i > JOINT HEARING ON H.R. 6 AND H.R. 1645: USES

OF CENSUS DATA TO DISHWTffi^EDERAL

t FUNDING

»

JOINT HEA1

BEFORE THE

SUBCOMMITTEE ON ELEMENTARY, SECONDARY, AND

VOCATIONAL EDUCATION

OF THE

COMMITTEE ON EDUCATION AND LABOR

AND THE

SUBCOMMITTEE ON CENSUS, STATISTICS, AND

POSTAL PERSONNEL

OF THE

COMMITTEE ON POST OFFICE

AND CIVIL SERVICE

HOUSE OF REPRESENTATIVES

ONE HUNDRED THIRD CONGRESS

FIRST SESSION

JOINT HEARING HELD IN WASHINGTON, DC, JULY 13, 1993

Education and Labor Com. Serial No. 103-37

Post Office and Civil Service Com. Serial No. 103-17

Printed for the use of the Committee on Education and Labor and the

Committee on Post Office and Civil Service

U.S. GOVERNMENT PRINTING OFFICE

75-540 cj WASHINGTON ! 1994

For sale by the U.S. Government Printing Office

Superintendent of Documents. Congressional Sales Office, Washington, DC 204(12

ISBN 0-16-043519-6
COMMITTEE ON EDUCATION AND LABOR

WILLIAM D. FORD,

WILLIAM (BILL) CLAY, Missouri

GEORGE MILLER, California

AUSTIN J. MURPHY, Pennsylvania

DALE E. KILDEE, Michigan

PAT WILLIAMS. Montana

MATTHEW G. MARTINEZ, California

MAJOR R. OWENS, New York

THOMAS C. SAWYER, Ohio

DONALD M. PAYNE, New Jersey

JOLENE UNSOELD, Washington

PATSY T. MINK, Hawaii

ROBERT E. ANDREWS, New Jersey

JACK REED, Rhode Island

TIM ROEMER, Indiana

ELIOT L. ENGEL, New York

XAVIER BECERRA, California

ROBERT C. SCOTT, Virginia

GENE GREEN, Texas

LYNN C. WOOLSEY. California

CARLOS A. ROMERO-BARCELO,

Puerto Rico

RON KLINK, Pennsylvania

KARAN ENGLISH, Arizona

TED STRICKLAND, Ohio

RON DE LUGO, Virgin Islands

ENI F. H. FALEOMAVAEGA,

American Samoa

SCOTTY BAESLER, Kentucky

ROBERT A. UNDERWOOD, Guam

Michigan, Chairman

WILLIAM F. GOODLING, Pennsylvania

THOMAS E, PETRI, Wisconsin

MARGE ROUKEMA, New Jersey

STEVE GUNDERSON, Wisconsin

RICHARD K. ARMEY, Texas

HARRIS W. FAWELL, Illinois

CASS BALLENGER, North Carolina

SUSAN MOLINARI, New York

BILL BARRETT, Nebraska

JOHN A. BOEHNER, Ohio

RANDY "DUKE" CUNNINGHAM, California

PETER HOEKSTRA, Michigan

HOWARD P. "BUCK" McKEON, California

DAN MILLER, Florida

PATRICIA F. RISSLER, Staff Director

JAY EAGEN, Minority Staff Director

SUBCOMMITTEE ON ELEMENTARY, SECONDARY, AND VOCATIONAL EDUCATION

GEORGE MILLER, California

THOMAS C. SAWYER, Ohio

MAJOR R. OWENS, New York

JOLENE UNSOELD, Washington

JACK REED, Rhode Island

TIM ROEMER, Indiana

PATSY T. MINK, Hawaii

ELIOT L. ENGEL, New York

XAVIER BECERRA, California

GENE GREEN, Texas

LYNN C. WOOLSEY, California

KARAN ENGLISH, Arizona

TED STRICKLAND, Ohio

DONALD M. PAYNE, New Jersey

CARLOS A. ROMERO-BARCELO,

Puerto Rico

DALE E. KILDEE, Michigan, Chairman

WILLIAM F. GOODLING, Pennsylvania

STEVE GUNDERSON, Wisconsin

HOWARD "BUCK" McKEON, California

THOMAS E. PETRI, Wisconsin

SUSAN MOLINARI, New York

RANDY "DUKE" CUNNINGHAM, California

DAN MILLER, Florida

MARGE ROUKEMA, New Jersey

JOHN A. BOEHNER, Ohio

(II)
MMITTEE ON POST OFFICE AND CIVIL SERVICE

WILLIAM L. CLAY, Missouri, Chairman

PATRICIA SCHROEDER, Colorado

FRANK McCLOSKEY, Indiana

GARY L. ACKERMAN, New York

THOMAS C. SAWYER Ohio

PAUL E. KANJORSKI, Pennsylvania

ELEANOR HOLMES NORTON, District of

Columbia

BARBARA-ROSE COLLINS, Michigan

LESLIE L. BYRNE, Virginia

MELVIN L. WATT, North Carolina

ALBERT RUSSELL WYNN, Maryland

GREG LAUGHLIN. Texas

SANFORD D. BISHOP, Jr., Georgia

SHERROD BROWN, Ohio

ALCEE L. HASTINGS, Florida

JOHN T. MYERS. Indiana

BENJAMIN A. GILMAN, New York

DON YOUNG, Alaska

DAN BURTON, Indiana

CONSTANCE A. MORELLA, Maryland

THOMAS J. RIDGE, Pennsylvania

THOMAS E. PETRI, Wisconsin

SHERWOOD L. BOEHLERT, New York

JIM SAXTON, New Jersey

GAIL E. WEISS, Staff Director

ROBERT E. LOCKHART, General Counsel

DORIS MOORE-GLENN, Deputy Staff Director

JOSEPH A. FISHER, Minority Staff Director

SUBCOMMITTEE ON CENSUS, STATISTICS AND POSTAL PERSONNEL

THOMAS C. SAWYER, Ohio, Chairman

FRANK McCLOSKEY, Indiana THOMAS E. PETRI, Wisconsin

ALBERT RUSSELL WYNN, Maryland THOMAS J. RIDGE, Pennsylvania

TERRIANN LOWENTHAL, Subcommittee Staff Director

(in)

CONTENTS

Hearing held in Washington, DC, July 13, 1993

Statement of:

Bl!^'fWilAia,n P

',A??0SJate Director for Demographic Programs, Bureau

ot the Census U.S. Department of Commerce, Washington, DC; Hon.

fc-merson J. Elliott, Commission of Education Statistics, U.S. Department of Education^ Washington, DC; and Patricia Ruggles, Senior Research Associate, The Urban Institute, Washington , DC 4

Prepared statements, letters, supplemental materials, et cetera:

Bute, Wiiham P., Associate Director for Demographic Programs, Bureau

I M

nsus'U

£- Department of Commerce, Washington, DC, pre- pared statement of g

Center for the Study of Educational Finance, Normal,'IL,'prepared s'tete^ ment ot >«--»•

Department of Education,Was'hm'gton" DC,''prep^r^'stetement of.'.'".".'.";.'.'.".' 85 td^•s

-

Hoi>- Don a Representative in Congress from the State of

California, prepared statement of 45

blliott, Hon. Emerson J., Commission of Education Statistics, U.'s" Dfr

TJ^TI °A ^"Cf1100. Washington, DC, prepared statement of 16

imobersteg Gail, Director, Federal Liaison Office, California Department

ot Education, prepared statement of 46

Northeast-Midwest Congressional Coalition, Washington, DC, prepared

statement of ....•...._.... 49

Payne, Hon. Donald M., a Representative in"Congress from thei State of

New Jersey, prepared statement of 45

bcarr, Harry A., Acting Director, Bureau of the Census, Washington, DC,

answers to questions, submitted by 47

(v)

JOINT HEARING ON H.R. 6 AND H.R. 1645: USES

OF CENSUS DATA TO DISTRIBUTE FEDERAL

FUNDING

TUESDAY, JULY 13, 1993

HOUSE OF REPRESENTATIVES,

SUBCOMMITTEE ON ELEMENTARY,

SECONDARY, AND VOCATIONAL EDUCATION,

COMMITTEE ON EDUCATION AND LABOR, AND

SUBCOMMITTEE ON CENSUS, STATISTICS,

AND POSTAL PERSONNEL,

COMMITTEE ON POST OFFICE AND CIVIL SERVICE,

Washington, DC.

The subcommittees met, pursuant to call, at 10:05 a.m., Room

2175, Rayburn House Office Building, Hon. Dale E. Kildee, presiding.

Members present: Representatives Kildee, Sawyer, Unsoeld,

Reed, Roemer, Becerra, Green, Woolsey, Payne, Gunderson, and

McKeon.

Staff present: Subcommittee on Elementary, Secondary, and Vocational Education: Susan A. Wilhelm, staff director; S. Jefferson

McFarland, III, legislative counsel; Jack Jennings, general counsel,

full committee; Diane Stark, legislative specialist, full committee;

and Jane Baird, minority counsel; Subcommittee on Census, Statistics, and Postal Personnel: TerriAnn Lowenthal, staff director;

Chris Collins, staff assistant; and George Conant, minority staff assistant.

Chairman KILDEE. The Subcommittee on Elementary, Secondary,

and Vocational Education convenes this morning for a joint hearing with the Subcommittee on Census, Statistics, and Postal Personnel. I am pleased to be chairing this hearing jointly with Chairman Sawyer•we can cut the gavel in half, if you wish. His work

as the Chairman of the census subcommittee is very well known. It

is an added bonus that Mr. Sawyer is Chairman of the census subcommittee and a valued member of this subcommittee as well.

Our topic this morning is the availability and quality of census

data used in Federal funding formulas. Chairman Sawyer has introduced legislation that would direct the Census Bureau to biannually update the count of poor children aged 5 through 17. Clearly, such data would go a long way in the distribution of Chapter 1

funds which currently occurs every 10 years as new census data becomes available. We will also discuss efforts to obtain data on the

number of poor children by school districts. As members are aware,

current law provides that the Chapter 1 basic grant formula is

(1)
used to count the number of poor children by school districts if

that data is available. In the absence of such data, county poverty

counts are used.

Previous attempts to obtain district level numbers have not resulted in data that could be used to make allocations at the national formula. The formula dates back to a woman by the name of

Mrs. Orshansky, I believe. When I first came to Washington 17

years ago, the Orshansky formula•that term was used a great

deal•was part of the lore of the distribution of these funds.

Which department did she work for?

Ms. RUGGLES. Social Security.

Mr. ELLIOTT. HEW, Social Security, and now HHS.

Chairman KILDEE. Okay•and now HHS.

At any rate, whenever we talk about Chapter 1 or distribution,

we generally refer to Mrs. Orshansky on this.

At this point, I would like to recognize Chairman Sawyer for his

opening statement.

Mr. SAWYER. Thank you, Mr. Chairman.

I worry any time you say "at any rate." That has been part of

the problem.

I am particularly pleased to join with you this morning. The

work that we have done together since I have been in Congress has

been perhaps as instructive as any that I have had the opportunity

to do since I have been here. The nexus of the issues that bring us

here today really goes to the heart of how well we provide assistance not only in education but a number of other arenas.

Today we are looking at the question, as you said, of the availability of data on poverty both from the census every 10 years and

in between. I am hopeful that as a result of the extraordinary testimony that we will hear today, we will come to better understand

how those estimates are produced, the quality of that data, and

what potential we have for producing estimates that are more

timely in the future.

I want to emphasize that this isn't just an academic exercise.

Poverty data are used to distribute some $20 billion annually in

Federal funding and drive distributions within States of dollar

amounts that go well beyond that. The interest of this committee

is, I think, indicative of the broad policy consequences of the full

range of Federal statistical programs.

Right now, the only source of reliable poverty data for small

places is the decennial census. It is the clear perception of large

numbers of members of this committee and others throughout the

country that the infrequent production of that data undermines

the ability of many Federal programs to reach their target populations effectively.

In the school year that just ended this past spring, the Chapter 1

grant program was funded using poverty data that reflected 1979

income data. Those numbers are 14 years out of date. If we tried to

fund the defense budget based on data that was as removed from

current situations as that is, we would simply be laughed off of the

world stage.

Chairman KILDEE. There are some, however, who would still try

to fund the Defense Department as it was during the height of the

Cold War.
3

Mr. SAWYER. I don't even want to get into that, Mr. Chairman.

When we talk about these data, they aren't just old, they are ancient, they are ancient history.

The change that we are going through right now is profound. We

are talking about data that may look precise by virtue of its geographic detail. But it is not just enough for the data to look precise.

If the numbers aren't timely, then to the degree that they fail to

capture change, they simply aren't accurate.

Change is a dominant characteristic of our time. If the census is

occurring only once a decade, we are failing to capture what is one

of the most important phenomena of our time, and that is change

itself. That is, as much as anything, the reason I introduced the

measure that we are reviewing today. It would require the Bureau

to produce and publish estimates down to the school district level

every two years. It would require separate tabulations of school-age

children.

Contrary to some of the things that have been suggested, this is

not a simple task. It clearly is going to require further research

and refinement of technique. Perhaps even an independent review

of selected methodology would be helpful. But it is also clear that

more frequent measurements are long overdue. If we can do them

and do them affordably, then we ought to do them. There is just

too much at stake with the policy that they drive. It would not only

help the Congress target the dollars that we appropriate, but it

would help us to understand and measure the programmatic success or failure of those programs that are driven by that kind of

data.

In any case, I am enormously grateful, Mr. Chairman, not only

to you and to Mr. Goodling for having this hearing, but to the Subcommittee on Commerce, Justice, State, and Judiciary for including $600,000 in their 1994 funding bill to begin research and development on an intercensal poverty estimates program.

So, again, Mr. Chairman, thank you, and I look forward to our

testimony today.

Chairman KILDEE. And thank you, Mr. Chairman.

When I was going to school, there was emblazoned on the wall

the statement, 'Knowledge is power," and knowledge should lead

us to fairness in this area too, so I think the more knowledge we

have, the more fair we can be to the students out there for whom

this, particularly Chapter 1, was designed. So no one should be

afraid of the truth. We should be seeking the truth so we can be

fair.

Mr. SAWYER. Mr. Chairman, I have attached to my scale in my

bathroom the saying, "What you measure is what you get."

Chairman KILDEE. Mr. McKeon.

Mr. MCKEON. It is hard to top that.

Thank you, Mr. Chairman. I would like to thank you and Chairman Sawyer for providing the opportunity today to consider H.R.

1645, the Poverty Data Improvement Act of 1993. I would further

like to commend Chairman Sawyer for his work on this bill. I am

proud to be a cosponsor of this important legislation.

Yearly, billions of Federal dollars are allocated based on poverty

data. By updating the poverty data every two years, as this bill requires, we will be most efficiently and effectively using these Fed-
eral dollars for their intended purpose. Currently, poverty figures

are taken from the decennial census.

My State of California is one of many in which the population

changed drastically during the past 10 years. For example, the

number of school children from low-income families increased 38.2

percent, or by 247,162 children, between 1980 and 1990. Since 1990

census data will not be factored into the Chapter 1 formula until

the 1993/1994 school year, past year allocations were based on 12-

year-old data. This translated in a loss of over $126 million for California in the 1992/1993 school year. This amount of funding can

make a big difference to schools and in the individual lives of children who benefit the most from these dollars.

This legislation will ensure not only updated but timely poverty

data as well. To achieve the goals we have in mind when we distribute these dollars, we must use accurate poverty figures. I urge

my colleagues to listen closely to the testimony today and to give

this legislation serious consideration.

Thank you, Mr. Chairman.

Chairman KILDEE. Thank you, Mr. McKeon.

Our witnesses this morning are Mr. William Butz, associate director for demographic programs, the Bureau of the Census, U.S.

Department of Commerce; the Honorable Emerson Elliott, commissioner of education statistics, U.S. Department of Education; and

Ms. Patricia Ruggles, senior research associate, the Urban Institute, Washington, DC.

Mr. Butz, you may begin.

STATEMENTS OF WILLIAM P. BUTZ, ASSOCIATE DIRECTOR FOR

DEMOGRAPHIC PROGRAMS, BUREAU OF THE CENSUS, U.S. DEPARTMENT OF COMMERCE, WASHINGTON, DC; HON. EMERSON

J. ELLIOTT, COMMISSION OF EDUCATION STATISTICS, U.S. DEPARTMENT OF EDUCATION, WASHINGTON, DC; AND PATRICIA

RUGGLES, SENIOR RESEARCH ASSOCIATE, THE URBAN INSTITUTE, WASHINGTON, DC

Mr. BUTZ. Thank you, Mr. Chairman; and thank you also, Mr.

Chairman. It is really a great pleasure for me to be here on behalf

of the Census Bureau, and I am honored to be here personally not

only appearing before this joint meeting of the two subcommittees

but also having the opportunity to appear with two very highly respected and esteemed colleagues.

I have submitted written testimony that I would appreciate being

put in the record.

Chairman KILDEE. Yes. Without objection, that will be included

in its entirety.

Mr. BUTZ. Thank you, sir, and I will summarize it rather quickly

and then be very happy to try to answer any questions you may

have.

In the first part of these remarks, I will provide an overview of

some current activities at the Census Bureau that relate directly to

updating 1990 decennial census estimates of poor schoolchildren,

and this overview will then provide the background for the second

part of my testimony which covers technical issues and concerns
5

that arise in launching a program to meet the requirements of

H.R. 1645.

Currently, we are working in three areas that bear directly on

these questions. The first involves development of specialized poverty tabulations for school districts directly from the 1990 census;

the second is the ongoing population estimates program, that we

have been doing for a number of years, that updates decennial

census population counts for some geographic areas every year, or

every two years; and the third is an effort to develop a program for

updating 1990 decennial census base estimates of income and poverty, a program that is directly related to the bill under question.

In the first area, we have nearly completed work with the Department of Education to develop estimates of poor school-age children for each of the approximately 15,000 school districts in the

country. This effort has yielded two benefits directly related to the

requirements of H.R. 1645. First, we have generated poverty estimates for 1989 for school districts, and these estimates are based

directly on data from the 1990 decennial census.

Second, working with the Department of Education, we have developed a process that assigns census households to the appropriate

1989/1990 school districts. This process of allocating households to

school districts would be necessary under the legislation.

In the second area, we have in place an ongoing program to provide updates of the decennial census population counts for States,

counties, and a variety of governmental jurisdictions. Annually, we

produce estimates of population for States and counties, and for jurisdictions below the county level we make such estimates of total

population on a biennial basis, but we have no system for making

population estimates for school districts, a matter that I will return

to in a moment.

Evaluations of our estimation methodologies indicate that it is

very difficult to make accurate population estimates for small

areas•that is, for example, those areas of under 5,000 total population. Our preliminary evaluation of estimates for 1990 based on

comparisons with the 1990 decennial census•that is, taking our estimates for 1990 and comparing them with what actually emerged

from the census•shows that for areas having populations under

5,000 in 1980, the magnitude of the estimation error for about half

of these areas was larger than the actual population change between 1980 and 1990. That is, for very small areas the error in our

estimation methods was actually larger than the change over the

entire decade, which calls into question the accuracy and the precision of such data for very small areas.

In the third area that I mentioned, we have attempted to establish an income and poverty estimates program to parallel the population estimates program. This project would be undertaken by the

Census Bureau as reimbursable work for specific Federal users of

these data. In this effort, we have contacted Federal departments

and agencies that use income or poverty estimates for purposes of

allocating funds, and based on our meetings with these representatives we developed a plan for providing income and poverty estimates that would meet most of their requirements. The plan would

be implemented in two phases: first, a phase to develop and test
methodologies; and, second, a phase for producing ongoing estimates on a biennial basis.

Let me now turn directly to the proposed legislation and the

issues that can affect our ability to meet the requirements set out

in it. First let me emphasize two basic elements needed to fulfill

the requirements of estimating the number of poor children by

school district. First, we must have estimates of the number of children and the number of families with children by school districts,

estimates that we do not now currently produce; and, second, we

must have estimates of the income distribution of such families.

These are the two basic elements needed to produce the estimates

of the number of schoolchildren in poverty in a geographic area.

Population estimates for school districts are an indispensable

component in the process of updating decennial census counts of

poor children. To meet this need, we would have to expand significantly our current population estimates program. We would need

to develop a method for estimating the number of schoolchildren

for each school district. We have never made postcensal estimates

of population by age for areas below the State level, so that you

can see this is a substantial increase in the amount of geographic

detail that would be necessary from the State level all the way

down to the school district level.

To even attempt population estimates of children by age at the

school district level, we would require expanded access to tax

return and Social Security information. This would likely include

receiving the Social Security numbers for all dependents on tax returns, that information being needed to then obtain information on

the age of these children.

