuals and    |            |
| conf         |     housing units                        |            |
| identiality) |                                          |            |
|              | ######## Medium: Internet                |            |
+--------------+------------------------------------------+------------+
| 2003         | Census 2000: Summary Social, Economic,   | Places     |
|              | and Housing Characteristics              |            |
|              |                                          |            |
|              | ########                                 |            |
|              |  Media: Internet, paper (printed report) |            |
+--------------+------------------------------------------+------------+
| 2003         | ######                                   | Census     |
|              | Congressional District Data Summary File | tracts     |
|              |                                          | within     |
|              | -   100-percent and sample data for the  | Congress-  |
|              |     redistricted 108^th^ Congress        | ional      |
|              |                                          | Districts  |
|              | ######## Media: Internet, CD-ROM         |            |
+--------------+------------------------------------------+------------+

**(Revised: 2/1/00)**

###### Attachment C

###### 

###### Examples of Relevant Web Sites

##### AMERICAN COMMUNITY SURVEY: [www.census.gov/acs/www/](http://www.census.gov/acs/www/)

Summarized data updated each year for areas; documentation for the
survey, including a questionnaire

**ANNIE E. CASEY FOUNDATION:** [www.ecf.org](http://www.ecf.org/)

The Foundation's mission is to help build better futures for millions of
disadvantaged children who are at risk of poor educational, economic,
social, and health outcomes.

**BUREAU OF THE CENSUS:** [www.census.gov](http://www.census.gov/)

Click on "American FactFinder" for data from censuses and surveys.

**BUREAU OF LABOR STATISTICS, LOCAL AREA UNEMPLOYMENT STATISTICS**:
[www.bls.gov/lauov.htm](http://www.bls.gov/lauov.htm)

CENTER ON URBAN POVERTY AND SOCIAL CHANGE, Case Western Reserve
University (contact: Claudia Coulton):
[http://povertycenter.cwru.edu](http://povertycenter.cwru.edu/)

Cleveland Area Network for Data Organizing, a data base that contains
neighborhood-level information from the 1990 census and a variety of
administrative data files.

CENTERS FOR DISEASE CONTROL AND PREVENTION:
<http://www.cdc.gov/scientific.htm>

**CHILD TRENDS:** [www.childtrends.org](http://www.childtrends.org/)

**CLEVELAND AREA NETWORK FOR DATA AND ORGANIZING (CAN DO**):
[http://povertycenter.cwru.edu](http://povertycenter.cwru.edu/)

Center on Urban Poverty and Social Change, Case Western Reserve
University. This is an interactive database of Cleveland neighborhoods
with mapping capabilities

COUNCIL OF PROFESSIONAL ASSOCIATIONS ON FEDERAL STATISTICS

<http://members.aol.com/copafs>

DC AGENDA: [http://www.dcagenda.org](http://www.dcagenda.org/)

Neighborhood Information Service assembles neighborhood-level data and
analyses to support community-based organizations.

DEMOGRAPHIC DATA VIEWER
<http://plue.sedac.ciesin.org/plue/ddviewer/ddv20/htmls/map-intro.html>

DEPARTMENT OF AGRICULTURE, Economic Research Service

<http://www.ers.usda.gov/rural>

Rural briefing room.

DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT, Office of Policy
Development and Research:

<http://www.huduser.org/datasets/pdrdatas.html>

Includes links to varous data sets associated with housing and urban
development such as the American Housing Survey and Low Income Housing.
Includes information such as annual adjustment factors to project-based
rent subsidies, assisted housing (national and aggregated information on
housing assistance at the local level, fair market rents, government
sponsored enterprise data, income limits, low-income housing tax credit,
property owners and managers survey, qualified census tracts and
difficult development areas, and section 8 administrative fees.

HUD's State of the Cities Data System:
<http://webprod.aspensys.com/socds/>

Contains data sets for tracking the conditions of America's cities.

DEPARTMENT OF JUSTICE

*NATIONAL INSTITUTE OF JUSTICE:*
[www.ojp.usdoj.gov/nij](http://www.ojp.usdoj.gov/nij)

*CRIME MAPPING RESEARCH CENTER:*
[www.ojp.usdoj.gov/cmrc](http://www.ojp.usdoj.gov/cmrc)

ECONOMIC DEVELOPMENT ADMINISTRATION:
[www.doc.gov/eda](http://www.doc.gov/eda)

Obtain their report on "Socioeconomic Data for Economic Development: An
Assessment" by Andrew Reamer and Joseph Cortright at:
[www.doc.gov/eda/pdf/socio.pdf](http://www.doc.gov/eda/pdf/socio.pdf)

ESRI for GIS mapping and software: [www.esri.com](http://www.esri.com/)

FEDSTATS: [www.fedstats.gov](http://www.fedstats.gov/)

Nationally available statistics produced by the federal government.

FLORIDA DEPARTMENT OF CHILDREN AND FAMILIES:

<http://www.state.fl.us/cf_web/district11>

Their GIS Research Services unit focuses on data collection, analysis,
neighborhood indicators and community mapping.