The poverty status of children is based on the total income of the

family in which they reside. Therefore, to meet the legislative requirements, we must develop estimates of the income distribution

for families having school-age children for each school district. The

most promising method for estimating family income is based heavily on data from individual income tax returns from the Internal

Revenue Service. But using tax returns presents some problems

that must be overcome.

For example, not all families file tax returns, especially those

with small amounts of income or income from nontaxable sources,

and of course those tend to be the families that are of interest

when one is examining the poverty population. We must therefore

develop methods that bring together the tax information with

other data from other sources that better reflect the situation in

the very lowest portion of the family income distribution. These

sources include local area data on numbers of families receiving

nontaxable cash and noncash government benefits•that is, information from administrative record sources for these programs.

I do want to emphasize that we have never attempted to update

family income or poverty estimates for detailed levels of geography.

There are many unknowns involved, as Chairman Sawyer mentioned a moment ago, in such an undertaking, and we should not

assume that all of the problems can be solved in a thoroughly satisfactory manner.

I want to close by mentioning four particular issues that are pertinent to our ability to meet the requirements of the proposed bill.
The first has to do with school district size. As I have mentioned,

our past experience with population and per capita income estimation indicates that the accuracy and therefore the usefulness of the

estimates declines as the size of the district or the geographic area

that we are trying to estimate declines.

Unfortunately for this purpose, most of the 15,000 school districts

across the country are very small. About 22 percent of all school

districts, I understand, contain fewer than 250 students, and about

74 percent enroll less than 2,500. I doubt very much that we will be

able to produce reliable income and poverty estimates for districts

this small.

The second is access to tax return data. We have used this in the

past for our per capita income estimates, and we will need it; in

fact, we will need increased access to such data under the provisions of the bill. But it is my understanding that Internal Revenue

Service regulations permit us to receive these files only to support

projects which we are directly authorized to conduct and for which

we receive direct appropriations under our own title.

The third issue involves our geocoding capabilities•that is, the

capabilities of placing households and families and people on the

right spot of the Earth, that is, in the right school districts, so that

we can associate the right income measure with the right numbers

of people. At present, our system for geocoding tax returns to geographic areas does not include coding to the school district level.

We hope to upgrade our coding system to provide this capability in

a general overhaul of our current methodology that we hope to proceed with, independent of this bill, during this decade. But we don't

know now if all of those efforts will be successful, and in any case

this new system will not be in place until 1996.

The final issue is the timing of the release of estimates. The bill

calls for the release of the first updated poverty estimates in 1995.

As we require 15 to 18 months of research and development and

additional time after that to implement estimation methods based

on the findings of these investigations, I think it would be very difficult for us to meet such a deadline. If we began this fall, I think

we could meet a 1996 deadline and that we might be able to

produce 1993 data on that mark rather than the 1991 data that is

proposed, and then we would catch up over time.

In closing, we anticipate that the small size of most school districts will prove to be an inherently limiting factor in our ability to

provide reliable updates of the 1990 census estimates of poor

school-age children. Moreover, the methodological and data access

issues that must be resolved, even for large geographic areas, are

formidable. However, we are optimistic about our ability to make

poverty estimates, as specified in the legislation, for counties and

areas having populations of 50,000 or more. If the law specifies

smaller areas, obviously we will do our best to produce estimates

for smaller areas. In summary, I am emphasizing that for areas of

50,000 or more we are confident now, although we do not know exactly the procedures that would be used, that we would be able to

do that. For areas less than 50,000, we are less confident. Obviously, we would make our best efforts if called upon to do so.

That concludes my testimony. I will be happy to try to answer

any questions.
8

Thank you.

[The prepared statement of Mr. Butz follows:]

STATEMENT OF WILLIAM P. BUTZ, ASSOCIATE DIRECTOR FOR DEMOGRAPHIC PROGRAMS,

BUREAU OF THE CENSUS, U.S. DEPARTMENT OF COMMERCE

INTRODUCTION

Chairman Sawyer and Chairman Kildee, it is a pleasure to be here today to testify before this joint subcommittee hearing on the Census Bureau's population and

poverty estimates programs and on the proposed bill H.R. 1645, the "Poverty Data

Improvement Act of 1993." In the first part of my presentation I will provide an

overview of the current availability of poverty data from the 1990 decennial census

and our existing efforts to provide updates of both population and poverty counts for

States, counties, and local governmental jurisdictions. This overview will provide

the context for the second part of my presentation in which I will focus on the technical issues and concerns that arise in launching a program to meet the requirements of H.R. 1645. I will lay out the steps required in updating decennial census

poverty estimates during the 1990s as outlined in H.R. 1645 and the issues that can

affect the accuracy and timing of such estimates. While the bill specifies poverty

estimates for virtually all governmental jurisdictions, most of my comments relate

to the production of estimates for school districts as these areas are the most problematical from a methodological perspective.

As you will see from my testimony, this legislation calls for research that is not at

all easy. There are tremendous hurdles to cross to get a system in place for school

districts. Unfortunately, crossing these hurdles is not inexpensive or quick, and we

are not at all confident that the estimates we would produce for the smallest of

these areas would be any better than the estimates currently being used from the

1990 census.

CURRENT STATUS AND PROGRAMS

Currently, we are working in three areas that bear directly on development of a

program to update 1990 decennial census estimates of the number of poor schoolchildren. The first involves development of specialized poverty tabulations for school

districts directly from the 1990 decennial census. The second is the ongoing population estimates program that updates decennial census population counts annually

for States and counties, and biennially for smaller governmental units. The third is

a Census Bureau effort to develop an income and poverty estimates program for updating decennial census-based estimates for similar levels of geography.

Specialized Poverty Tabulations

We have nearly completed work with the Department of Education to develop estimates of poor school-age children for each of the approximately 15,000 school districts in the country. This effort has yielded two benefits related to the requirements of H.R. 1645.

First, we have generated "baseline" poverty estimates for 1989 for school districts

directly from the 1990 decennial census files. I use the term baseline because any

efforts to update poverty estimates during the 1990s will require these data as

input.

Second, we have developed a process that assigns census households to the appropriate 1989-1990 school district. We worked closely with the Department of Education in order to enhance our capabilities in this area. For its part, the Department

of Education provided detailed maps defining the boundaries of school districts in

relation to standard "census" geographic areas [blocks]. The cost of the whole effort,

borne by the Department of Education, was in excess of $6 million.

Population Estimates Program

We have in place an ongoing program to provide updates of the decennial census

population counts for States, counties, and a variety of governmental jurisdictions.

On an annual basis, we produce estimates of population for States and counties. The

estimates for States are made for persons by sex within single-year age categories.

The estimates for counties are restricted to toal population with no detail on demographic groups. For jurisdictions below the county level [cities, incorporated places,

townships, and functioning minor civil divisions], we make estimates of total population on a biennial basis. At the present time we have no system for making popula-
9

tion estimates for school districts and have only begun rsearch on methods for providing population estimates by age for areas below the State level.

Our population estimation methods rely heavily on administrative records provided by the Internal Revenue Service in the form of tax returns and on data from the

Social Security Administration and the Health Care Financing Administration.

These data are geocoded using our geographical classification system and merged

with data from previous tax years, the previous decennial census, and records on

births and deaths to arrive at updates of the decennial census figures.

We conduct periodic evaluations of our estimation methodologies and have found

that our estimates for small areas•those places of under 5,000 population•are subject to large errors. For these areas, we see average error levels over a 10-year time

span of between 15 and 20 percent. Our preliminary evaluation of estimates for 1990

based on comparisons with the 1990 decennial census shows that, for areas having

populations under 5,000 in 1980, the magnitude of the error in our estimate for

about half of these areas was larger than the actual population change between

1980 and 1990. Thus, this rate of accuracy is only slightly better than simply using

the previous decennial census count for the entire decade.

We have set forth an ambitious plan to improve our postcensal population estimates program during the 1990s. This includes the addition of county population

estimates by broad age groups and improved methods for assigning geographic codes

[geocoding] to the tax return information that is so vital to the estimation process

Census Income and Poverty Estimates Program

During the past two years, we have attempted to establish an income and poverty

estimates program for updating decennial census estimates during the 1990s. This

project would be undertaken by the Census Bureau as reimbursable work for specific Federal users of these data. In this effort, we have contacted Federal departments

and agencies that use income or poverty estimates for purposes of allocating funds

or for other program requirements. This consortium of users expressed a wide range

of needs. Based on our meetings with representatives of these organizations, we developed a plan for providing income and poverty estimates that could meet most of

their needs.

We have devised a plan that would be implemented in two phases. Since we have

never attempted to develop postcensal estimates of household median income or

poverty rates for persons as required by members of our consortium, the first phase

in our plan would be to test the feasibility of making such estimates. We anticipate

a testing period of 15-18 months during which we will examine alternative estimation techniques based on a small sample of States and large counties. If we are successful in our estimation for these large areas, we will extend our research to smaller geographic areas in order to test the relationship between the population size of

an area and the accuracy of our estimates. As we have ample evidence from our

population estimates program that the accuracy of postcensal population counts for

small areas during the 1980s was poor, we must conduct a thorough investigation to

understand how the accuracy of income and poverty estimates is affected by population size.

The second phase of our plan to provide updates of 1990 decennial census income

and poverty estimates calls for implementation of the results of our research and

development work. Guided by these results we would construct a system for producing estimates on a regular, biennial basis. While the ultimate goal of our proposal is

to produce estimates for all States, counties, cities, and other incorporated places,

we may well not be able to achieve this goal for small areas.

As of this date there are two issues that we must resolve before we can begin

work on this reimbursable project. First, we are discussing details regarding the financial contributions that each consortium member will make to funding the

project. Second, we are negotiating with the Internal Revenue Service to obtain

access to income data contained on their Individual Master File of individual

income tax returns. The tax return information is the most important source of

data available for estimating the distribution of family income for small geographic

areas. I will discuss this critical issue in more detail later.

MEEETING REQUIREMENTS OF THE LEGISLATION

Given this background, I will now turn more directly to the proposed legislation

and the issues that can affect our ability to meet the requirements set out in H.R.

1645.

There are three main elements specified in this legislation. First, it calls for the

production and publication of estimates for the number of children aged 5 to 17
10

years old living in families with income below the poverty level. Second, it requires

that these estimates be made for all States, counties, local govenmental jurisdictions, and school districts. Third, it specifies that these estimates be made at intervals of not less than two years beginning in 1995.

I will begin by describing the basic elements needed to fulfill the requirements of

estimating the number of poor children ages 5 to 17 in school districts. These are [1]

estimates of the number of children and the number of families with children by

the age of the children, and [2] estimates of the income distribution of such families.

I will complete my presentation by pointing to various important issues that must

be resolved in developing a system to provide these estimates.

Estimates of Families With Children by Age

Population estimates for school districts are an indispensable component in the

process of updating decennial census counts of poor children. To meet this need, we

would be required to expand significantly our current population estimates program. We would need to develop a method for estimating the number of children

ages 5 to 17 in grades served by each school district. As mentioned earlier, our current plans call only for development of estimates of population by broad age groups

at the county level. We would also need to initiate a program to develop estimates

of families with children by age of the children because the official measure of poverty is based on the level of family income. We have no experience in making postcensal estimates of the number of families for small areas.

To attempt estimates of children by age at the school district level, we would require expanded access to, and use of, administrative records. This would likely include receiving the social security numbers for all dependents on tax returns. A possible alternative approach might make use of the data on school enrollments collected by the National Center for Education Statistics. Such an approach would have to

take into account the number of children enrolled in private schools [about 10 percent nationally], a figure that varies widely among school districts. At present we

have no evidence concerning the feasibility of such an approach.

Estimates of Family Income

The poverty status of persons is based on the total income of the family in which

they reside. Therefore, we must develop estimates of the income distribution for

families having school-age children, for each school district. The appropriate poverty

thresholds by size of family can then be used to determine the number of families

and number of children in poverty.

While there are other methods that might be used to derive the family income

distributions, we think that the most promising are based heavily on Federal individual income tax returns. The advantages of tax returns include [1] ability to geocode tax return addresses to census geographic areas, [2] coverage of a very large

proportion of the population, [3] coverage of a very large proportion of the income

received by families, and [4] availability as a data source on an annual basis.

Using tax returns also presents some problems that must be overcome. Most notably, not all persons file tax returns, especially those with small amounts of income

or income from nontaxable sources. In addition, income defined on tax returns does

not include all sources used in the official determination of poverty status and tax

filing units are not wholly consistent with our definition of families.

We must, therefore, develop methods that bring together the tax information with

other data that reflect the situation in the lowest portion of the income distribution.

These sources include local area data on numbers of families receiving nontaxable

cash and noncash government benefits. Cash benefits include Aid to Families With

Dependent Children and Supplemental Security Income. Noncash benefits include

food stamps and free and reduced-price school lunches. While the value of noncash

benefits is not counted as income in the official measurement of poverty, participation in these programs is a good indicator of the number of poor. Most of these data

are not available for school districts. Hence, our challenge would be to find methods

to use the tax and benefit data in ways that lead to estimates of the entire income

distribution for all areas needed.

I must reemphasize at this point that we have never attempted to update poverty

and income estimates for detailed levels of geography. There are many unknowns

involved in such an undertaking and we should not assume that all of the problems

can be solved in a satisfactory manner.

Additional Issues

There are additional issues that are pertinent to estimating the number of families and children, and family income distributions. I will discuss them now.
11

Three of these issues concern the physical characteristics of school districts. These

are [1] the small size of school districts, [2] the quality of the information used to

code census households to school districts, and [3] the overlapping nature of some

school district boundaries.

1. School district size. Our past experience with population and per capita income

estimation for small geographic areas shows that the accuracy of our estimates is

directly and inversely correlated with the size of the geographic area for which we

are making estimates. This relationship also applies to the poverty estimates derived directly from the 1990 decennial census and is revealed in the confidence

limits bounding each estimate.

Here are two typical examples. According to the 1990 Census, there were 4,053

children in the grades and areas served by the Highland Park City Schools of

Wayne County, Michigan on April 1, 1990. Using data from the long form, collected

on a sample basis, the census estimated that 2,003 of these children were living in

families with income below the poverty level for 1989. The width of the 95 percent

confidence interval for this estimate is 500 children, ranging from 1,705 to 2,301.

The second example from the Coventry Public School system of Summit County,

Ohio [1,800 school-age children] shows an estimate of 175 poor children in 1989 and

a 95 percent confidence interval 184 children wide•between 83 and 267.

These wide confidence limits on our estimates of poor children reflect large sampling variability because these estimates are based on the Census sample and the

size of the sample is proportional to the size of the area. Given the degree of uncertainty associated with the census estimates, we can anticipate significant problems

with estimates from update methodologies as well.

Unfortunately for this purpose, most of the 15,000 school districts across the country are very small. About 22 percent of all school districts contain fewer than 250

students and about 74 percent enroll less than 2,500. I doubt very much that we will

be able to produce reliable income and poverty estimates for those districts.

2. Quality of the school district boundary data. The quality of school district

boundary data is very important to the overall quality of population and poverty

estimates. We have experienced some problems in assigning census households to

grades in school districts. These problems occur mainly in situations where the

school district boundary "split" a census block. Better methods need to be established for allocating the children to the appropriate school district.

We would also need to develop an ongoing system to update the geocoding system

for changes in the boundaries of school districts. If such work is not undertaken, our

estimates could reflect only the situation as of the 1989-1990 school year. Our information indicates that school district boundaries change for between 100 and 200

areas each year.

3. Overlapping school district boundaries. School district boundaries sometimes

overlap and these overlaps may complicate the estimation process. This overlap affects about 25 percent of the total number of school districts. In these cases of overlap, children in the same family may attend school in different school districts depending on their grade level. We must therefore devise methods that account for

these overlaps when computing estimates of poor children.

Finally, I have four other concerns that I would like to mention: [1] access to the

tax return data needed in the estimation process, [2] enhancements needed in our

geocoding system, [3] need for a formal estimates review process, and [4] completion

of the first round of estimates in 1995.

1. Access to tax return data. As I mentioned earlier when discussing our small

areas income estimates proposal, we will need access to an extract of information

from the Internal Revenue Service's Individual Master File of all Federal individual

income tax returns. This will permit us to geocode these returns to school districts

and to test various estimation strategies. It is our understanding that Internal Revenue Service regulations permit us to receive this file only to support projects which

we are directly authorized to conduct and for which we receive direct appropriations.

Although H.R. 1645 would amend section 182 of Title XIII, U.S. Code, to authorize

the Bureau of the Census to produce and publish poverty estimates for school districts, the Congress may decide to provide funding for this work to another agency,

such as the Department of Education. As we are not permitted to use the tax return

data to perform work under reimbursable agreements with other agencies, we may

be denied access to the data for the purposes specified in the bill. Our ability to

make the estimates requested in an efficient and timely manner could be diminished under such an arrangement.

2. Geocoding capabilities. At present our system for geocoding tax returns to geographic areas does not include coding to the school district level. We hope to up-
12

grade our coding system to provide this capability in a general overhaul of our current methodology. The new system will provide this capability for areas having citystyle street addresses but will not be able to assign school district codes for areas

where the addresses are not city-style. We do not anticipate that this new system

will be in place before 19%. Until that time, we will be limited to current procedures that can assign codes covering States, counties, and other incorporated places.

3. Establishment of a review and challenge process. Because the poverty estimates

will be used to allocate funds, we will need to establish a system that permits school

districts and other stakeholders to review our procedures and challenge the validity

of our estimates. Such a review and challenge process was an integral part of our

efforts to provide population and per capita income estimates for the General Revenue Sharing program in the 1970s and early 1980s. For that program we received

between 500 and 1,000 formal challenges of our estimates annually. Given the anticipated difficulty in making poverty estimates for school districts and the levels of

funding involved, we expect that a formal review process could generate a much

larger volume of challenges.

4. Timing of the release of estimates. The bill calls for the release of the first updated poverty estimates in 1995. As we would require 15-18 months of research and

development and additional time to implement estimation methods based on the

findings of our investigations, it would be very difficult for us to meet such a deadline unless work on the program could begin within the next several months.

CLOSING

We anticipate that the small size of most school districts will prove to be an inherently limiting factor in our ability to provide reliable updates of the 1990 decennial census estimates. Moreover, the methodological and data access issues that

must be solved in order to meet the requirements of this legislation, even for large

geographic areas, are formidable. However, we are optimistic about our ability to

make poverty estimates as specified in the legislation for counties and areas having

populations of 50,000 or more.

That concludes my testimony. I will be happy to answer any questions.

Chairman KILDEE. Thank you very much.

Mr. Elliott.

Mr. ELLIOTT. Thank you very much, Chairman Sawyer and

Chairman Kildee. I have a prepared statement that has been submitted, if I could have that in the record, and I will do a summary.

Chairman KILDEE. Yes, it will be included in its entirety.

Mr. ELLIOTT. Thank you very much.

To complement the things that Mr. Butz has said, I would like to

address my comments this morning to five questions in particular:

Why were the school district estimates developed to begin with?

How are the estimates developed? How are the estimates used in

the Chapter 1 program, Chapter 1 being the largest Federal assistance to elementary and secondary education? How does the reliability of the estimates affect the distribution of Chapter 1 funds?

And then, finally, how might the estimates be improved that are

used for allocating Chapter 1 funds?

First, why were the school district estimates developed to begin

with? As a part of the 1988 Hawkins-Stafford Elementary and Secondary School Improvement Amendments, Congress mandated that

the National Center for Education Statistics submit a report after

each decennial census on the social and economic status of children

who reside in local education agencies, and Mr. Butz has just described a part of that process. The estimates of related children 5

to 17 years in poverty families by school district is needed for the

Chapter 1 program, and it is one of several products from this overall project.
13

There are, I think, at least four reasons that these kinds of estimates are needed. One is that, in fact, the data are used as a basis

for allocating Federal funds, including funds in Chapter 1. Second,

there is a within-State use of the data, so that the Federal Government, if it allocates down to counties, still provides additional information through the school district data so that States can use it to

allocate among the school districts within a county within their

own State. Third, things do change from one decennial census to

the next decennial census, and they need to be brought up to date.

And finally, Mr. Kildee•I think my recollection is correct on

this•there were a number of States, led by Michigan actually, in

1987 that were very much concerned when it looked as though the

National Center for Education Statistics and the Bureau of the

Census would not be able to provide the decennial census data by

school district as they had for 1970 and 1980. Michigan and a group

of other States were very intent on using the data for within-State

planning purposes. It was very important to them, and they made

sure the Congress understood that in 1987 when our legislation was

under consideration.

Second, how were these estimates developed? The Census

Bureau, in conducting the 1990 census, used two questionnaires to

enumerate households. They used a short form containing basic demographic and housing information for most households in the

country. In addition, they used a longer form with many more

questions, including questions on income, for a sample of households. Estimates of children ages 5 to 17 years and families with

income below poverty are developed from households that received

the long form or the sample questionnaire.

Third, how are the estimates used in the Chapter 1 program?

The Department of Education allocations for Chapter 1 are computed on the basis of county level data. States are responsible for

making suballocations down to the school district level, and States

use a variety of data to make the district allocations. Some States

use the census estimates of children in poverty to allocate among

districts. Others use items such as the number of children in the

school lunch program or the AFDC children to allocate funds

among the individual school districts within a county. These latter

items have an advantage in that they can be kept current. States

also have the responsibility for making corrections and adjustments to the data to reflect boundary changes in school districts,

such as consolidations.

Fourth, how does the reliability of the estimates affect the distribution of Chapter 1 funds? When the Census Bureau, the National

Center for Education Statistics, or other statistical agencies release

estimates to the public, they typically provide a discussion of

sources of error that might affect the data as well as their suitability for various purposes. These discussions usually differentiate two

kinds of error: sampling error and nonsampling error or bias. Nonsampling error that may affect these estimates can occur as a

result of several different kinds of problems. For example, the district boundaries may have been drawn incorrectly, or a digitizing

error may have occurred when the Census Bureau computerized

the boundaries of the paper maps. Another example: districts that
14

have a higher proportion of their population in split blocks within

the census count will have lower reliability.

But for most districts sampling error is probably the more significant source of error affecting the estimates. As noted earlier, these

estimates are derived from a sample that was part of the 1990 decennial census. Nationwide, one out of every six households was

asked the income questions that are used to determine poverty

status. For the 1990 census, the Census Bureau used higher sampling rates for small governmental units to increase the reliability

of the estimates for those areas. The minimum sample size was one

in eight for tracts in large cities, and the maximum was one in two

for small incorporated towns. Because school districts were not considered in the decisions about sampling, the effective sampling rate

for school districts ranges from one extreme to the other. Small

school districts, however, tend to be associated with small towns or

rural areas that were sampled at a higher rate.

We believe that most of the challenges of the 1990 census estimates made by States and school districts reflect estimates that are

strongly affected by sampling error. Typically, they would be cases

where the estimate is significantly lower than the true value. Such

cases have a low probability of occurring. The fact that they do

occur, however, is one of the shortcomings of using estimates from

probability samples for the purpose of distributing money to small

jurisdictions.

Poor children in small districts are much more likely to receive

too little or too much Chapter 1 money than are poor children in

large districts because the sampling variability in the estimates is

greater in small districts. Although this misallocation of funds can

have a significant effect on the program of an individual district, it

will likely have a negligible effect on the overall targeting of Chapter 1 funds. This is because the vast majority of cases that have a

large percentage error will occur in the small districts and therefore the error will affect relatively few children.

Fifth, how might the estimates be improved? Probably the best

procedure is one that was used by the Census Bureau to improve

the estimates of per capita income for the general revenue sharing

program. The per capita income estimates from the decennial

census had the same problem that we face here•that is, large sampling errors for small jurisdictions. The procedure, called JamesStein estimates, is a method of incorporating additional information into the estimation process, especially information with little

sampling errors, so that the resulting new estimates are closer to

the true value than the original statistics. Thus, application of the

James-Stein procedure could result in considerable improvement in

the estimate.

Information such as the number of children receiving free or reduced-price lunch or the number of AFDC children could be a good

predictor of the number of children in poverty even if they do not

measure precisely the same thing. NCES is currently discussing

the possible application of this procedure to develop more accurate

estimates with several States.

A second way of improving the estimates would be to update

them for the changes that occur over time. Although it would be

most useful if the Census Bureau could develop periodic estimates
15

for all counties and districts, as proposed in Congressman Sawyer's

bill, the Bureau's testimony indicates that updates of the district

data would be of questionable quality. The use of updated estimates

for counties would go a long way toward keeping Chapter 1 allocations current with changing conditions. County updates of children

in poverty would permit updating the allocations for States, and

county estimates could also be useful as controls in State efforts to

update children in poverty estimates.

States, perhaps working with the National Center for Education

Statistics, may be in a better position than the Census Bureau to

make such updates since they are able to make use of local and

State sources of data as well as national sources. If the county allocations are updated, I would recommend encouraging States to explore the feasibility of developing such updates for their districts.

The national Center for Education Statistics would be happy to

provide technical assistance to the States as needed. We would be

prepared to answer your questions.

Thank you, Mr. Chairman.

[The prepared statement of Mr. Elliott follows:]
16

Statement of Emerson J. Elliott

Introduction

Chairman Sawyer and Chairman Kildee, thank you for the

opportunity to testify at this joint subcommittee hearing. In

response to the stated focus of the hearing and discussions with

Committee staff, I will limit my remarks to five aspects of the

development and use of the 1990 Census estimates of children in •

poverty for the Chapter 1 allocations. The five aspects are:

Why were the school district estimates developed?

How were these estimates developed?

How are the estimates used in the Chapter 1 program?

How does the reliability of the estimates affect the

distribution of Chapter 1 funds?

How might the estimates be improved?

Why were the school district estimates developed?

As part of the 1988 Hawkins-Stafford Elementary and Secondary

School Improvement Amendments,P.L.100-297, Congress mandated

NCES to "submit a report to the appropriate committees of the

Congress concerning the social and economic status of children

(based on data from the most recent decennial census) who reside

in areas served by different local educational agencies". The

estimates of related children 5-17 years in poverty families by

school district, needed for the Chapter 1 program, is one of

several products of this overall project. For the purposes of

this testimony, my discussion is limited to this use of the data.

How were these estimates developed?

The Census Bureau, in conducting the 1990 census, used two

questionnaires to enumerate households. They used a short form,

containing basic demographic and housing information, for most

households in the country . In addition, they used a long form

or sample questionnaire, which comprised the short form items as

well as many others•including questions on income• for a sample
17

of households. Estimates of children ages 5-17 years, in

families with income below poverty, are developed from households

that received the long form (sample) questionnaire.

The process used to develop the estimates for school districts

involved numerous steps:

1. State officials drew school district boundaries on maps

supplied by the Census Bureau.

2. The Census Bureau "digitized" the boundaries. This

process creates computer-readable school district

boundaries.

3. The Census Bureau assigned all households with children

ages 5-17 years, which completed a 1990 census sample

questionnaire, to a school district as follows:

a. Households were classified by block in the census.

b. Blocks wholly contained within a district were

assigned to that district.

c. Blocks with a school district boundary running

through them were split into two ( or more) parts.

Households in the split blocks were allocated to

the respective school districts based on

specifications provided by the State. Typically,

this was done proportionally to land area.

4. NCES determined the grade range for each school

district based on information provided by the State as

part of NCES's 1989-1990 Common Core of Data

collection.

5. The Census Bureau determined the grade for each child,

5-17 years, based on the respondents responses to the

census question on highest grade completed.

6. Children were classified as "relevant" for a particular

school district if they lived in a housing unit within

the district's boundaries and their current grade was

within the district's grade range.
18

7. Relevant children were classified as "in poverty" if

their family's pre-tax cash income, as reported on the

sample questionnaire, was below the official poverty

threshold for that size and type (e.g. one parent, two

parents) of family.

8. The Census Bureau developed a "weight" for each person

in the sample such that the sum of the weights was

equal to the total census count. The Census estimate

of children 5-17 years of age in poverty families is

therefore equal to the sum of the weights of such

children in poverty families in the sample.

How are the estimates used in the Chapter 1 program?

School district level data are not used by the Department of

Education to calculate Chapter 1 allocations for States.

Departmental allocations are computed on the basis of county

level data. States are responsible for making sub-allocations

down to the school district level, and States use a variety of

data to make the district allocations.

Some states use the Census estimates of children in poverty to

allocate to districts. Others use items, such as the number of

children in the school lunch program or AFDC children, to

allocate the funds to individual school districts. These later

items have an advantage in that they can be kept current. States

also have the responsibility of making corrections and

adjustments to the data to reflect boundary changes,

consolidations, etc.

How does the reliability of the estimates affect the distribution

of Chapter 1 funds?

when the Census Bureau, NCES, or other statistical agencies

release estimates to the public, they typically provide a

discussion of sources of error that might affect the data as well
19

as their suitability for various purposes. These discussions

usually differentiate two kinds of error•sampling error and nonsampling error or bias. Non-sampling error that may affect these

estimates can occur as a result of several different kinds of

problems:

First, the district boundaries may have been drawn incorrectly

or a digitizing error may have occurred when the Census Bureau

computerized the boundaries from the paper maps.

Second, districts that have a higher proportion of their

population in split blocks will have less reliability. This

source of error was much diminished for 1990 compared to 1980

because of the much smaller block size in 1990. There were 7

million blocks in 1990 vs. 258,000 in 1980.

Third, there was also less error in assigning children to

districts in 1990 in situations where there are overlapping

districts. For 1990, the assignment of children to elementary

vs. secondary districts was made using the child's enrollment

grade rather than the child's age, as was done in 1980. The use

of age is known to introduce a bias favoring secondary districts.

For example, in 1980, 14-year-olds were typically allocated to

secondary districts even though at the time of the census about

one half of 14-year-olds were still in the 8th grade and were the

responsibility of an elementary district.

Finally, there are various errors associated with the taking of

the census. For example, the census may fail to count some

people or respondents may fail to report their income correctly.

On balance, respondents to Census surveys report less income than

administrative records show they receive. This underreporting of

income is especially common among households at the high and low

ends of the income distribution. Combined with the practice of

excluding from poverty calculations the considerable amounts of

noncash income households receive, this underreporting of pre-tax
20

cash income would tend to increase the number of poor children

aged 5-17 in the sample. We have no reason to think that

underreporting of cash income by low-income households would be

different in small districts than in large. So, the effect of

income underreporting probably would be independent of the

sampling errors discussed below.

For most districts, sampling error is probably the most

significant source of error affecting the estimates. As noted

earlier, these estimates are derived from a sample that was part

of the 1990 Decennial Census. Nationwide, one out of every six

households was asked the income questions that are used to

determine poverty status. For the 1990 Census, the Census Bureau

used higher sampling rates for small governmental units to

increase the reliability of the estimates for those areas. The

minimum sample size was 1 in 8 for tracts in large cities and the

maximum was 1 in 2 for small incorporated towns. Because school

districts were not considered in decisions about sampling, the

effective sampling rate for school districts ranges from one

extreme to the other. Small school districts, however, tend to

be associated with small towns or rural areas that were sampled

at a higher rate.

As a result of sampling, the estimates are subject to random

variability inherent in all sampling processes. For that reason,

the estimate may be too high or too low compared to the "true

value" that would have been obtained if all households had been

asked the income questions. In order to provide a basic

understanding of this random component of error, I will describe

the situation where this type of error is most likely to be

serious, that is, in very small school districts. In this

example, let us assume that there are 100 school districts, each

having an enrollment of 300 children, of whom 20 percent, or 60

children, are in poverty. If all households in a census were

asked the income questions, the Census would estimate 60 children

in poverty for each district. If, however, only one in three

6
21

households were asked the income questions, the estimates would

vary from district to district. According to statistical theory,

about one half of these districts will have estimates within 15

percent of the true value (i.e. between 51 and 69), and about

two-thirds will have estimates within 22 percent of the true

value (i.e. between 47 and 73). On the other hand, the estimates

for the remaining one third of the districts will be more than 22

percent from their true value and about 5 percent will be off the

nark by over 50 percent (i.e. less than 30 or greater than 90).

As districts increase in size, this distribution becomes narrower

and the estimates ,on average, become more reliable.

We believe that most of the challenges of the 1990 Census

estimates made by States and school districts reflect estimates

which are strongly affected by sampling error. Typically, there

would be cases where the estimate is significantly lower than the

true value. As we have shown above, such cases have a low

probability of occurring. The fact that they do occur, however,

is one of the shortcomings of using estimates from probability

samples for the purpose of distributing money to small

jurisdictions. Poor children in small districts are much more

likely to receive too little or too much Chapter 1 money than are

poor children in large districts, because the sampling

variability in the estimates is greater in small districts.

Although this misallocation of funds can have a significant

effect on the program of an individual district, it will likely

have a negligible effect on the overall targeting of the Chapter

1 funds. This is because the vast majority of cases that have a

large percentage error will occur in small districts and,

therefore, the error will affect relatively few children.

Table 1 shows the size distribution of school districts in the

U.S. Overall, about 50 percent of all districts have less than

1000 students, and they account for a little over 7 percent of

all students attending public school. The smallest 25 percent of

all districts have less than 300 students. All of these districts
22

combined, however, enroll only 1.3 percent of all school

children. Thus, it would appear that the use of the census

estimates provides a "correct" distribution of funds overall, but

that a significant number of small exceptions occur. In the next

section, I will discuss the question of what might be done about

these exceptions.

How might the estimates b« iigprqvad?

As indicated above, sampling error is the major source of error

for most of the school district estimates. In a way, this is

fortunate because there are techniques that can be used to reduce

this error. Probably the best procedure is one that was used by

the Census Bureau to improve the estimates of per capita income

(PCI) for the General Revenue Sharing Program. The PCI estimates

from the decennial census had the same problem that we face here-

-large sampling errors for small jurisdictions.

The procedure, called James-Stein estimates, is a method of

incorporating additional information into the estimation process-

-especially information with little sampling error•so that the

resulting new estimates are closer to the true value than the

original statistics. The procedure has its largest effect on

estimates with large sampling error. The amount of the

improvement depends upon how good the additional information is

in predicting the true value for each district. In our case,

information, such as the number of children receiving free or

reduced price lunch or the number of AFDC children, could be a

good predictor of the number of children in poverty even if they

do not measure precisely the same thing. Thus, application of

the James-Stein procedure could result in considerable

improvement in the estimates. NCES is currently discussing the

possible application of this procedure to develop more accurate

estimates with several States.
23

A second way of improving the estimates would be to update them

for changes that occur over time. The Census Bureau's testimony

indicates that periodic updates for all districts, as proposed in

H.R. 164S, would be of questionable quality. Although district

level updates would be useful, they are not critical, in my

opinion.

The use of updated estimates for counties would go a long way

toward keeping Chapter 1 allocations current with changing

conditions. County updates of children in poverty would permit

updating the allocations for States. In addition, updated county

controls could be carried down to districts for States that use

items like children in the school lunch program when making subcounty allocations to districts.

County estimates could also be useful as controls in State

efforts to update children in poverty estimates. States, perhaps

working with NCES, may be in a better position than the

Census Bureau, to make such updates since they are able to make

better use of local and state sources of data. If the county

allocations are updated. I would recommend encouraging States to

explore the feasibility of developing such updates for their

districts. NCES would be happy to provide technical assistance

to the States as needed.

I would like to thank the Committees for this opportunity to

testify and will be pleased to answer any questions you may have.
24

Chairman KILDEE. Thank you very much.

Ms. Ruggles.

Ms. RUGGLES. Thank you. I am happy to be here today to discuss

the provision of updated income and poverty estimates for geographic areas at the State level and below.

I plan to address three main questions: Do we actually need

more frequent poverty estimates? Is it feasible to do more frequent

poverty estimates for smaller geographic areas? And what data and

other resources would be needed in order to undertake this kind of

estimate? Although these questions arise in considering H.R. 1645,

the Poverty Data Improvement Act of 1993, I plan to focus on the

general issues involved rather than on the specific provisions of

that bill. I will, of course, be happy to answer any questions you

may have.

Poverty estimates are used for a number of different purposes.

For example, they may be used to track the relative well-being of

different demographic groups within the population, to assess and

compare the impacts of various economic trends over time, and, not

least, to allocate funds under specific Federal programs to aid the

poor.

The Census Bureau currently produces two major types of poverty estimates that are used for these purposes. First, estimates of

the total number of poor in the United States as a whole are produced every year using data from the Current Population Survey, a

survey of about 60,000 households. These estimates provide the

basis for the poverty rate figures that Census releases every year.

Although the CPS is quite large as sample surveys go, it doesn't

include enough households to be representative for each State individually. Reasonably reliable estimates of poverty rates can be

made from this survey for some of the larger States. For smaller

States, however, the survey does not collect information on enough

different households in each State to provide an accurate picture of

income and poverty status for the State population as a whole.

Estimates for counties and other jurisdictions below the State

level generally cannot be made using CPS data. Instead, poverty estimates for jurisdictions at the State level and below are produced

using data from the decennial census.

The decennial census collects information on many more families

than does the CPS, and, as you know, every household in the country returns a census form or is supposed to return a census form.

Estimates of income and poverty status are computed using the

long form returns, which are completed by about one household out

of six. The census long form collects the basic information on

sources of family income and on household composition that are

needed to determine each family's poverty status. In all, about 15

million long forms were completed for the 1990 decennial census.

This very large sample of returns allows income and poverty estimates to be calculated for even very small geographic areas, including counties, cities, and towns.

The major advantages of the decennial census are its very large

size and its comprehensive coverage of all areas of the country. Its

major disadvantage, of course, is the fact that it is performed only

once every 10 years. As we know from the CPS and other annual

surveys, poverty rates can fluctuate quite a bit from year to year.
25

Roughly 30 percent of the people who are poor in any given year

will not be poor the next year, but new families will be entering

poverty instead.

Across the Nation as a whole, differences in the numbers of

people leaving and entering poverty lead to changes in the overall

poverty rate. When there are more exiters than entrants, the rate

goes down, and when there are more entrants than exiters, it goes

up. Even at the broad national level, poverty rates are seen to fluctuate by as much as two to three percentage points within a fairly

short period of time. Between 1989 and 1991, for example, the overall poverty rate rose from 12.8 percent to 12.42 percent of the U.S.

population. This represented an increase of four million in the

number of people in poverty. So that is a pretty big change over a

two-year period.

These changes in poverty rates are not equally distributed across

all geographic areas. One region of the country may be particularly

hard hit by a recession, for example, while another is experiencing

a relatively high rate of job growth. Other factors, such as the migration of poor people within an area or the influx of poor immigrants into the area, can also affect local poverty rates. Over a

period of 10 years, local jurisdictions may experience widely varying changes in their poverty rates.

Because local area poverty statistics are computed from the decennial census, they are available only once every 10 years, and because it takes some time to collect and process data on income and

population, these estimates are typically already a few years out of

date when they become available. For example, the most recent set

of estimates which relates to family incomes received in 1989, just

became available for local jurisdictions this year. Without some

special effort to update these estimates, they will be in use from

now until approximately the year 2003.

These estimates are already somewhat out of date. The past four

years have seen some substantial changes in income and poverty

rates for the Nation as a whole, and these changes have affected

different jurisdictions quite differently. It is not hard to imagine

that by the year 2003 the distribution of poor people across different jurisdictions within the United States could be very different

from what it was in 1989. Allocation of funding for Chapter 1 and

similar programs will not necessarily go to the jurisdictions with

the largest poverty problems in the year 2003 if these allocations

are based on the geographic distribution of poverty that we saw in

1989. Basing allocation formulas on data that are this outdated

seems neither efficient nor fair.

What alternatives could be explored to improve the quality of

our income and poverty estimates for local areas? Because there

are large differences in levels of need across different counties,

cities, and so forth within the United States, it does seem that it is

necessary to use some kind of indicator of local area income in deciding how to allocate funds. The use of data based on the 1989 distribution of needs allows for very precise targeting of funds, but

unfortunately that precision may not be closely related to actual

needs and, in fact, is likely to become less accurate as time goes on.

Can these 1989 income and poverty estimates be updated in some

meaningful way before the next decennial census?
26

As Mr. Butz explained, the Census Bureau does update its estimates of the populations of specific jurisdictions, such as States,

counties, and cities, between decennial censuses. Using data from

tax and Social Security records as well as vital statistics reports,

updated estimates of the total population are produced for States

and counties every year and for subcounty jurisdictions every other

year. These estimates rely on baseline population counts from the

decennial census, but more recent data are used to project trends

in population since the census within each specific jurisdiction.

These estimates do not contain a high level of detail. State estimates are available by age and gender, and only population totals

are available for most other jurisdictions. Nevertheless, these estimates are substantially more accurate for reasonably large jurisdictions•over 50,000 in population, for example•than estimates

based on the decennial census alone would be. Because the tax and

wage records used in updating are not comprehensive, however, estimates for very small jurisdictions, which may not be well represented in the available records, are substantially less accurate.

Currently, Census does not attempt to track year-by-year

changes in income and poverty status across jurisdictions at the

State level and below. Recently, however, the Bureau has proposed

using a methodology similar to that used to update the population

estimates to produce updated income and poverty statistics for specific jurisdictions. Such a procedure would be feasible.