HEALTHY COMMUNITIES PROJECT, UNVERSITY OF TEXAS-HOUSTON, SCHOOL OF
PUBLIC HEALTH:
[www.sph.uth.tmc.edu/chis/background.htm](http://www.sph.uth.tmc.edu/chis/background.htm)

INTEGRATED DATABASE ON CHILDREN'S SERVICES IN ILLINOIS, CHAPIN HALL
CENTER FOR CHILDREN, UNIVERSITY OF CHICAGO:
[www.chapin.uchicago.edu](http://www.chapin.uchicago.edu/)

How children in Illinois use public social service programs. This data
base includes some longitudinal data and is being used as a national
standard for how to use administrative records for policy. Replication
is planned in California, Massachusetts, North Carolina, and Wisconsin.

MARYLAND GOVERNOR'S OFFICE FOR CHILDREN, YOUTH, AND FAMILIES

[www.ocyf.state.md.us/results.htm](http://www.ocyf.state.md.us/results.htm)

Tracks results and indicators of the well being of Maryland's children.

NATIONAL NEIGHBORHOOD INDICATORS PROJECT, URBAN INSTITUTE, WASHINGTON,
D.C.

<http://www.urban.org/nnip/index.htm>

The National Neighborhood Indicators Partnership (NNIP) is a
collaborative effort by the Urban Institute and local partners to
further the development and use of neighborhood-level information
systems in local policymaking and community building. All local partners
have built locally self-sustaining information systems with integrated
and recurrently updated information on Neighborhood conditions in their
cities. These systems facilitate the direct use of information by local
government and community leaders to build the capacities of distressed
urban neighborhoods.

PITON FOUNDATION, DENVER: [www.piton.org](http://www.piton.org/)

This is a private foundation whose mission is to provide opportunities
for children and their families to move from poverty and dependence to
self reliance. The Piton's Data Initiative for Denver has the user
friendly Neighborhood Facts, a web-based database of neighborhood
indicators, maps, and histories.

POPULATION ESTIMATES, Bureau of the Census:
[http://www.census.gov/population/www/estimates/](http://www.census.gov/population/www/estimates/geonotes/)

RAY MARSHALL CENTER FOR THE STUDY OF HUMAN RESOURCES (LBJ School of
Public Affairs, U. of Texas):
[www.utexas.edu/research/cshr/](http://www.utexas.edu/research/cshr/)

ROCKEFELLER FOUNDATION: [www.rockfound.org](http://www.rockfound.org/)

SAN DIEGO ASSOCIATION OF GOVERNMENTS:
<http://www.sandag.cog.ca.us/data_services/>

SANDAG maintains extensive databases of facts and figures from a
historical perspective, current information, and forecasted information.
The information encompasses population growth, housing, employment, and
income as well as

data on crime and the local economy.

SMALL AREA INCOME AND POVERTY ESTIMATES (SAIPE):

[www.census.gov/hhes/www/saipe/techdoc](http://www.census.gov/hhes/www/saipe/techdoc)

Detailed information about the SAIPE program is available on the Census
Bureau's web page.

UNIVERSITY OF MICHIGAN, Inter-University Consortium for Political and
Social Research (ICPSR): \< <http://www.icpsr.umich.edu/archive1.html>\>

Archive of computerized social science data.

UNIVERSITY OF WISCONSIN AT MADISON, INSTITUTE FOR RESEARCH ON POVERTY:
[www.ssc.wisc.edu/irp/](http://www.ssc.wisc.edu/irp/)

The Institute for Research on Poverty is a national, university-based
center for research into the causes and consequences of poverty and
social inequality in the United States. It is nonprofit and nonpartisan.

URBAN INSTITUTE:
[www.urbaninstitute.org](http://www.urbaninstitute.org/)

URBAN INSTITUTE'S NATIONAL NEIGHBORHOOD INDICATORS PARTNERSHIP:

[www.urbaninstitute.org/nnip/](http://www.urbaninstitute.org/nnip/)

The NNIP is a collaborative effort by the Urban Institute and local
partners to develop and use neighborhood-level information systems in
local policymaking and community building. The local partners have built
locally self-sustaining information systems with integrated and
recurrently updated information on neighborhood conditions in their
cities.

URBAN STRATEGIES COUNCIL, Oakland California:
[http://www.urbanstrategies.org](http://www.urbanstrategies.org/)

The data group used administrative records files and census files for
city reports and maps.

[^1]: Beginning in 2004, and every year thereafter, areas and population
    groups of 65,000+ will have annual averages. Beginning in 2006,
    places of 20,000 -- 65,000 will have 3-year average estimates
    updated every year; for areas of 20,000 or less, updated 5-year
    averages will start to be available in 2008.

[^2]: Available to researchers only under the rules of confidentiality
    and laws or agency practices.

[^3]: ^2^

[^4]: ^2^

[^5]: ^2^