The same tax and Social Security records that are used to update

the population estimates also contain information on incomes. This

income information could help to identify trends in income and

poverty status over time across different local jurisdictions. If

wages were stagnating and unemployment was high in a particular

area, for example, the impacts of these problems would be clear in

the tax and Social Security data, and that information could be

used in updating the poverty estimates.

Conversely, areas where wages were high and jobs plentiful could

also be identified. Trends in poverty among those not in the labor

force•for example, the elderly and the disabled•are harder to

track using tax and wage data, but these data could be supplemented with information from other sources, such as the food stamp

program.

As with the updated poverty estimates currently performed by

Census, only the year-to-year trends would be tracked across jurisdictions using the administrative data. Baseline estimates of

income and poverty status would still need to be calculated from

the decennial census. These estimates would simply be updated between censuses using other sources of data. When new decennial

census data became available, the estimates would need to be rebenchmarked for each jurisdiction in much the same way that the

population estimates are currently recalibrated. Nevertheless, as

with the population estimates, the updated income and poverty estimates would almost certainly prove to be more accurate for jurisdictions over 50,000 in population than are estimates based on the

decennial census alone.

After reviewing the available data, in other words, I believe it

would be both useful and feasible to update income and poverty estimates for States, counties, and other jurisdictions over 50,000 in
27

population on a regular basis between decennial censuses. What,

however, should we do about jurisdictions with populations below

50,000?

The decennial census provides very detailed information on geographic areas, and reasonably reliable income estimates can be

made using these data for jurisdictions as small as 5,000 in population in the year of the decennial census. Such a jurisdiction would

typically be represented by more than 800 long form returns in the

decennial census, enough to get a representative picture of the

income distribution. Unfortunately, no other source of data available to the Census Bureau contains such a wealth of information

on the incomes received within such small jurisdictions, however.

Updating income and poverty estimates requires a relatively

large sample from each jurisdiction covered because it is necessary

to have representative records across many different income categories if the updates are to be accurate. The Census Bureau's own

studies have shown that they cannot reliably estimate even population changes between decennial censuses for places below 5,000 in

population because there just isn't enough detailed information

available.

Because updating income and poverty estimates is more complex

than updating population estimates and requires even more detailed data, it is very unlikely that accurate and reliable estimates

can be produced for smaller jurisdictions. School districts, for example, typically have populations of less than 5,000 and are therefore not good candidates for individualized income and poverty updates.

What are the implications of this problem for local area poverty

estimates? The fact that we cannot produce specific update factors

for smaller jurisdictions doesn't mean we can t use any data at all

on these jurisdictions, it just means that the best estimate of the

change in income and poverty rates experienced in these jurisdictions is likely to be the change observed for the larger jurisdiction,

such as counties, cities, and towns, of which they are a part.

Because we do have baseline data from the decennial census on

relatively small jurisdictions, including many school districts,

major differences in their original distributions of income can be

taken into account when considering funding allocations. In updating those estimates, however, the best we can do between censuses

is to project income and poverty status assuming that the direction

and magnitude of change is similar to that seen in the county or

the city as a whole. Attempting more detailed updates than this

might increase precision, but it would do little to increase the accuracy of the estimates.

In conclusion, I would just like to say that the decennial census

long forms are our major source of information on incomes and

poverty status. These census data are very comprehensive and provide a lot of geographic detail, and they do allow us to make more

accurate estimates of income and poverty status across States,

counties, and other jurisdictions. However, they do become fairly

outdated pretty fast, especially in times of rapid economic change.

A systematic series of updates between the decennial censuses

could greatly improve the accuracy of these estimates, especially

for jurisdictions over 50,000 in population.

75-540 0-94
28

The Census Bureau's proposed methodology for performing such

updates appears feasible and appropriate. Because it relies heavily

on administrative records that are already being collected for other

purposes, it would also be reasonably cost effective, although some

increase in funding would be needed to implement this program.

Given the importance of the issue and the large sums of money allocated using income and poverty data, however, such a funding increase would be a worthwhile investment. I commend the Bureau

and these two subcommittees for your proposals to improve the

quality and the timeliness of these data.

Chairman KILDEE. Thank you very much, all of you, for your testimony.

This committee will be reauthorizing Chapter 1 this year, and we

find a number of difficulties. Without getting into all of them, some

are political, some are statistical, some are fiscal, there are a variety. We may have to try to massage those in the fairest way possible. States in the Northeast, particularly States like Massachusetts

and Rhode Island, seem to have had this situation when the census

was taken. The good news was, they didn't have as much poverty.

The bad news was, that drove relatively fewer dollars to those

States for Chapter 1. In Michigan, on the other hand, the bad news

was, we had a lot of poverty. The good news was, that drove relatively more dollars to the State of Michigan. Those are some of the

things we have to wrestle with, and those are more political matters.

But it is important for us in the next decade, and maybe even

this year since we are somewhat removed yet in time from when

the census was taken•it is important for us to have at our disposal

as up-to-date data as possible so we can make sure that the purpose

of Chapter 1 is carried out, that we actually help kids.

What would the additional cost be to develop mid-decade poverty

estimates for counties?

Mr. BUTZ. Mr. Kildee, let me try that one. In order to give you a

precise estimate, we would really have to take a look at the numbers and get back to you, but the methodology that would be required is the same•the development of methodology is the same

whether one were going to do it once or do it every other year.

So I think part of the answer is that for the first two years or so

of development it really doesn't matter, and the bill has funding in

it for that, and if that bill were passed we would proceed on that

basis. In order to produce production numbers•that is, at the

middle of the decade•would require somewhat more; how much

more I don't know at the moment, but we could work up an estimate•that is, the actual annual amount to produce on a production basis.

Chairman KILDEE. But when you are doing it for a specific purpose, to determine poverty, you could zero in on that part of the

data you are gathering.

Mr. BUTZ. Yes, sir.

Chairman KILDEE. SO it would not be a question of replicating

the entire census over again, you would be looking for some particular

Mr. BUTZ. NO, that is correct. The update would be done not on

the basis of taking another census but on the basis of using infor-
29

mation from the previous census•in this case, the 1990 census•in

combination with information from various administrative record

systems•for example, the tax data, AFDC case records and the

like•in order to update the information, but it wouldn't require

additional primary data collection of a census sort.

Chairman KILDEE. Very good.

Mr. Sawyer.

Mr. SAWYER. Thank you, Mr. Chairman.

Mr. Butz, you talked at one point in your testimony about the

lack of confidence in data for small areas, under roughly 5,000. You

spoke at another point in terms of your confidence in the ability to

produce estimates over 50,000. What do we do in between?

Mr. BUTZ. Well, that 50,000 cut-off I know sounds very arbitrary,

but it is an attempt to come up with an approximate number. That

is the level above which, based on our research, we are very confident that we can produce good data•that is, data that are definitely better as the decade proceeds than using information from the

last census. That 5,000 cut-off was just simply an illustration. At

that cut-off, about half of the very small jurisdictions for which we

do population estimates had more error than the 1980 to 1990

change.

As you go up from 5,000 to 50,000, in general, overall, the quality

of the estimates improves, but it isn't until you get to about 50,000

that we are confident that we will be able to develop measures that

will produce better information. So the 5,000 is terrible; the 50,000

we are confident will be good; and below 50,000 we are really not

sure that it will be better for enough areas, whether it be counties

or school districts, to make the effort worthwhile.

Mr. SAWYER. Which is it more important to get the numbers

right for, the largest number of school districts in the country,

which would be below 5,000, or the largest number of schoolchildren in the United States, who are in districts above 5,000?

Mr. BUTZ. Well, I can't answer the question of which would be

most important, but certainly if we do the estimates for areas

above 50,000, most of the children are in larger school districts, so

that the distribution of children by school districts is very different

from just the distribution of school districts. I don't know, in fact,

what proportion of children are in very large school districts, but

certainly a much larger proportion are than the proportion of such

school districts of the total.

Mr. SAWYER. Mr. Elliott supplied a table that is awfully interesting in those terms. I would commend it to all of the members of

the committee. It really places in perspective that relationship between districts and children.

Let me ask you this, because this is really where I am leading,

and perhaps more than one of you could comment on this, about

the potential to merge in similar areas, in areas of over 50,000,

like-structured districts with demographic and economic similarities that you could measure on a fairly large scale, and to develop

from those merged territories greater accuracy in terms of intercensal projections and estimates than you could simply by using

the originally developed data alone.

Was that a clear question?
30

Mr. BUTZ. Yes, sir, and certainly that would be a help. The

larger the district, whether it comes from combining small ones or

whatever, in general, the greater the accuracy of the estimates.

Ms. RUGGLES. A variance on that would be simply to use data for

the town or the city in which the school districts were located as

the basis for updating the estimates. You would still use the basic

school district base to get a baseline for what the poverty level

looked like in different places, but in updating that you would just

assume that there was about the same level of economic growth

throughout the town or throughout whatever the jurisdiction was.

Mr. SAWYER. I can appreciate that. I am always concerned,

though, about using averages over a fairly large area where there

are, in fact, very substantial differences in terms of local jurisdictions within the larger county. I know of a number of members of

this committee who experience phenomena of that kind.

Let me ask just a couple of very quick questions. You mentioned

the problem of nontax filers. What other kinds of administrative

records would serve as useful proxies in trying to improve those estimates for nonfilers?

Mr. BUTZ. I think the kinds of data that we would look at would

be AFDC rolls, food stamp rolls, case rolls of children who participate in free or reduced-price school lunches, that kind of information, to try to get at the segment of the population that is most

likely to be in poverty and whose families are least likely to file

IRS returns. Which sources we would actually use and how we

would combine or model them would be a question for research,

but it would be those kinds of data that would be used, and they

would indeed be very, very important because we can't just rely on

IRS tax return data to do this job.

Mr. SAWYER. Mr. Chairman, I have really only one more. I don't

want to monopolize the time.

Mr. Elliott, I understand that the tabulations for school-age children by school district won't be completed until 1994 or 1995. We

have received a number of inquiries about that. I was wondering if

you could comment on that.

Mr. ELLIOTT. We are expecting the data to be available by October of this year, Mr. Chairman.

Mr. SAWYER. Is that correct?

Mr. ELLIOTT. That should be available for all districts in all

States by that time.

Mr. SAWYER. That is good news.

Mr. ELLIOTT. May I pick up just a bit on the question that you

were posing earlier about, should we try to cover more districts or

more students?

Mr. SAWYER. I would be pleased to have you comment.

Mr. ELLIOTT. That is a sort of question that I think a statistical

agency can't answer, but I would like to make a comment about it,

and that is, to some extent the issue is pivotal around what kinds

of other data are available that might be used to go to some level

below the county and whether or not those data are up to date and

satisfactory.

Now we have used extensively in States information on school

lunch and AFDC, but that judgment about whether that is a satisfactory substitute really has to be a political judgment.
31

Mr. SAWYER. Mr. Elliott, I hope you understand that the purpose

of the question was to get at the capacity to provide appropriate

mergers for districts for which you might find other symptomatic

measures or proxies, whether it be any of those that have been

mentioned, or race, or ethnicity, or any of the others that would

allow merger of like areas into sufficient size to provide the level of

confidence that you need to drive dollars based on that data.

Mr. ELLIOTT. Right.

Mr. SAWYER. Thank you.

Thank you, Mr. Chairman.

Chairman KILDEE. Thank you.

Mr. McKeon.

Mr. MCKEON. Thank you, Mr. Chairman.

I tend to be kind of simple-minded and try to make things simpler rather than more complicated. I was on a school board for a

number of years, and we had a lot of data on our children, and we

had to furnish that to the county and to the State to receive Federal funding and State and county funding. What if we just went to

the schools and asked them for the data? If we went to the State,

they have to already have that information to receive the funds.

Why don't we do that instead of going through this other complicated process?

In other words, they have the information on each child already.

What about getting it from them instead of from the census?

Mr. BUTZ. As I understand it, Congressman, that is a possible

source of part of the information that we need, but it wouldn't provide all of the information.

If we, for example, could not use Internal Revenue Service data

for one reason or another in this effort, one possibility would be to

use those data in combination with others. My understanding is,

though, that that would not give us as complete a picture as would

be obtained by using the various data sources together.

Mr. MCKEON. It may not be as complete, but by the time we wait

several years to get complete data we have lost several children

through the process anyway. So we could use that to go back and

check. But maybe the data that they have on a daily basis to collect ADA and other sources, even though it may not be as accurate,

might be more timely and possibly could be used more to achieve

the desire that Chairman Sawyer is working for.

Mr. BUTZ. Could I see if Mr. Elliott, who knows more about those

data than I do•would you like to add anything?

Mr. ELLIOTT. I would like to add a comment with regard to the

availability of information at the school level about poverty. There

is information provided by parents when children apply for the

school lunch program. The question is, how consistent is that

across districts and from one State to another? There is not a general measure available always in every school of poverty of parents, and we don't request that information except for the school

lunch program.

So I think there would be a question with regard to information

from that sort of a source unless all of us worked very hard to

make sure that the information was provided on a consistent basis

from school to school and from State to State as well, and it prob-
32

ably would require considerable supplementation of what is now

done for the school lunch program.

Mr. MCKEON. Well, whatever we do, it looks like it is going to

take a lot of hard work. So it is just a matter of what would be the

quickest, easiest way to get it to reality.

Mr. BUTZ. Exactly. We certainly agree on that point.

Ms. RUGGLES. I think that you could use school lunch data, but I

think the point that Mr. Elliott makes is a good one. We have done

several studies for the Department of Agriculture on the school

lunch program, and we have found that the number of parents who

apply or fill out forms indicating their poverty status does depend

a lot on what the lunch prices are, so that schools with relatively

high lunch prices are likely to get a lot more people applying for a

free school lunch than schools with relatively low lunch prices. So

that kind of thing you would want to be a little bit careful about.

But you are right, it should be possible to use those data to some

extent.

Mr. MCKEON. Thank you.

Chairman KILDEE. Thank you, Mr. McKeon.

Mr. Reed.

Mr. REED. Thank you, Mr. Chairman.

I would like to address a question to the panel, if you could all

respond. Please correct me if I am in error, but as I understand,

the poverty data is based on a national cost of living threshold.

How adequate is this baseline in terms of calculating the statistics?

Does this data accurately reflect regional cost of living differences?

Thirteen thousand dollars a year for a family of four goes a lot further in some States than it does in my own home State of Rhode

Island, and, following up on the Chairman's initial question, the

good news of no poverty in Rhode Island is news that escapes the

people of Rhode Island. But what is tangible is the potential decrease in assistance based on census numbers. I wonder if you

could comment on that issue.

Ms. RUGGLES. I would be happy to. You are absolutely correct

that the poverty line is the same poverty line across the Nation as

a whole, and it doesn't include any geographic differences for price

differences in different areas. We use the same, you know, $13,000

for a family of four figure in every State, regardless of what prices

are in that State. It would be desirable to incorporate more State

variation into the poverty thresholds, in my opinion.

I think the Bureau of Labor Statistics does have a research program underway right now to look at the question of how that could

be done technically. We don't really know any definitive answer to

that question so far. I think it is an area where we need more research. But you are right that there are very large cost of living

differences across different parts of the country, and ideally it

would be a good idea to take those into account in defining poverty.

Mr. REED. I wonder to what extent this could be disaggregated,

as even within States there are great differences between certain

communities and other communities, and even a statewide figure if

you were at that level might mask some real differences which

would deny truly impoverished families and systems the opportunity to receive assistance.
33

Ms. RUGGLES. You are right, States are not the only jurisdiction

that you would want to look at in thinking about this question. In

particular, what research that has been done on this indicates that

there are big urban/rural differences and that those differences

within States are often larger than across-State differences. So

probably if you did come up with some sort of cost-of-living adjustment you would want to do it separately for urban and rural areas

as well as separately for different regions of the country or whatever.

Mr. REED. Thank you.

Another question: In my home State, and I believe it is common

in many other States, the governor is talking about consolidation of

school districts and changing lines, and I wonder how, if this process of recalculation of data numbers and census numbers, if it is

done on a frequent basis•how can you ensure that the problem of

frequently changing school district boundaries is adequately addressed with respect to Chapter 1 distributions? Is that something

that you have thought about or like to think about?

Mr. ELLIOTT. One of the things that we have done as a part of the

project that the Congress mandated to the National Center for

Education Statistics in 1988 is have each State give us the boundaries actually drawn on a map that can be translated into the

Census Bureau tiger Files that computerize all of that information.

That would also have to be kept up to date if you had intercensus

counts.

Mr. REED. Thank you very much.

Thank you, Mr. Chairman.

Chairman KILDEE. Mr. Gunderson.

Mr. GUNDERSON. Thank you, Mr. Chairman.

I guess I don't want to discount what any of you have said, but I

sit here and I listen to you and I think of the issue, and it seems to

me we start out from three basic premises, all of which are faulty.

The first is that we use data that is as much as 10 years old; the

second is that we use data by county, which has no relevance to

the true income need of a particular school district; and, worse

than that, we use all of this to determine funding for a Chapter 1

program that, once you get into the school, isn't based on income at

all.

So you really have three basic premises, all of which are wrong,

and we are trying to figure out how do we defend or maintain this

process. It would seem to me we ought to all agree this one doesn't

work and we have got to find a new one. Would anyone disagree

with that?

[No response.]

Mr. GUNDERSON. Well, Mr. Chairman, now you know what your

next hearing is going to be on.

Let me ask you this then. To your knowledge, is there any State

that doesn't have some kind of system for determining on a regular

basis the income level of people by school district?

Mr. ELLIOTT. I simply don't know. I would suspect there are a lot

that do not, but I don't know. I would be glad to check that.

Mr. GUNDERSON. If you could. As Mr. McKeon said, that might

be helpful.
34

I know in Wisconsin, and I would agree with Mr. Butz that, sure,

not everybody files income taxes, but most people do, and I think

that is much more accurate than this three-premised inaccuracy

we operate under today.

We always, by school district, determine the amount of income

and the amount of taxes you pay, and I would certainly think that

that system, using that data from the previous year, would be a

heck of a lot more relevant than the data we use today.

Mr. ELLIOTT. Although even so, if you want to look across the

Nation as a whole•and much of this question comes because you

want to have something on a consistent pattern for the Nation as a

whole•information like that within Wisconsin might not be consistent at all with similar information from the State of Michigan.

Mr. GUNDERSON. In terms of income? Calculating what net

income or taxable income is?

Mr. ELLIOTT. The way the calculations are made and the jurisdictions, right.

Mr. GUNDERSON. That is why we have you statisticians to figure

that out.

Mr. BUTZ. In fact, Mr. Gunderson, many of the States do population estimates for pieces of geography that the Census Bureau

doesn't do, and they use very different methodologies and very different data sources, and, as you are probably aware, frequently

counties will even make their own population estimates for very

small areas within the county using very different methods, and, as

Mr. Elliott says, one of the difficulties here is how to do this in a

way that is consistent across the country so that if people disagree,

as they will, about the results, at least the methodology will be

something that everyone will agree is fair or unfair in equal measure for all.

Mr. GUNDERSON. Let's go back to the suggestion Mr. McKeon

made, again, because my Chapter 1 task force back home has suggested the very same thing, which is school lunch data, as being

the most relevant, accurate data that they believe exist today in

the school. Can any of you give us a reason why that would not be

as accurate or more accurate than the present system?

Ms. RUGGLES. The way the school lunch program works is that

forms are sent home to parents to fill out if they wish to apply for

free or reduced-price school lunches. That means that it is entirely

up to the parents to decide. It doesn't actually collect information

on how many families are in poverty, it collects information on

how many families have low incomes and want their children to

receive free or reduced-price school lunches.

There is very little attempt to check that the parents have accurately filled out those forms, and there is no attempt to update it.

You know, it is whatever the parents say their income is, effectively. It isn't carefully updated over time, and there is no use of

records or any of that kind of thing. Nevertheless, it probably gives

you a reasonable picture of the proportion of the population that is

below, you know, 130 percent of the poverty line, which is the cutoff for free meals.

The biggest problem is that there are very big differences across

school districts in the price of the lunches that they charge, and

the work that we have done for the Department of Agriculture
35

really does indicate that you get a very different rate of filling out

those forms and requesting school lunches in the first place if you

have a lower priced school lunch than if you have a higher priced

school lunch, so that there is a certain amount of inconsistency

across school districts.

Mr. GUNDERSON. I am not suggesting it is perfect, but going back

to one of those three faulty premises we are dealing with here, eligibility for Chapter 1 is only partially relevant to economic challenges. The other is environmental challenges. I remember making

the mistake in my first term in Congress of suggesting that every

student who was in Chapter 1 was financially challenged, and I

had some parents very quickly take me to the woodshed on that

one, and I have never forgotten that, and I think that we have to

be careful here that, yes, we are going to have differences, but

what is the basic purpose of the program?

Certainly in California with the base closings, I bet we are going

to have some environmentally challenged children from home that

has nothing to do with their present or even their former, previous

years' income.

Ms. RUGGLES. Yes. I think there is no question that it would be

useful to use the school lunch data at least to control, you know, as

a further check on what your number of children in poverty estimates are at the school district level.

Mr. GUNDERSON. We are going to have so much controversy over

the formula on Chapter 1 anyway. To what degree can any of you

check to see if there is any kind of reasonably consistent income

data being collected at the State level?

Mr. BUTZ. Mr. Gunderson, we are going to check on that.

I want to emphasize that in looking at these different data

sources and in pointing to the difficulty, we are going to be looking

for the simplest methods that we can. We are not going to start out

to try to build a complex model. If we find a particular data set or

several data sets that together do a very good job, we will certainly

tend to use those.

The difficulty is that data of these kinds, of the sort that you suggest and others, simply haven't been tried yet at a national level,

so we really don't know what is going to work, but we are going to

be looking for the most efficient ways of doing it that we can. We

are not going to be trying to build a complex system for its own

sake.

Mr. GUNDERSON. Okay.

Thank you, Mr. Chairman.

Chairman KILDEE. Thank you.

Ms. Woolsey.

Ms. WOOLSEY. Thank you, Mr. Chairman.

I live in California, and that is, of course, a State that would

really benefit from improved data, no question about it. We have a

growing group of very poor citizens in my State, and we have the

highest unemployment rate, so obviously people aren't doing so

well. But I happen to represent an unusually affluent part of the

State, which is just north of San Francisco. Even then, we have a

lot of areas where there is real poverty.

Now my concern in what I am hearing here is, in my district,

which is affluent, we may have to overlook and ignore these pock-
36

ets of poverty. Am I right in hearing this? I mean we are going to

be able to identify within affluent districts and affluent counties

families and schools that have need?

Ms. RUGGLES. Yes. The school district data that is currently

being developed gives you the overall income picture from the decennial census at the school district level, so that to the extent that

there are school districts with relatively high poverty rates in your

district, it would show up in those data.

Ms. WOOLSEY. It may be one school within a district.

Ms. RUGGLES. Even then, all that means is that your district

might have a lower than average poverty rate, but its poverty rate

wouldn't be zero. It would still get money based on the people who

were poor.

Ms. WOOLSEY. In hearing the discussion this morning, I wasn't

feeling very confident about that; I'm feeling better.

I question, are we getting bogged down by using old systems and

old data in what is a new world? I mean do you have available to

you the newest technologies? We can have all kinds of complicated

data in this day and age, and if we use new technologies it

shouldn't be that complicated. We should be able to come up with

what we are looking for.

Mr. BUTZ. Yes, Ma'am.

Ms. WOOLSEY. I mean you agree with me. Now, do we make this

available to you?

Mr. BUTZ. We certainly try to use the most up-to-date statistical

and computing and data gathering technologies. I think in some

areas the Census Bureau is very up to date; in other areas, we have

a ways to go. But certainly one of our principal resources that we

draw on is people such as Dr. Ruggles who is an expert in the private sector, and makes herself available in a variety of ways, as do

others, to help keep us up to date in terms of methodology.

Part of what we are talking about here is, in fact, using the best

statistical, demographic, economic, survey, and computing technologies in the next couple of years under this legislation to try to put

together a means of producing the very best numbers available. So

we will certainly try to be as up-to-date as possible.

Ms. WOOLSEY. All right. Thank you.

Chairman KILDEE. Mr. Green.

Mr. GREEN. Thank you, Mr. Chairman.

To follow up on Mr. Gunderson and Ms. Woolsey, I have looked

at the census data from 1990 for my own district, and we have analyzed it. There are just volumes and volumes of things I can compare in my district in an urban area of Houston that we can use,

and it seems like the data is there, it is just, how do we apply it?

and that is where we come in and develop the formula.

Obviously, I am not satisfied, coming from Texas, like California,

that we see the current formula or method•we feel that we have

just thousands and thousands of students who are not being served

by Chapter 1, for example, because of the 10-year wait we have because we come from a high-growth State, particularly a border

State•California, Texas, Arizona. We have a lot of growth, and so

we are 10 years behind the times.

I like the idea of what Mr. Gunderson said about other methods

that we can use, and Ms. Woolsey also. One of the suggestions is,
37

we know the poverty growth rate, for example; that can be found,

and project that over the years and say, okay, if you have to go by

county, although we would much prefer it to be districts, I know in

Texas we do that down to the school district level, but if we have

to, on Federal, base it on counties, we can do that, because we need

an update.

I think the bill we are discussing, we have to have something

other than every 10 years because of the changes in growth rate

and the changes in what is happening. We are a mobile society. I

can say during the seventies we probably had a great deal of influx

from Michigan, for example, in my area, but in the eighties we

were in such bad economic shape, we only looked good to folks who

were making a dollar an hour, and so that is where our population

growth came from. So I think we need to do something more than

every 10 years, and that is the basis of the bill.

In other mechanisms, the school lunch program I am real familiar with, because that is how our compensatory education is funded

in Texas, similarly Chapter 1, and it is a funding formula; we use

the school lunch program for it. There are bound to be ways we

can make the information more valid, I think, instead of just whatever a parent wants to say on the form, so we can utilize that if

that is a better number. I don't think any of us are happy with the

way the formula is provided now, whether you are from Texas or

Mr. Reed from Rhode Island. So that is what we are searching for,

some kind of changes.

Having looked at the data from my own district, I can pretty

well tell you, although thank goodness we don't have any outdoor

toilets, but I can sure tell you that the census tells me how many

we have that have a bath and a half or two baths, and so we are

bound to be able to tell the poverty rate and how many services we

need to provide for those children through Chapter 1, and not just

Chapter 1 because I know we are talking about all Federal programs, and I think that would be one of the biggest reforms we

could finish here, outside of everything else, if we could provide an

update on the services we provide.

My question would be, have you thought about using the poverty

growth rates? I know that is updated annually or at least available

with some numbers that we can verify.

Ms. RUGGLES. I think that that is essentially what this proposal

is, to use poverty growth rates for each specific jurisdiction, and I

think that that is what the Census Bureau is trying to figure out a

way to make feasible.

Mr. GREEN. Okay. Can you do that with the numbers you have

now or the information that you have now?

Mr. BUTZ. No, sir, because the poverty rates that we have now,

as Ms. Ruggles mentioned in her testimony, are at an annual level

and come from a relatively small survey, a survey of some 60,000

households. Once every 10 years we use the decennial census as a

data source, which does provide data for very small jurisdictions.

The updating would use those decennial census data that are

very detailed and would try to update them using all these other

sources of information that we talked about. So it would be an updating process, but it would require both those data and the methodology that we do not now have to combine those sources of infor-
38

mation together to produce sufficiently accurate numbers as time

marches on, every two years under the bill, at relatively small

levels of geography.

Ms. RUGGLES. But basically the point of the census effort is to try

and produce meaningful poverty growth rates for each jurisdiction•each county, State, town, and so on•and that is what they

are using all this extra data for, is to try and put together for that

specific jurisdiction how much has poverty grown or how much has

it declined.

Mr. GREEN. And I also understand the concern about whether

you are talking about the small numbers, the smaller jurisdictions.

Of course, I think our goal on the committee is to serve actually

the children instead of the jurisdictions, because that is where the

funding goes to. Hopefully the Chapter 1 benefits that child instead

of that district, or that county, or even that State.

Thank you.

Thank you, Mr. Chairman.

Chairman KILDEE. Mr. Becerra.

Mr. BECERRA. Thank you, Mr. Chairman.

I apologize for being late and not being able to listen to all of the

testimony, and perhaps these questions have been answered, but

let me ask a little bit about the undercount that took place in the

1990 census and what the impact of that undercount will be with

any type of calculations and formulas we come up with.

As I understand it, for example in California, I understand that

there was an undercount in the area of over a million people, and I

suspect that we will find that, when you take a look at the poverty

population, that would be even a higher percentage of the undercount. How are we going to try to tackle that undercount when we

come up with a formula?

Mr. BUTZ. The decision of the Secretary of Commerce was not to

adjust the 1990 census for differential undercount, and the director

of the Census Bureau made a subsequent decision not to adjust the

population estimates for undercount, and it is those estimates that

I described earlier as one of the foundations of the population estimates, which in turn is one of the foundations for the poverty estimates that we are discussing here.

So under those decisions, those differential undercounts, as measured by the Census Bureau, will be carried on through the decade

until the next census occurs.

Mr. BECERRA. Is there any way for the Department of Commerce

to reexamine its decision not to incorporate those undercounts?

Mr. BUTZ. I am afraid answering that is beyond my knowledge

and my authority.

Mr. BECERRA. Well, let me ask it this way. Are you aware of anything that prohibits the Department from reexamining that undercount?

Mr. BUTZ. I am not aware of anything.

Mr. BECERRA. Okay.

Again, I think the next two questions will be in line with what I

asked at the beginning. Amnesty applicants under IRCA will all be

eligible to become citizens over the next year or two. They are, for

the most part, a population that is somewhat mobile. Is there any
39

fear that we may have missed some of these folks or that they are

a good portion of the undercount?

Mr. BUTZ. That is a population that has a relatively high differential undercount, along with some others, but to the extent that

these people are picked up in administrative record systems in the

school lunch program and the AFDC program of the sort that we

have suggested, and to the extent that that information is modeled

or put together with the baseline information from the 1990

census, there will tend over time to be some accounting for those

people in the combined estimates. How that will work and how

much of the differential undercount could be made up that way I

don't know.

Mr. BECERRA. Does it help that the Immigration and Naturalization Service has documentation for each of these individuals and, I

suspect, family members as well? Can the Census Bureau use the

information that is housed within INS?

Mr. BUTZ. Yes, sir, we do use that information as a means of developing our estimates of the numbers of undocumented aliens in

the U.S., and we also use that information to some extent in

coming up with our estimates of the numbers of people missed in

the census.

Mr. BECERRA. The final question I have relates to the migrant

population, which of course is probably the most undercounted population. I looked through your testimony, and you mentioned that

the small areas are those areas that have the largest undercount.

Are we going to try to do anything to try to take care of the undercount that may have occurred with the migrant population?

Mr. BUTZ. Well, first, I didn't mean to state or imply that the

small geographic areas necessarily have the largest undercount.

They tend to have the largest sampling error in producing estimates.

For the 2000 census, we are, with the excellent cooperation of

Chairman Sawyer and his leadership, making enormous efforts to

try to find ways to reduce the differential undercount for the 2000

census. It is a daunting job, but we are doing our best to plan ways

of doing it.

Mr. BECERRA. Any chance of accelerating that for your estimates

coming up in the next couple of years?

Mr. BUTZ. It is really a job that involves the census much more

than it does the estimates. The census is the basis for the estimates, and to the extent that the census is right, the estimates will

tend to be right.

Mr. BECERRA. Mr. Butz, I am a bit apprehensive about what you

say, because it is clear that we are using figures that may lead to

some erroneous results in terms of our estimates. Is there anything

you see that we can do short of waiting until the year 2000 to try

to correct some of the inaccuracies in the estimates for some of the

populations that might be missed or more mobile?

Mr. BUTZ. There are methods available for adjusting the estimates process, for adjusting the census as a base for the estimates,

but the decision has been made that below the State level, certainly, the accuracy of those adjusted estimates is unclear and cannot

be demonstrated to be better than the accuracy of the estimates
40

based directly on the census without adjustment. That was the decision that was made.

As I say, there are methods of doing it. The question is, do those

methods produce demonstrably more accurate data or not? The decision has been made, no.

Mr. BECERRA. Thank you.

Thank you, Mr. Chairman.

Chairman KILDEE. Mr. Payne.

Mr. PAYNE. Thank you very much, Mr. Chairman.

I am very sorry to be this late, and it is good to see Congressman

Sawyer still on the job, looking over the census. We spent a lot of

time trying to see that the 1990 census would be done correctly, as

I wished that the 1980 census would have been done correctly, and

as we agonized with the 1970 census which was done poorly, and I

decided to do some research and found out that when they did the

first census it was reported that it was inaccurate back, I think, in

1890.

Mr. BUTZ. Yes, sir. The President was upset about it at that time.

Mr. PAYNE. And so the thing that really amazes me is the fact

that the undercount is accurate. We have a way of knowing how

many people were not counted, and that always amazes me because

we simply can't use that because it is circumstantial evidence. We

have to really see the body or see the note from that individual.

So I really would hope that there could be other approaches.

They tell me that they do have ways of knowing deaths and births

and utility bills and things of that nature and that a fairly accurate accounting is being kept, but the fact that the enumerators or

the individuals have not responded, therefore they are ineligible,

this becomes serious.

For example, in New Jersey about $33 million is missing because

of Title I. There is a total of 10 programs in Title I of the Elementary and Secondary School Act which rely on population figures

and formulas for determining allocations. Programs under Titles II,

III, IV, and V rely on population for determining allocation standards, and we also know the whole question of Chapter 1, and it is

really creating a very serious hardship in my district where schools

are seriously being recommended they downsize their Chapter 1

programs.

I missed the testimony, and I am sure that we will attempt to

have a more accurate census in the future, but the continued undercount I think is totally unfair, especially in light of the fact that

it can be done in a more judicious manner.

Also, I get confused by the judge's rulings where he will rule

that•is it Judge Green who has been dealing with this?•the judge

in the recent case, where he acknowledged that there was an undercount, but would use it partially for one reason but wouldn't use

it for another. I think that that kind of confusion even from the

judiciary where we can't get a clear picture•it is hard for both

people to be right, and the judge is saying, "You are both right, or

either we are both wrong,' and someone has got to be right, and

someone has got to be wrong. I mean that is the way they taught

me in school.

So I just wonder what type of resolution, because it does do irreparable damage, particularly to the poor districts, and I just wonder
41

if anyone has any quick, one-minute response, because I am sure

you handled it in your testimony.

Mr. BUTZ. Congressman, the principal goal of planning for the

2000 census is to reduce the differential undercount. That is our

principal objective. It is a very difficult problem. You have said a

lot there, and it is hard to know how to respond.

I would say in terms of the difference of interpretation of numbers that we feel that we have a very good handle at the national

level of what this undercount is, how much it is, how it is distributed across different groups•renters, owners, by racial and ethnic

groups. The difficulty is trying to work that down to very small geographic levels, and in that sense it is somewhat analogous to the

problems that we are talking about here of producing data for

school districts. Again, it is that low level of geography that presents the problem.

For example, you mentioned birth and death records. At the national level, we do go back, way back, decades back, and add up

how many people were born and died, moved in and moved out, but

that is national data, and where someone was born isn't where

they live now.

So it is very difficult, and, unfortunately, it is subject to different

interpretations as to whether the data are useful for one purpose

and not for another, and I share a good deal of your frustration.

Mr. PAYNE. Finally, also you find that there becomes a differential within a State geographic area. For example, several of the

cities, I encouraged that they sue the Census Bureau because of the

fact that we knew there was an undercount. We had an undercount in 1980, and we have restored about 30,000 people, almost 10

percent of the population in the City of Newark, back when the

1980 census was done, and we also had some numbers put back in

1990. But the undercount numbers which affected my district, if

they ran them through the States throughout the Union, the undercount in New Jersey was less than it was projected for some

other States. Therefore, New Jersey would have lost under the recognition of the undercount.

So the State takes a position, the governor's position•who is a

former Democratic colleague here in the House•took a different

position than I took as a representative of Newark and Elizabeth

and many impoverished cities because it would have benefited our

district, but overall the State, in comparison with the national situation, would have lost.

So we do find also that the integrity is questioned when State officials do what they feel is best for the State and, once again, the

poor districts suffer by virtue of the decision which would be considered almost local from a national standpoint on a State level,

but when we get down to the difficult real local areas, it makes it

extremely more difficult.

Thank you very much.

Chairman KILDEE. It is my understanding that Caesar Augustus

questioned the figures made during the census when Sabines was

governor of Syria. So there are always questions on the census, and

I am sure you are trying to upgrade your systems ever since the

time of Caesar Augustus to try to get more accurate figures. I know
42

you are working hard at that, and I know Mr. Sawyer is very interested in accomplishing that.

One question: Commissioner Elliott, you stated that in the absence of school district updates from the Census Department,

county updates of the poverty counts would be useful for updating

the distribution of Chapter 1.

Mr. ELLIOTT. Right.

Chairman KILDEE. Are there any special problems for producing

district level updates for school districts that cross county boundaries?

I ask the question because I represent basically three counties in

Michigan. One of the counties is considered the first or second most

affluent county in the country, and the other one is the county in

which the city about which Michael Moore wrote the book "Roger

And Me" is located. One of the school districts crosses that county

line, one in the more affluent county and one in the county that

has very high unemployment. How would we be able to ascertain

the figures there?

Mr. ELLIOTT. There would have to be a similar sort of thing that

is done for the mapping of information from the 1990 census in

each school district, and that is, where the boundaries occur you

try to draw a line where the actual boundary is and then proportion the population on each side of the line. That frequently takes

a lengthy conversation within a State to agree on what will be the

basis for the proportion that is split, and that is passed along to us,

and then we give it to the Bureau of the Census so the information

can be produced back by that new boundary. But the same sort of

thing would have to happen there where something went beyond

the jurisdiction of a single county to pick up a portion of each of

the two counties in which the district was located.

Chairman KILDEE. And under the present situation, it is the

State that makes that determination, how much money would flow

to that county, and then also the State makes the determination of

how much money would flow to a particular school district within

that

Mr. ELLIOTT. Within the county, and then if more than one

county is involved, then they would have a larger jurisdiction and

fit within the combination.

Chairman KILDEE. Thank you very much.

Mr. Sawyer.

Mr. SAWYER. Thank you, Mr. Chairman.

Just an observation. It has been enormously impressive, the

amount of effort that the members of this committee have made to

come to grips with the substance and the implications of the subject that we are discussing today. I am grateful for the questions

that have been asked, frankly, on both sides of the table.

Let me ask a question, and I direct it primarily at Mr. Elliott

and Ms. Ruggles. We are trying to find, in addition to the way in

which we can statistically improve the quality of intercensal estimates, the kind of administrative data that will give us the best

indications of how well we are doing and where we ought to make

those adjustments.

Can you talk about the kinds of data, the sets of data, that have

the greatest consistency from one State to another? We can talk
43

about the failures of certain kinds of data, lunch programs and

others, but can you talk about the data that provides the greatest

consistency?

Ms. RUGGLES. I think probably the food stamp program for the

low end of the population is the data set that you would want to

use, because it is the only program for low-income people that has

the same rules and the same income cutoffs and so on, no matter

where you live.

For example, the Aid to Families with Dependent Children program has different benefit levels in different States, and that affects the eligibility, so it isn't very consistent across States. But the

food stamp program is pretty consistent, and we do have a reasonable set of case records for that program.

Mr. ELLIOTT. I think probably Ms. Ruggles is more familiar with

those data outside of the education system than I am. Actually, I

would welcome a challenge within the National Center for Education Statistics to work with our State Cooperative Statistics Program to see whether either there are some available data sets that

might be more consistent or, on the other hand, whether some cf

the ones around, like school lunch, might be improved considerably. That is simply not something that we have explored, and I

think that is something that could be done.

Mr. SAWYER. Just with regard to the school lunch program, the

poverty thresholds are at 130 percent or something like that. It

would be fine if the distribution of poverty between those two were

consistent across the country, but I know that when I think of Mr.

Gunderson's State, there are fewer people who are impoverished,

but when they are impoverished they are extremely impoverished

by comparison to many other places. That kind of consistent,

smooth flow just doesn't exist.

It brings me to a second question, and that is the notion of geographic cost-of-living differences. We have had several members

who have talked about that in one way or another. Is there a reliable way to build those kinds of considerations into our estimates?

Mr. BUTZ. Well, the responsibility and the expertise for that

within the Federal Government is principally in the Department of

Labor at the Bureau of Labor Statistics, and, as Pat Ruggles mentioned earlier, they are and have in the past been concerned about

this. It is apparently a severe problem, a data problem, in finding

the information that will reliably produce better cost-of-living

measures at local areas than if one simply uses the national average overall. But I believe they are working on it still.

Ms. RUGGLES. Yes, they are. They do have a research program in

this area. In addition, I think the Poverty Panel that the National

Academy of Sciences has going on right now will probably come up

with some recommendations in this area.

Mr. SAWYER. Let's see. Maybe by September of 1994, right?

Ms. RUGGLES. AS a matter of fact.

So I think that there is work going on in this area, but it is a

difficult technical problem. What constitutes a representative

sample of prices for particular places is a pretty tough thing to

figure out and to compare from place to place.

Mr. SAWYER. In closing, Mr. Chairman, all of that really goes

back to what you started us with, the Mollie Orshansky numbers.
44

What we are trying to deal with here is to come together with a

three-legged stool that works. We are trying to get an accuracy of

measurement that comes from timeliness to work together with a

definition of poverty that may well have changed substantially

since the mid-1960s•end of the sixties, when it was made official,

and a distribution formula that recognizes both in a way that produces the results that we want. In any case, it seems to me that

accurate measurements are the cornerstone of that kind of sound

policy.

Thank you very much.

Chairman KILDEE. On that point, I know in my district it would

be hard to take just AFDC figures to determine poverty because we

have so many people in my district now who are the working poor,

and they are in great need. So there has to be a constant reevaluation of what constitutes poverty and how then we distribute funds,

all types of funds, including Chapter 1 funds, based upon the poverty.

I lived in the City of Flint when you could quit school on Tuesday and go to work for Buick on Wednesday, and those days are

gone forever. We find so many people, and we are sometimes assured, "Don't worry, we'll move into a service industry economy,"

and we are moving into a service industry economy in Flint, and

people are very poor in doing that.

So I think we have to constantly upgrade the methods of counting, upgrade the methods of determining various things about

people, and constantly upgrade our formula to distribute the dollars based upon those figures.

Before we go to the next question, I would like to acknowledge

the presence of a very dear friend and former chairman of this

committee, whose picture is right behind me, but there he is right

in front of me over there, Mr. Gus Hawkins.

[Applause.]

Ms. Woolsey.

Ms. WOOLSEY. NO questions, Mr. Chairman.

Chairman KILDEE. Mr. Payne.

Mr. PAYNE. No, thank you, Mr. Chairman.

Chairman KILDEE. Without any further questions then, you have

been very, very helpful. Certainly you are the professionals, the experts, the parite, as we use a Latin term, in your field, and even

very, very helpful to this committee. I want to thank all of you. We

look forward to continuing to work with you as we work on H.R. 6,

the Elementary and Secondary Education Act, and on Mr. Sawyer's

bill, H.R. 1645. The record will remain open for two additional

weeks for any additional testimony.

Thank you very much.

[Whereupon, at 11:52 a.m., the subcommittees were adjourned.]

[Additional material submitted for the record follows.]
45

STATEMENT OF HON. DONALD M. PAYNE, A REPRESENTATIVE IN CONGRESS FROM THE

STATE OF NEW JERSEY

Chairman Kildee and Chairman Sawyer, I would like to take this opportunity to

thank both of you for bringing these distinguished panelists before us today to discuss H.R. 6 and H.R. 1645.

I am honored to be a part of this important process that will examine whether

accurate data is being provided in between census years. Census data is extremely

important because it often serves as a basis for legislation, case studies, and research models, among other things. The proposed legislation would require the

Census Bureau to produce poverty estimates for States, counties, cities and school

districts.

Currently, data is only collected during the decennial census and frequently does

not reflect the overall poverty situation in a given area.

Census data is particularly important in light of reauthorization under Chapter 1.

Education allocation formulas usually rely on population groups that approximate

the intended beneficiaries, and for Local Education Agencies, the benefit is intended

for educationally disadvantaged children living in relatively low-income areas. Furthermore, for other ESEA allocation formulas, a variety of populations are used for

the calculation of grants.

A total of 10 programs under Title I of ESEA, rely on population formulas for

determining allocation. Four other programs under Title II, III, IV, and V•also rely

on population for determining allocation standards.

In poor, inner city districts, the availability of accurate, valid data is crucial in

improving the lives and education of children. I strongly believe that we need data

that will reveal the true poverty situation in our country. I encourage a full and

engaging discussion on this legislation.

I would like to thank all of today's panelists for taking the time to present their

testimony for us today. I look forward to hearing their comments.

STATEMENT OF HON. DON EDWARDS, A REPRESENTATIVE IN CONGRESS FROM THE STATE

OF CALIFORNIA

I would first like to thank Congressman Tom Sawyer, Chairman of the Post Office

and Civil Service Subcommittee on Census, Statistics, and Postal Personnel as well

as Congressman Kildee, Chairman of the Education and Labor Subcommittee on Elementary, Secondary, and Vocational Education for giving me the opportunity to

share with you the Delegation's views on H.R. 1645, "The Poverty Data Improvement Act of 1993."

H.R. 1645 is a critically important bill which would treat all the people of the

United States equally. It would require the Census Bureau to produce and publish

poverty estimates for States, counties, cities, and school districts every two years beginning in 1995. This would replace the current system which utilizes decennial

census poverty data to annually allocate the estimated $20 billion in grants to State

and local entities. The reliability and usefulness of this data, collected once every 10

years, quickly becomes outdated and ineffective in accurately allocating the funds to

the places and people of greatest need.

Californians are particularly aware of how the accuracy of census and poverty estimates affect the State's ability to get its fair share of Federal dollars. The unique

characteristics of California's population make it among the most difficult to count

accurately. Based on research completed at the Population Resource Center, a nonprofit, non-partisan organization, California will gain 30 percent more people and

absorb three-eighths of the population growth nationwide between the years 1990-

2005. This increase includes the integration of nearly one-half of all legal foreign

immigrants and refugees into the United States annually. The spiraling growth of

California places an incredible burden on the resources of our State and local governments which the data from a decennial census cannot accurately gauge or accommodate.

While most areas of the country are starting to feel the benefits of an economic

upturn, California still finds itself reeling from the recession. With numerous base

closures scheduled, many defense plants shutting down, and statewide budgetary

problems, the crisis will likely worsen. However, this is not a question of pitting one

State, or region, against another. The question before the Congress today with this

legislation is fairness. We have an obligation to use the numbers that most accurately reflect the needs of our State and local governments. That was Congress'

intent when it passed legislation requiring the allocation of funds based on population data. Everyone "wins" by having the most up-to-date data to distribute the Federal grants to State and local governments.
46

One of the most prominent examples of the need for this legislation is the Chapter 1 program. For current 1992-1993 school year. Chapter 1 funds were allocated

using the 1980 census. This census data was collected in 1979, making the income

data 14 years old and hopelessly out-of-date. In California alone, using 1980 census

data instead of 1990 data for just the 1992-1993 school year, this translates into a

loss of over $125 million. This is magnified manyfold over the course of the 14 years,

not just in funding but also in the loss of services and goods which such moneys

could provide for the underprivileged children who need it the most. H.R. 1645

would resolve this disparity with timely poverty data updates below the national

level.

I understand the Census Bureau has already prepared a proposal for research and

development of the methodology for producing the data which H.R. 1645 would require. The cost of this program is set at only $450,000 annually. This figure is only a

fraction of the amount of funds that are allocated each year on the basis of poverty

data. In such times of scarce funds and budget reductions, this clearly is an effective

and efficient use of taxpayer money.

Last August, I had the opportunity to testify before the former Director of the

Census Bureau, Dr. Barbara Everitt Bryant, in favor of adjusting the intercensal

population estimates. Again the issue was fairness. Unfortunately the Bureau ultimately decided not to use the Post Enumeration Survey for its annual census adjustment. I was disappointed in their decision. H.R. 1645 goes a long way toward

mitigating the problems debated at that time.

Mr. Chairman, during these trying economic times and budgetary cutbacks, we

must allocate what limited resources we have in the most effective, efficient, and

fair manner. Without accurate periodic poverty estimates, many of our most desperate and destitute people will find their needs going unmet. H.R. 1645, "The Poverty

Data Improvement Act of 1993," will provide an inexpensive, effective way of fairly

distributing this shrinking source of funds. I will be working diligently for its passage and I urge my colleagues to join me in strong support of this legislation.

STATEMENT OF GAIL IMOBERSTEG, DIRECTOR, FEDERAL LIAISON OFFICE, CALIFORNIA

DEPARTMENT OF EDUCATION

The reliance of poverty estimates generated through the decennial census for the

allocation of Federal program funds presents some important problems with regard

to the allocation of education funding. Due to the rapidly changing demographics in

the United States, the data used to distribute funds for programs such as Chapter 1,

Hawkins-Stafford Elementary and Secondary School Improvement Act [Public Law

100-297], are hopelessly outdated before they are used. For example, the allocation

for fiscal year 1992 program funds for the Chapter 1 program was based on data

collected for the 1980 census. Since this data actually incorporates 1979 poverty estimates, the data was actually 14 years old when it was used to distribute funds. The

1990 poverty data will be five years old by the time it is used to allocate Chapter 1

funds.

The use of data 5 to 14 years old to direct Federal funds raises some significant

issues of accuracy and fairness. Between 1980 and 1990, California became home to

an additional 247,162 poor children. This represents a 38 percent increase! These

poor children, many of whom are limited-English proficient, have now been in California classrooms for years. Since they are uncounted in the Chapter 1 formula,

there have been no additional funds provided to serve these children under this program. With diminishing fiscal resources, the targeting of funds to the intended recipients is particularly important. The use of outdated census data for the distribution of Chapter 1 and other program funds flies in the face of efforts to target resources fairly.

Since the Chapter 1 program formula is used to allocate funds for a number of

other education programs, the accuracy of this data has had far-reaching implications for education programs in California and throughout the Nation. In addition,

the enormity of demographic changes that result every 10 years exacerbates conflicts among States that are struggling to provide programs with diminishing State

and Federal resources. If poverty estimates were provided every year, or at least

every two years, the distribution of Chapter 1 funds would follow the children as

populations shift throughout the decade. This would not only enhance the targeting

of funds, it would also eliminate the enormous shift in funds that occur after each

decennial census and allow for the gradual absorption of decreases and increases of

funds as populations shift.
47

The California Department of Education is strongly supportive of Congressman

Thomas Sawyer's bill, H.R. 1645, which requires the Census Bureau to produce poverty estimates every two years for States, counties, cities, and school districts.

ANSWERS TO QUESTIONS SUBMITTED BY THE CENSUS BUREAU, DEPARTMENT OF

COMMERCE, WASHINGTON DC

First set

Question la

You expressed concern about access to IRS data for the proposed intercensal

small area poverty estimates program. Would the direct requirement for these estimates in H.R. 1645 and direct funding, as provided in the House version of the fiscal

year 1994 Commerce, Justice, State and Judiciary Appropriations bill, eliminate

that concern?

Answer la

We met recently with IRS officials. They expressed their support for this important project and assured us that we will have access to the data we will need to

carry it out.

Question lb

Does [or would] the Bureau have access to W-2 and 1099 forms, if those records

become necessary for the poverty estimates program?

Answer lb

Based on our recent meeting with the IRS, we believe that we will have access to

whatever records are necessary for the program. While all details regarding the

level of access have not been worked out, we have agreed to work together to develop the best possible estimation methodology.

Question 1c

Will you need additional legislative language to facilitate the transfer of data

from the IRS to the Census Bureau?

Answer 1c

No. We believe current statutory authority is sufficient.

Question 2

In addition to separate tabulations for the number of poor school-age children,

will the Bureau be able to report separate tabulations for other age groups in poverty [such as "over 65 years of age"]?

Answer 2

As you know we have developed a plan to provide updates of the 1990 census

income and poverty estimates during this decade. This plan would be funded by a

consortium of departments and agencies who use these data for the allocation of

funds and for other administrative purposes. The plan, as it now stands, is aimed at

meeting the needs of this consortium. Since none of the members have requested

poverty estimates for population subgroups other than children, we have not

planned to make estimates for any other demographic breakdowns. Expanding the

scope of the project to include estimates for other population groups may be possible. This would, of course, require additional work in both the population estimation

and income and poverty estimation.

Second set

Question 1

What would be the most reliable and cost-effective way to update census data on

children in poor families, whether at the county level or the LEA level, between

now and the next decennial census, so that we can minimize the likelihood of large

population shifts, and attendant Chapter 1 allocation changes in the future?

Answer 1

We have developed a proposal to provide updates of 1990 census income and poverty estimates during the decade. Since we have never attempted to make these

kinds of estimates, it is difficult to say what would be the most reliable and costeffective ways to produce these data. The first phase of our proposal is to conduct

the research that would answer that question.
48

The large population shifts that you reference reflect real changes in the size and

geographic distribution of the population during the 1980s. A program to provide

periodic updates of the 1990 census poverty estimates during this decade would

reduce the time period between poverty estimates from 10 years to an interval of 2

to 3 years. These estimates could be used to make more frequent adjustments to the

allocation of Chapter 1 funds. These more frequent adjustments would be smaller,

but, their net effect over the 10-year period between censuses would reflect the same

overall change as measured by the censuses.

Question 2

There is legislation that would require changing county data on poor school-age

children by the statewide rate of increase or decrease in all school-age children.

What is your opinion of this proposal? Is its implicit assumption•that local trends

in school-age poor children closely track statewide changes in total school-age population•valid? Are there States where during the 1980s, total and poor school-age

populations moved in opposite directions?

Answer 2

We are unable to answer the first two parts of the question at this time. Since we

have not received a copy of the proposal, it is not possible for us to comment on it at

this time. With regard to the second part of the question, we have not made any

analysis of the relationship between local trends in poverty and statewide changes

in the size of the total school-age population. We do have data to answer the third

part of the question. These data show that during the period between 1980 and 1990,

14 States experienced increases in the number of poor school-age children [based on

related children age 5 to 17 years old] while experiencing declines in the total population of such children. We found no States in which the number of poor children

declined as the total number of children increased. There were 12 States in which

both the number of poor children and the total number of children increased. In the

remaining 25 States [including the District of Columbia] both the number of poor

children and the total number of children declined between 1980 and 1990.

Question 3

What would be the advantages and disadvantages of conducting a mid-decade

census in 1995?

Answer 3

The clear advantage to conducting a census [including collection of sample data

on income and other measures of poverty] in 1995 would be to produce updated statistics five years earlier than would occur by waiting for the next decennial census.

Thus, data would be available in 1996-1997 rather than 2001-2002.

The biggest disadvantage would be cost. It likely would cost well over $1 billion to

conduct an actual census [as opposed to a large sample survey] that could produce

data [both 100 percent and sample] for small areas [such as school districts or counties] similar to that for the 1990 census. Even if funds were available beginning in

fiscal year 1994, it is unlikely that all the preparations [particularly address list development] needed to take a census could be completed in time to conduct a census

the following year.

For Witnesses from the Census Bureau and the National Center for Education

Statistics

Question 1

What is the current status of the proposal that has been worked on by Census

Bureau staff to provide updated county-level estimates of children in poor families

biannually beginning in 1995? Does the Department of Education intend to support

and contribute to this project? What are current estimates of its cost, and the probable reliability of the population estimates it would produce?

Question 2

Do you plan to keep up-to-date the LEA maps prepared for the purpose of compiling the 1990 census by LEA?

Answer to 1 and 2

The two questions assigned to the Census Bureau and the National Center for

Education Statistics witnesses are largely within the scope of NCES programs. We,

therefore, defer the answering of these question to them.
NORTHEAST-MIDWEST

CONGRESSIONAL COALITION

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Testimony

Of

Hon. Dean A. Gallo

Co-chair. Nortbeaat-IUdKest Congressional Coalition

before

Subcommittee on Census. Statistics and Postal Personnel

House Post Office and Civil Service Committee

Subcommittee on Elementary. Secondary and Vocational Education

House Education and Labor Committee

July 13. 1993

U.S. HOUSE OF REPRESENTATIVES

530 Ford House Offlca Building. Washington, O.C. 20616 • 1202) 226 3920
50

Messrs. Chairmen, I an testifying today on behalf of the Northeast-Midwest

Congressional Coalition, which is co-chaired by myself and Rep. Marcy Kaptur

and which la honored to have both of you as active members. I am pleased to

present for the record some comments on the adequacy of the nation's poverty

statistics and the Coalition's suggestions for how they can be improved.

To begin, let me congratulate the panel on two counts: first, for the

attention you are paying to this issue, and second, for what a joint hearing

says about both the legislative role in setting statistical policies and the

user considerations that should go into the making of those policies.

First, you deserve credit and the thanks of Congress for taking seriously

a subject that some might dismiss as too arcane and esoteric for elected

officials to bother about. You, in contrast, recognize that, as legislators,

our understanding of social conditions and our ability to formulate responsive

programs depend on our having good information. But, our dependence on

statistics goes far beyond these policy development uses. Indeed, because of

congressional decisions, taken over the past twenty-five years, to use

number-driven formulas for allocating federal funds and defining eligibility

for benefits, we are now in a position where the implementation of the vast

bulk of federal domestic policy rests squarely on the nation's statistical

programs. Statistics drive billions of dollars this way and that•into some

hamlets, into some cities, towards some persons, away from others. When

statistical data programs (1) fail to measure the conditions we policymakers

think they are measuring, or (2) fail to measure them in a timely fashion, then

the policies we enact also will fall. The foremost example of these two types

of statistical failures•not measuring what we intend and not doing it

promptly•is poverty data, the subject of this hearing.

Before turning to that subject, however, 1 want to explain my second

reason for congratulating the panel. It is significant that the Subcommittee

on Census, Statistics and Postal Personnel•the authorizing body for the Census

Bureau's statistical programs•and the Subcommittee on Elementary, Secondary

and Vocational Education•which depends on census data for the implementation

of its policies•have chosen to hold a joint hearing. That says something

important about the making of statistical policy. It says that statistical

programs cannot be designed or evaluated in isolation from the ways the data

are used. The poverty data are a case in point; defenders often excuse the

shortcomings of these data by noting that they were not designed for the way

Congress now directs that they be used in allocating funds and defining who is

eligible for benefits. Somehow, these defenders manage to imply that the fault

is not with the data but with Congress for using data the "wrong" way. I would

submit that this formulation needs to be reversed: BECAUSE OF THE WAY CONGRESS

USES THEM. THE POVERTY DATA NEED TO BE REDESIGNED TO DO WHAT CONGRESS INTENDS.

By holding a joint hearing, you are doing something very significant here

today. You are saying, in effect: (1) that statistics need to be designed with

the users in mind; (2) that Congress itself is a primary user; and that (3) the

implementation of federal domestic policy is now the first and foremost use of

the products of the federal statistical system. Elected officials want a

statistical system that anticipates and responds to the data needs of the

policymaking and policy implementing process, a system that grows with the way

data are used. Unfortunately, the statistical system has not been responsive

to the way Congress is using poverty data.
51

I wont to concentrate today on two ways in which the poverty data fail to

meet the needs of Congress•their lack of timeliness and their lack of

validity. The timeliness problem is obvious; using data that are ten or more

years old to carry out federal domestic policy•as we have done with Chapter 1,

the Job Training Partnership Act, and other programs•is irresponsible. The

Chairman's actions to develop legislation on the subject of more timely poverty

numbers are commendable. The Census Bureau's initiative to produce biennial

updates of poverty data is long overdue.

The timeliness problem thus seems well on its way to solution. So, I want

to call the panel's attention to an even greater problem with the poverty

statistics•that of their validity. Simply stated, while we assume that the

poverty data provide counts of the number of persons in need, in fact they do

no such thing. The poverty counts are numbers below one national income

standard that does not take into account the very real cost-of-living

differences from one area of the country to another. Because of cost-of-living

differences, a person whose income is just above the poverty level but who

lives in a high cost-of-living state such as Connecticut or California can be

much worse off than a person whose income is just below the poverty line but

who lives in a low cost-of-living area such as Louisiana or New Mexico.

Despite greater need, the former is not counted as poor; the latter is.

Our current measurement of poverty, in short, does not measure relative

need. And yet, relative need is the very concept Congress is groping for when

we design allocation formulas for programs. Relative need is what we think the

poverty data is measuring. Not so. By using these data, we misdirect funds,

sending proportionately more funding relative to need to some areas and less to

others. This means that funds adequate-to-need for remedial education, for

summer Jobs, for training, and for other human development purposes, are

systematically denied to places like Newark, Detroit, Cleveland, Los Angeles,

the former mining towns of western Pennsylvania and northern Minnesota, and

remote rural locations in Vermont and Iowa. Needy persons who by any

commonsense consideration are "poor" continue to go without. The resulting

patchwork of funding priorities amounts to no federal policy at all.

Addressing the timeliness problem only deals with half the issue. It is

equally important, perhaps even more important, that, in reforming the poverty

data, you deal with the present inability of these data to measure relative

need. The way to do this would be to use a state-by-state cost-of-living index

to produce state-specific poverty income standards. A state's residents whose

income falls below the state-specific poverty standard would then be counted as

"poor." The poor from all states, added together, would constitute the poverty

population of the nation.

I would like to submit with my testimony two state-specific cost-of-living

indices to show what can and should be done to make the poverty data

meaningful. You will notice that both researchers, using somewhat different

methodologies, arrive at similar results in terms of the rank order of states

and the magnitude of cost differences separating them. Differences of over 50

percent in the cost of living distinguish one state from another. With

differences this wide, it is clear that our present use of one national income

standard to define poverty does grave injustice to the concept of need. The

procedure I am advocating would multiply the national poverty standard by the

values in a cost-of-living index to create poverty standards for each state.

The second attachment to ay testimony demonstrates this procedure.
52

I want to anticipate two doubts that may be raised about the idea of

state-specific poverty standards. First, some analysts will posit that

cost-of-living differences within states may be as great as differences between

states•the example of New York City and upstate New York is often cited in

this regard. Although the cost-of-living does vary within a state, it still is

the case that a state-cost-of-living-adjusted poverty standard will come closer

to the truth of what level of income constitutes need in any one state than

does our present national poverty standard. In addition, the seminal work by

Dr. Walter HcMahon, Professor of Economics at the University of Illinois, shows

that while there is a 57 percent difference in purchasing power between the

highest and lowest states, the variation within states Is in the lower ranges

of 22 to 35 percent. Finally, the poverty data we have now are arbitrary and

manifestly incorrect. What I am proposing is an obvious improvement;, which may

itself be improved further. In short, I want to say to the panel: don't do

nothing just because you may not be able to achieve perfection.

Second, some may argue that we need more research on this idea. In

answer, I would say that while research on state cost-of-living differences is

not voluminous, it is respected. Indeed, the U.S. Department of Education has

arranged to have one of these indices updated this fall when new census data

become available. Instead of ordering more research or calling for more study,

I would urge the panel to move forward and direct the Census Bureau to

implement the idea of state poverty standards. I know, from the

Northeast-Midwest Coalition's experience with legislation on another data set

(unemployment insurance [UI] wage records), that nothing spawns research on a

subject or leads to technical breakthroughs faster than Congress directing an

agency to implement something.

All of us here have great respect for the research and analytical

capability of our statistical agencies. We as policymakers don't have to

figure out all the details of how to design data. We do need to set the

direction, to point out the goal, and to issue the marching orders. 1 am

confident that, if Congress enacts legislation directing the development and

employment of state-specific poverty standards, our statistical agencies will

respond to the challenge quickly and competently.

Finally, let me offer you an additional reason for moving on the issue of

state poverty standards now. As you well know, it is not often that the

attention of members of Congress is captured by an issue in the realm of

statistical policy. Right now, the effect of the 1990 Census data on

allocations for our remedial education program, Chapter 1, and the pending

reauthorization of that program are making members more than usually sensitive

to the importance of statistical policy and the need for attention to the

statistical system. I fear that if you do only half the job now•if you enact

what the Census Bureau has already indicated it is prepared to do, i.e. produce

more frequent poverty data•that you will never again have the opportunity to

address the second part of the problem--the ability validly to measure need.

Again, the experience of the Northeast-Midwest Coalition's initiative on

UI wage records is instructive. If we had treated the issue piece by piece

with one bill at a time, we would never have been able to sustain the effort.

I urge you to capitalize on the fact that Chapter 1 is creating a natural

constituency for true, comprehensive reform of the poverty data that addresses

the two problems of timeliness and validity. You will not have this chance

again for a long time.
53

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55

CENTER FOR THE STUDY OF EDUCATIONAL FINANCE

Department of Educational Administration and Foundations

College of Education and the Graduate School

Illinois State University, Normal, Illinois

The MacArthur/Spencer Special Series on Illinois School Finance

MacArtnur/Spencer Senes Number 20

GEOGRAPHICAL COST OF LIVING DIFFERENCES:

INTERSTATE AND INTRASTATE,

UPDATE 1M1

Walttr W. McMahon

Shao-Chung Chang

With cartographic assistance from Kevin Carlock

Canter tor ihe Study of Educational Finance

Illinois Slate University

Normal, Illinois 61781

April 1991
56

Abstract

geographical Coat of Living Dl»er.nc.i:

Interstate and Intraatate. Update 1991

This paper develops a method for estimating currant geographical

differences in the coat of living index for all states for 1981-1990.

These estimates baaed on BLS data are shown to correspond closely to

statewide coat of living estimates for 1989 baaed on the American Chamber

of Commerce Research Association data for selected cities.

Tha papar alao develops estimates of the coat of living aa among

laxga citlaa, metropolitan araaa, and nonmetropolitan areas within each

atata for 1989, and for all countries within Illinois for 1989.

Living costs ara highest in Hawaii, Alaska, Connecticut, Washington

D.C., New Jaraay, Massachusetts, New York, and California. They are lowar

in Mississippi, Meat Virginia, Arkansas, Idaho, and Utah. There ia a 57

parcant difference in tha purchasing power between tha higheat and lowaat

atataa, whereaa the variation in real purchasing power within atataa

between the higher coat large citiea and lower coat metropolitan and

nonmetropolitan areas ia 22 to 35 percent respectively.

The basic pattern of differences peraista since 1977 with ahifta

related to economic growth rataa.

The authora are Professor of Economics, and graduate student in

Economica respectively, at tha University of Illinois at Urbana-Champaign.

The latter was particularly involved in developing Part V on Intraatata

differences. They would like to expreas appreciation to Dale Mitchell and

David Braddock; to Wenhui Hu for able research assistance; as well aa to

Patric Henderahott, Donald Haurin, and to' two anonymous referees for

constructive auggeationa. We alao appreciate the assistance of Alan

Hickrod and the MacArthur/Spencer Foundation who supported work on thia

project.
57

Geographical Coat of Living Differences:

Inttntiti and Intraatata? Update 1991

Halter H. HcMahon and

Shao Chung Ghana

Significant difference* in the cost of living exiet among different

parts of the country* ee well ae among different rural and urban counties

within the same etate. But no systematic eatimatee of differencea in the

cost of living among atatee have been computed eince I960. The Bureau of

Labor Statistics discontinued collecting and publishing ite coet of living

index for 24 SMSA' s in 1981, and the American Chamber of Commerce

estimataa are alao for selected cities and only for the most recent years.

A systematic procedure for eetimating theee differences among states

and localitiee based on the Bureau of Labor Statistics data for the 24

SHSA'a was developed earlier by HcMahon and Helton (1978). The reeuiting

eetimates for 1977 found many uaee. The baeic method wae adopted and

extended by Pournier and Rasmussen (1986) who produced estimates by states

for 1980. But since then there have been large differential impacts among

states following the oil price increases of 1979-80, the recession and oil

price declinea in 1981-83, the effects of high interest rates on exchangerates and agricultural exporting states, and the industrial recovery in

the late 1980s. All of these could be expected to lead to differential

effects on prices and coats among geographical areas and therefore a

changed pattern of geographical coat of living differencee.

The ideal way to evaluate theee differences would be to collect

price data from PMSAs, MSAs, and Nonmetropolitan areas in every state,

weighting these by the population in each area, and to also conduct

detailed budget studies of family expenditures in each of these localitiee

to establish the necessary geographical variation in the weighta to be

placed on each budget component. This procedure would be prohibitively

expensive, however, and therefore likely will never be done in this

detail. The Bureau of Labor Statistics in fact has moved in the opposite

direction by discontinuing the collection and publication of ita cost of

living index in 1981. The American Chamber of Commerce Reeearch

Association ACCRA (1990) has recently started collecting data and

computing an index for selected citiee. But the budget weights do not

vary by geographical area, and the index is not computed on a statewide or

on a countrywide baais.

What is needed is a reduced form (predictive) equation that can be

used to estimate the COL by statss, or by counties within each state baaed

on successive readings on the key explanatory variables in each place at

each date, checking to see that the structure does not change. Thia

paper does this, refining the procedure used in HcMahon-MeIton (1978) and

in Fourmer and Rasmussen (1986) to adapt it to both the kind of data that

are available and to new housing value data that are now available on an

annual basis. The results thsn are croes checked with statewide estimates

based on the ACCRA (1990) sample. The reeult is an index of the
58

cost-of-living estimated for each state from 1981 through 1990 based on

the predictor variables for each state both on a baa* year (U.S. average

for 1981 - 100) and on a normalized annual (U.S. average for each year •

100) baaia. The paper concludes with a brief consideration of the nature

of changes in the geographical differences in the cost of living between

the earlier studies for 1977 and 1980 and the present, as well as of the

trends during the 1980's.

I. Existing Cost of Living Measures and Their Uses

Both the OS BLS (1982) index for 1981 and the ACCRA (1990) index for

1989 are for selected cities, and the geographical boundaries of the

relevant PKSAs and MSA* change over time. The Consumer Price Index (CPI)

is published by the BLS for IS major urban areas, as well as for urban and

rural breakdowns within the four geographical regions for the U.S., but it

has a base of 100 in the base period (1982-84 - 100) and therefore does

not show the initial differences in the level of living costs. The CPI

instead is an index of price changes since that time, whereas in fact the

cost of living in the base year in these places varied considerably. The

CPI alao is not available by state.

The method adopted therefore seeks to take these base-year

differences in the cost of living into account by starting with the last

report for a family cost of living budget for 1981 as reported by the BLS

(in 1982). Attention is confined to those explanatory variables that have

a logical relationship to the cost of living within each of the HSAs,

since as much stability as possible in their predictive capacity is

sought, and also to variables for which data is available on an annual

statewide basis for 1981-1990. For these reasons there are some

differences in the explanatory variables from those used by Fournier and

Rasmussen (1986, p. 184) who do not seek relationahipa to the structural

parameters, or stability over time, because they are concerned only with

the single 1980 census year. However, the explanatory variables used here

do reflect the major factors used in the Fournier and Rasmussen (1986)

analysis as well as in the original HcMahon-Helton (1978) estimates for

1977. After exploring the relationships within these HSAs, the cost of

living index is then generalized to a statewide basis as explained below,

and the stability of the relationship to these same explanatory variables

is explored. This procedure is better than using the HSAs in each year

because as mentioned above the geographical boundaries of the HSAs change

over time, causing problems for explanatory variables such as population

change and per capita income, whereas the statewide measures of these

variables can be expected to be considerably more accurate over time.

The rationale for uses of specific cost of living indexes is

straightforward. Geographical differences in the cost of living affect

the purchasing power of wages and salaries, which are always paid in

nominal dollars, at different locations. For salaries to be comparable in

real terms they therefore must be deflated (i.e., divided by) a

geographical cost of living index such as the one developed here. To

avoid questions of interpersonal comparisons of utility, the BLS concept

2 -
59

of • standard budget for a family of four, which ue us* here, ia ona that

soaka to Icaap tha haad of tho houaahold on tha same indiffaranca curve

with raapact to commodities purchaaod irrespective of whara ha or aha

locatee.

Thia concapt, howavar, includaa tha living coata but doaa not

includa tha non-nonatary banafita of diffarant locations (e.g., tha

aunahina, aaaaida, or accaaa to altarnativa and battar job opportunitiaa),

banafita that partly juatify tha higher coata and that alao affact

location daciaiona. That ia, it may coat individuals sera to maintain tha

aana living atandard in cartain locationa, but thoaa locations may offar

various additional advantages that thay ara willing to pay for. A

geographical coat of living index ia limitad to differences in tha

monetary coata of living auch aa differences for comparable housing

accommodation! in diffarant placaa, which can be substantial.

Tha uaaa that have developed for geographical coat of living

indices, aa wall aa an interpretation of lta potential misuses, dapand

upon thia concapt. It ia usaful to anployaaa in making daciaiona to

locate becauss, to tha extent that tha coat sida ia to be conaidarad in

staking these decisions, it ia what tha salary will buy in raal tarma, not

in nominal terms, plua thair avaluation of tha non-monetary returns that

basically govarn tha outcomes. That ia, in analyzing tha choice, tha

evidence ia that a "aonay illusion" ia not strong, aftar allowing for laga

in adjustment, in which caaa employees would tend to make a correction for

price level and coat of living differences first, and then evaluate the

non-monetary benefite, albeit implicitly. Because of thia behavior,

ssiltiplant firms with planta in different locationa, atata school systems

with urban and rural unit districts, univaraltiaa competing in interetate

job markets, and other kinda of employers who wiah to maintain aalaries

that are comparable in different locationa (plus or minus tha non-monetary

environmental fringes) must alao normally make some adjustment either

explicitly or implicitly both for differences in costs of living aa well

aa for the non-monetary advantages of the higher cost locationa rather

than looking only at'the more purely nominal wage and salary differences -

Some adjustments for non-monetary returns to particular locationa or

regions have been considered by Roback (1988) and by Blomquiat at al.

(1988).

A cost of living index haa alao been uaed to adjuat production coata

or investment coats to raal terms when making geographical coat

comparisons. This would includa econometric estimates of coat functiona

uaing croas-eection data, interstate comparisons of adequacy in education

(e.g., A. Hickrod et al., 1987, p. 9), and comparisons of rates of return

to education such as in the atudy by Israeli (1983) where the author

extends the cost living index for the sampled population of 39 MSAa for an

earlier year to tha non-sampled population of 237 MSAs. A coat-of-living

index is not precisely the same aa tha cost of production, investment

coata, or an index of educational costs, but tha procedure should give a

reasonable approximation in those casaa where wage and salary coata ara a

vary large percentage of total coats, as is true in the caaa of schools

and collages for example. Geographical diffarencea in tha smaller

- 3 -

75-540 0-94-3
60

non-labor costs in these caaaa may alao be correlated with geographical

differences in living coata, but thia ia a point that could ba examined in

apacial cases.

ii. flu 8MB and Tilt idH

Thara hava baan aavaral aarliar attempts to investigate tha sources

of differences in tha coat of living in addition to tha recent ones

mentioned above. Sherwood (1975), for example, uaad tha BU indices and

price data to construct standard budgets that iaolate the effect of

climatic differences on coata. But hia indices are limited to thia one

source of differencea and alao were constructed for only the 44 cities and

ragiona in hia BLS sample. Haworth, Rasmussen, and Mattila (1973) and

Alonao and Pajana (1970) explored the extent to which urban population and

other variables explain diffarencea in tha coat of living within tha BLS

sample, but they did not undertake predictions for nonsampled areas.

Alonao (1970) finds urban population size, when income ia included, to be

of minor significance. Israeli (1977) found that housing diffarencea were

a good predictor of the differential in nominal wages and prices among

selected citiea. But tha only major efforts to extend coat of living

indices from sampled to nonsampled areas hava bean by Simmons (1973, 19B8)

and by HcMahon and Melton (1978). Simmons sampled prices in 12 Florida

counties and than uaed regression equations to extend theee pricea to all

counties in the atate. The first reault, in tha absence of budget atudiea

to obtain the necessary weights, ia therefore closer to a geographic price

index than to a coat of living index. Augmented by budget atudlaa, it haa

been uaad by the State of Florida since 1974 in the Florida school aid

formula. McMahon and Helton (1978) developed a model that explains coat

of living diffarencea within tha BLS aample, and then uaad the regreeaion

coefficients, together with measures of the explanatory variables for the

non-sampled areaa, to extend tha coat of living index to all SO atatea and

to eatimate the coat of living for countiea within California, Illinois,

Pannaylvania, and Taxaa. Fournier and Rasmussen (1986) updated thia aa

indicated above for atatea for 1980, but only for thia one Cenaua year.

Mow tha data availability haa changed, and there ia need to update the

index on an annual baaia for tha 1981-1990 period.

Economic theory auggasts that changea in the effective demand for

goods and for housing, especially when auppliea are not perfectly elaatic,

can play a large part in tha determination of geographical diffarencea in

living coata.

The demand function for goods and services in any given locality

expreaaaa the quantity demanded primarily aa a negative function of price

(a, < 0), a positive function of per capita income in the locality (a2 >

0) and a positive function of both assets in housing, H, and

imputed housing user coata (a}H) which include capital gaina and loaaea:
61

U> q • e,p • m,r • e,/V • e.AP • u,

»

Here p • a pries index relevant to good* and HJVICII purchased in the

q - • market basket of good* and aarvicaa naadsd to auatain t

family of four at the same level, irrespective of tha aita,

COL - pq » tha coat of living.

M • value of tna houaa of givon siza and quality (aaaaurad hara aa

tha aadian aalaa prica of existing singla•family homes

available froa tha National Aaaociation of Realtors (1990),

aP - parcant changa in tha population in tha araa over tha

pracading fiva yaara, and

u, - diaturbaneaa.

Tha structural factors ahifting tha daaand function, Y, H, and OP, have a

logical baais in economic thaory and can firat ba conaidarad briafly.

Individual ineoaa is a critical element in tna demand for virtually all

gooda and aarvicaa, raising daaand by ahifting tha budgat constraint

outward whan ineoaa is highar bacauaa most goods ara normal goods (a. > 0).

Hhera supply is inalastlc (as in tha casa of land prieas), aapacially for

those ltams that ara not tranaportabla or gaographically aobila, thia can

bid up tha pries and lsad to gaographical diffarancas in living costs.

Consumer demand can alao ba incraasad by an aasat affact, and tha

valua of housing, H, is a aignificant component of total assets. Tha Life

Cycle Hypothesis of Ando and Modigliani (1963), which with various

extensions by Friedman, Heckman, and others dominates tha thaory of tha

household, measures it by uaing the total stock of asaata or not worth.

But such a comprehensive measure of all assete is lees rslevsnt for

purposss of analysis of geographical price diffarencea than ara tha assets

specific to tha locality in tha form of equity in housing. Apart froa

this asset effect, it is also that land is immobile resulting in an

inelastic supply, so that when demand rises, housing prices are driven up

which means a highar imputed annual user-cost of housing. Sherwood (1975,

p. 14) found that out-of-pocket housing costa vary widely among areas,

ranging from an index of 168 in Boaton to 6B in Austin, Texas. Using the

median aales price of housing in a locality as an index to housing costs

and aa a measure of paat aaaet accumulation that includes capital gains

and loasaa has ths further merit of being a measure that la widely

available for all yaara for many large and email metropolitan araaa on an

annual baais from the National Aaaociation of Realtors (1990), whereas

both housing coate and the more comprehensive aaeet measures ars not.

Climatic difforsnees also may havs effects on differences in living

costs. So we will explore below the merits of using an additional

variable for climate, C.

- 5 -
62

Population growth has ambiguous effect* on price*, aa waa itrtiMd

earlier by McHabon and Helton (1978, p. 326). Rapid population growth

accompanied by effective purchasing power can increase thai pressure on

some facilities other than housing, and act to raise their prices (i.e.,

a4 > 0). However, par capita income ia a better measure of effective

demand, and because it and H are included aa variables explaining par-unit

costs, thia effect of only population growth (that does not necessarily

hava the purchasing power) ia laaa likely to be strong. On the other

hand, economies of acala in certain services such aa schools and city

services also can be achieved aa pointed out by Alonso (1970, pp. 72-75)

(i.e., on the aupply aida below a7 < 0).1The net effect cannot be

inferred from economic theory, but because of the large migration toward

the south and the aun belt atataa during the 1980a where economies of

acale could be meaningful, the hypothesis is that thia relationship will

be negative (a4 < 0).

The aupply aquation expresses price aa a positive function of the

quantity auppliad both in the short run and in the long run (as > 0), aa

well aa of houaing coata aa mentioned above (a6 > 0)i

(2) p - e,g- • a,H • a,AP • u,

where p, " disturbances and all other variables have been defined under

equation (1). Assuming linearity, the demand and aupply functions may be

aolved simultaneously eliminating q. The resulting reduced-form price

equation then can be multiplied throughout by the appropriate quantity

weight q repreaantlng the market basket of commodities in the standard

budget for a family of four. Because theae quantity weighta are designed

to maintain the aama level of well being in each area, they are treated aa

conatanta and aa part of the parameters in aquation (3) below. Thia

reault contains tha key determinants of the coat of living, COL, in each

locality:

*^ l/«»-«i !/«,-«, I/*,-*.

Because a* < 0, all denominators can be expected to be positive. The first

two numerators relating to Y and to H also can be expected to be positive

as suggested above, and because the hypothesis is that a7 > 0, the sign of

the third numerator is indeterminate.

III. Estimation of the Model

Tha parameters can be simplified as shown in equation (4), the model

to be estimated. Here fl. and n? are expected to be positive, and Bj to be

indeterminate, but probably negative since the positive effects of

population increase on the demand side are likely to be picked up by Y and

H, whereas the negative effects due to economies of scale and the movement

further out and to retirement communities remain:

- 6
63

<«) COL • p,r • M • M^ • «

The definitions and data aourcaa for the variablaa arai

COL • Coat of Living Indax, for 1981 for 24 MSAa and 4 ragional

non-matropolitan araaa aa published by tha U.S. Bureau of Labor

Statiatica (1982, p. 45). Thaaa and tha ACCRA (1990) maaauraa

for tha respective aata of atataa within which sample data for

aalactad localitiaa exiete are extended to a atatawida baaia in

1981 and 1989 raapactivaly by uaing a waightad average of tha

MSA and non-metropolitan component! of tha COL. Weights

conaiat of tha percent of tha population that ia metropolitan

va. non-metropolitan in each atata from tha U.S. Buraau of tha

Cenaua.

X • Par Capita Paraonal Income, in thouaanda of dollara. For

atataa this ia from U.S. Department of Commerce Survey of

Currant Buainaaa (1990) where it la alao available for thaaa

MSAa and by county within each atata. (Disposable income ia

not available on an equally conalatant baaia.)

H - Value of Housing, measured aa tha median value of an exlating

one-family home. Thia ia available from tha Census of Housing,

U.S. Department of Commerce for 1980 only, and froa tha

National Association of Realtors (1990) aa reported in the U.S.

statiatlcal Abatract (Table 1236) for 1981-1990.

AP - Percent Change in Population, for tha preceding five yaara,

from Currant Population Reports, Sariea P-2S, U.S. Department

of Commerce (1990, p. 16, Table 1), and various other iaauea.

Tha reaulta obtained for tha regression for tha MSAa in 1981, tha

laat year the BLS collected data, and for tha regreaaiona uaing atatawida

data for the corresponding states for 1981-1990 are shown in Table 1. The

signs are all aa expected, and the t-atatiatica indicate that all

coefficianta reach the 0.05 level of aigniflcanca or above except for that

on Y in a few of the earlier yeara where it is cloaer to the 0.10 level of

aignificance. Multicollinearity among tha explanatory variablaa ia

reasonably low (aa shown In Appendix A), with the expected positive simple

correlation between Y and H of .38 the highest. Tha R

J

aa shown above ia

raaaonably good for croaa aaction data (and highest in the moat recent

yeara).

Turning to tha statewide regreaaiona (Eqna. 6-15), the procedure uaad

ia one of firat conatructlng a atatawide 1981 COL indax for the atataa in

which the MSAa are located by weighting the BLS index for tha 24

(metropolitan) MSAa and their index for the nonmetropolitan areaa by each

atataa' diatribution of population aa between metropolitan and

nonmetropolitan araaa. In Table 1, a comparison of Eqa. (5) and (6)

raveala regression coefficienta for the MSA and atatawide data that are

very similar. In Appendix A, a teat ia ahown to aee if aa between the two

75-540 0-94
64

Table 1

Haior Determinants of Coat of Living Difference

(t-atatiatica are in parentheaoe)

MSM. n - 24i

a!

(5) 1981 COL - .001SY • .178H - .57AP • 74.1 .514

(0.952) (2.04) (-3.05)

Statewide (Population Weighted) Meana. Baaed on BLS Data, n-22:

(6) 1961 COL - .002Y • .1S2H - .564P • 67.6 .552

(1.63) (2.61) (-2.22)

(7) 1982 COL - .002Y • .163H - .62AP • 74.4 .463

(l.SS) (1.87) (-2.17)

(8) 1983 COL • .002Y * .191H - .65AP • 72.3 .549

(1.89) (2.23) (-2.24)

(9) 1984 COL - .002Y • .274B - .90AP • 72.4 .635

(1.77) (3.19) (-2.74)

(10) 1985 COL - .002Y • .285H - 1.12AP • 72.8 .758

(2.34) (4.63) (-3.65)

(11) 1986 COL • .002Y t .289H - 1.37AP • 74.2 .811

(2.27) (5.40) (-4.21)

(12) 1987 COL » .0014* • .266H - 1.54AP • 83.9 .806

(1.74) (4.96) (-3.70)

(13) 1988 COL • .002* * .202H - 1.62AP • 84.3 .804

(2.54) (4.23) (-3.35)

(14) 1989 COL - .002Y • .154H - 1.40AP • 7S.3 .778

(3.40) (3.37) (-2.62)

Statewide (Population Weighted) Meana. Baaed on ACCRA Data, n - 34

(15) 1989 COL =• .002Y • .141H • .01AP • 62.5 .870

(5.44) (7.48) (0.13)
65

regressions there is • change in the structure. The null hypothesis

cannot ba rejected, indicating that thara is no significant change. This

is not surprising because the inference is that at least the firat two

variablea are atructural factors. Also a very high percentage of 'the

population in almost all theee 22 states is metropolitan as opposed to

nonmetropolitan or rural.

Whan climate, C, is added to the regression measured as a dummy

variable with a value of 0 below the Mason-Dlxon Una, including

California, to reflect the lower heating coats in sun-belt etatas,

especially while oil and related energy prices were very high, the result

shown below in Iq. (16) is typical.

(15) 1981 COL • .0013V • .233* - .44AP • 5.21C • 68.9 **

(0.863) (2.63) i-1.64) U.09)

Uelng "climate" would seem to have more logic than merely uaing regional

dummies. But although the R

J

is slightly higher, the significance of each

of the other explanatory variables is reduced in comparison to Eq. (6),

and the t-statiatic for climate never reaches the 0.05 level in any year

from 1981 through 1990. So given this lack of significance, climate waa

dropped aa an explanatory variable.

Other regressions ware teatad, uaing population levels in place of

the change in the population over time, for example. But when per capita

income is included as an explanatory variable, aa ahown in Eq. (16) below,

population level, P, is not insignificant:

(17) IMXCEB - -002y« .172ff- .SSflP* .00001P • 67 .1 R' • .564

' ' (1.58) (2.39) (-2.09) (0.02)

The R

2 is no higher than whan P is dropped, as in Eq. (6), and lower than

whan climate is used instead of P in Eq. (16). This insignificance of

population levels when per capita income is included was discovered

earlier by Alonso (1970).

It is impossible to test the regressions as shown for years prior to

1981 because the number of KSAs covered in the National Association of

Realtors (and hence also the Statistical Abstracts) data on the median

sales price of existing housing, H, diminishes and is totally inadequate.

However, for further tests on the stability of the coefficients, the SMSA

and statewide 1981 COL were updated for the years following 1981 (the

latter shown in Table 1) by use of the consumer Price Index, which shows

percentage changes from the base year. For the 24 MSAs, the CPI is

available, and the results of a second test for change in the structure

from 1981 to 1989 is shown in Appendix A. This again reveals no evidence

of significant structural change. For the statewide COL, the percentage

increments from the base year in the CPI for metropolitan vs.

nonmetropolitan areas were weighted by the percentage of the population

living in metropolitan vs. nonmetropolitan areas in each state or region.

This weighted percentage change in the CPI then was used to update the

- 9
66

base level 1981 COL. The CPI la baaed on budget studies that reflect the

changing budget proportion* over time in purcnaaea In each area, and theae

CPI weights arc periodically updated.2Thia method of updating In the

sampled areas la alao the method uaed earlier by the BLS to update their

own index.

Aa a further check on the accuracy of the COL estimates in recent

years, an independent data source for selected communities in 34 states

sampled by the American Chamber of Commerce Research Association was used

to create an ACCRA-baaed statswide COL index for 19B9 for theae states.

The method is the same as that used to create the statewide index based on

the BLS sample aa described above. Specifically, all of the communities

sampled by ACCRA within each state were grouped by PHSA, MSA, and

nonmetropolitan areas (using neighboring statea in those cases where there

was no nonmetropolitan area sampled). The means within each category then

were weighted by the proportion of the population in that state in PKSA'a

(if any), MSA's, and nonmetropolitan areas. The resulting weighted mean

COL for each state in which sample data exists was used in a regression

containing the same explanatory variables as shown in Eq. (IS) in Table 1.

There the significance of per capita income, Y, and housing, B, is very

high, exceeding the .01 level. The regression coefficient for Y is the

same and the coefficient for H is very similar to those in the BLS-based

regressions. Population change, AP, however is not significant, with t •

0.13. Appendix A shows simple correlationa among the explanatory

variables that are in the earns pattsrn (for this different sample) as for

the BLS regressions. A test for differences in the coefficients does

reveal a significant difference, undoubtedly due to the difference in the

population change coefficient. The statewide COL estimates for the 50

states based on these independent BLS and ACCRA data sources will be

compared and discussed shortly below.

All of the regreaaion results suggest that differencea in the median

sales price of housing emerge as the most significant source of

differences in the coat of living, although per capita income alao is

important. However, the median house prices, H, as reported by the

National Association of Realtors overstates increases in constant-quality

house prices by about 2 percent a year, as shown by Hendershott and

Thibodeau (1990, pp. 328, 333). Thia overstatement is significantly

related to changes in real income (see ibid., 1990). Therefore the

increases in prices of a constant-quality house are likely to be somewhat

less important, and larger per capita income somewhat more important as

determinants of differences in the cost of living than the regression

results might suggsst, given that H is a fraction of Y. Direct

out-of-pocket housing costs account for about 23 percent of a typical

household budget, and the imputed own equity contribution due to capital

gains and losses which vary together with H are likely to account for even

more. Higher per capita income is alao especially significant in

Connecticut and the Northeast seaboard.

The effect of the percentage growth of population is not a major

explanatory variable because its regression coefficient is multiplied by

ths very small values for AP as compared to Y and H, its effect la not

10 -
67

only emeller but also insignificant In the ACCRA-baaed ragraaalon.

Nevertheless lowar coata dua to economies of acala in public services and

perhapa movement by higher income and retired paraona following tax cuta

in tha 1980a to placaa lika Raw Hanpshira (from Boa ton) and toward tha

retirement aunbalt atataa may atlll ba a minor factor.

IV. Geographical Dlffarancaa in tha Coat of Living

Tha Raaulta

Tha dlffarancaa in tha coat of living among tha SO atataa and tha

District of Columbia ara shown in Tabla 2 with tha 1981 D.S. average

traatad aa tha baaa yaar. Tha index la obtalnad ualng tha atatawida

ragraaalon aquation (6) shown in Tabla 1 together with measures of par

capita personal income and tha median aalaa price of existing single

family home for each atata and for each yaar iron 1981-1990, aa wall aa

the percent change in population for tha preceding five years for aach

atata from 1977-1990.JTha coat of living index than waa normalised, with

tha raaulta shown in Tabla 3, so that 100 repreeente tha national

unweighted average for each yaar for all atataa.4

Theae raaulta in Tabla 3 indicate that there la a 42 percent

variation in tha coat of living in 1990 among states in tha continental

0.8., and a 57 percent variation if Hawaii and Alaska ara included. Tha

higher coat of living atataa continue to ba in tha Beat, Connecticut, Raw

Jersey, and tha District of Columbia in particular, plua Alaska and

Hawaii. In these placaa higher Incomes, higher prices, and higher housing

coata ara all a factor. Tha lowar living coat atataa are those in tha

South, such aa Miaaiasippi, Arkansas, Alabama, South Carolina, Arizona,

and New Mexico, where there ara lowar heating coata, and laee population

denaity may contribute to lowar coata of land. Tha Midwaatern and North

Central atataa remain in the middle.

Tabla 4 shows the normalized cost of living index for 1969 baaad on

the ACCRA regression (Eqn. 15) compared to the 1989 index baaed on the BLS

data. Thaaa are rank ordered from highest to loweet coat ualng tha ACCRAbased index. The percentage dlfferencee shown on tha right are quite

small, conaidaring the differencea in tha concepts diacuaaad below, with

a difference of leas than 3.3 percent between tha estimates in 75 percent

of the atataa. The differences range from 0 percent (when rounded) in

North Carolina, Nebraska, Wyoming, Oregon, Taxaa and Otah to a high of

-8.23 percent in Arizona, +7.56 percent in Hisaouri, +7.03 percent in

Rhode Island, and -6.58 percent in New Mexico.

Examination of tha reasons for theae differencea reveala three

sources, that may ba uaaful to those wiahing to make evaluative judgmenta

in tha usa of tha results:

1. In a few atates where there are very large cities, the ACCRA samples

are sometimes confined to one suburb that may not ba representative,

e.g., Naasau-Suffolk to rapreaent New York, Schaumburg for Chicago.

- 11
68

This appears most frequently to lead to a email over-estimate of

costs by the ACCRA-baaed index.

2. In other states there are large cities that are not aampled by ACCRA

(e.g.. Providence, RI and Alexandra, VA), or that are grouped by the

U.S. Census with the MSAs even though they contain more people (e.g.,

St. Louis and Kansas City, MO). Since they are underrepresented,

this could lead to an underestimate of costs ueing the ACCRA-based

index in these states.

3. Some states have had a huge influx of population in the five years

leading up to 1969 (e.g., Arizona +16.7 percent, Florida # 14.6

percent, 7.1 percent in Mew Mexico, and 15.5 percent in New

Hampshire). Thia could contribute to aome understatement of the true

cost of living by the BLS-based index for these placaa. There are no

percentage losses of population in any state that are anywhere near

thia large.

4. Beyond this, there is the more general point that the ACCRA-based

index uses the same budget weights on prlcee in all regions (e.g., no

heavier weight for the higher heating coats in Maine). So the

concept is slightly different, and the ACCRA index ia perhaps

somewhat closer to a geographical price index than to a coat of

living index.

Therefore some differences in the BLS-based and ACCRA-based atatewide

indices are to be expected. But overall, the relatively small percentage

differencea in the two estimates, the very small differences in the range

from highest to the lowest, and the reasonably close correspondence in the

rank order serve to increase confidence in the accuracy of the estimates

in Table 3, perhapa substituting the ACCRA-based atatewide estimate in

those three or four states indicated in point #3 above that have had

extraordinarily large increaaea in population in the late 80'a.

With respect to changes over time, the pattern remains much the same

as in McMahon and Melton (1977). Living coats in Massachusetts,

Connecticut, District of Columbia, Alaska and Hawaii, which were high in

1977, now are relatively even higher. And the lower coat of living areas

such as Kentucky, Louisiana, Mississippi, West Virginia and Wyoming now

are relatively even lower. The recession in the farm states throughout

the 1980's lowered living costs there since 1977 relative to the other

states, and the industrial recession in 1981-8 3 lowered per capita incomes

and relative living costs in the industrial states. But then the later

industrial recovery from 1983-89 also appears to have been a factor in

raising demand and costs. In this recovery period, increaaea in the coat

of living begin to occur in Massachusetts, Virginia, and parts of the

industrial midwest that perhaps have been arrested by the 1991 recession.

- 12 -
69

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71

Table 4

id* COL Index via Prediction Equation and ACCRA Sample Compared

1989

P.E. STATE Prediction ACCRA ACCRA Difference

ink Equation Normalized COL between P.E.

Normalized COL Non- and ACCRA

COL normalized Normalized COL

3 Alaska 125.46 129.15 133.14 -2.85%

1 Hawaii 127.44 128.52 132.50 -0.86%

2 Connecticut 125.98 125.22 129.09 0.61%

4 Die-Columbia 123.27 121.71 125.48 1.28%

5 New-Jersey 121.07 118.39 122.05 2.26%

6 Massachusetts 119.87 115.73 119.31 3.58%

S New-York 111.53 115.44 119.01 -3.39%

7 California 118.14 113.66 117.18 3.94%

13 New-Hampshire 105.93 110.04 113.64 -3.74%

10 Delaware 108.11 109.25 112.63 -1.05%

17 Pennsyvania 100.75 107.88 111.22 -6.62%

12 Maryland 106.25 104.60 107.84 1.57%

9 Virginia 109.58 103.84 107.05 5.53%

16 Illinois 102.49 103.27 106.66 -0.75%

20 Michigan 97.89 103.OS 106.26 -5.01%

31 Florida 94.59 100.68 103.79 -6.06%

11 Rhode-Island 107.52 100.66 103.57 7.03%

22 Washington 97.43 99.01 102.07 -1.59%

14 Maine 103.13 98.13 101.17 5.09%

46 Arizona 89.89 97.95 100.98 -8.23%

15 Vermont 102.72 97.44 100.45 5.42%

23 North-Carolina 97.25 97.34 100.3 5 -0.10%

28 Nevada 95.20 97.04 100.04 -1.89%

44 New-Mexico 90.09 96.44 99.42 -6.58%

21 Wisconsin 97.78 95.76 98.72 2.12%

18 Minnesota 100.27 95.47 98.42 5.03%

32 Georgia 94.20 95.19 98.13 -1.03%

19 Colorado 100.08 9S.13 98.07 5.21%

25 Ohio 96. 86 94.90 97.94 2.06%

34 Nebraska 94.16 94.59 97.52 -0.46%

30 Wyoming 94.63 94.39 97.31 0.67%

33 Oregon 94.16 94.29 97.21 -0.14%

42 Louisiana 90.97 94.22 97. IS -3.47%

35 Texas 93.81 93.86 96.76 -0.05%

27 Indiana 95.67 93.35 96.7S 1.94%

38 North-Dakota 92.22 93.65 96.55 •1.53%

41 Montana 91.54 93.60 96.50 -2.21%

29 Iowa 94.98 93. 3C 96.19 1.79%

4 3 Alabama 90.51 92.79 95.66 -2.46%

45 South-Dakota e9.?4 92.65 95.52 -2.93%

47 South-Carolina 89.65 92.09 94.94 -2.65%

26 Kansas 95.83 91.79 96.63 6.40%

36 Tennessee 92.91 91.68 94.52 1.34%

49 Arkansas 88.38 91.54 94.37 -2.91%

48 Idaho 89.18 90.62 93.62 -1.59%

39 Kentucky 92.13 90.60 93.60 1.75%

51 Mississippi e7.51 90.50 93.30 -3.30%

37 Oklahoma 92.91 90.21 93.00 3.00%

24 Missouri 9C.S9 =0.07 92.86 7.56%

••0 Vest-Virginia 91.£9 9°.=5 92.'': 1.94%

50 L'tah se.s: ?».0i 91.81 -0.:5t:

L'.S. L'nweiahted Av«. 100.00 100.00 103.09 o.o:;
72

v. Coat Differences Within statea

Walter w. McKahon and Shao Chung Chang

Differences in the cost of living among larga citiea, medium sized

cities, and nonmatropolitan araaa in each atate are developed in Table S

baaad on tha ACCRA data collected for selected citiaa. These results will

be compared to coat of living estimates for each county in Illinois

(Table 6) and metropolitan nonmatropolitan differences baaad on these

independent BLS-baeed regressions (in Table 7). Tha results of tha two

indapandant approaches again are reinforcing. But the reaulta also again

reveal some locations where there appear to be sampling errors in the

means that are baaed on the ACCRA data.

MetroDolitan-Nonmetropolitan Differences

To estimate differencea in the coet of living between larger citiea

and nonmatropolitan areaa, all of tha locationa sampled by ACCRA were

firat grouped within each atate by PKSA (> l.S million population), HSA

(1.5m-50,000) and nonmatropolitan araaa (< 50,000). Since many atatea

have no citiea large enough to be a PMSA, there are blanks for thaae

atatea in column 1 of Table 5. Where there were no nonmatropolitan area

data collected (aae • in Column 3), or where the ACCRA data ia for only

one location (aae c in Table S) the atata waa pooled with data for the

same city size claaa in adjacant atatea. For some of the very largest

citiea such aa San Franciaco and Chicago, ACCRA haa collected data for

only one suburb (marked b in column 1, Table 5). In these cases a

regression equation baaed on housing valuea, per capita peraonal income,

and population change la uaad to obtain tha estimate shown. Estimates at

thia level of detail should be uaed with some caution, and with one eye tg

the citiea in which data waa actually collected by ACCRA because there ia

some variation within each size category. Nevertheless certain patterns

emerge.

The coat of living is distinctly higher in larger citiea, and only

slightly higher in the medium sized metropolitan areaa from that in

nonmatropolitan towns of < 50,000. There ia a 74 percent difference in

the coat of living aa between the higher coat citiea and the loweat

nonmatropolitan areaa in the U.S. Thia is larger than the 57 percent

variation in the state averages in Table 3, aa might be expected. But

even within the same state, the real purchasing power of the persons

living in the largest cities is 22 to 35 percent below the purchasing

power of those living in medium sized citiea and nonmetropolitan areaa

reapectively.

- 16
73

l*bl» 1 :o»l of Living

Per L*r*» Citsm. ^irw>nm*n f

_.__ L*r«>» City MM'* If. •tr•oUu

iT*Tt (»•» 7 1.9 a) Cl.9 •.-9O.00O) <»•» < 9O.0O01

RtwrtiW-li

••ft FfMCIIU

Colorado

•CtlDIt

Dal*•r.

01%-COluaOla

rlor id*

Hl*at-Hl*l*»h

44.02 44.40 •

127.40 137.10

101.19 100.43

44.SO •3.10

114.73 44.29 •

tso.ro

1ST.SO

110.34

I91.M b

lit.to

•V.A3 •3.49

1 .xo

131.79 ••.32 a

112.09 c ios.m

129.90 *

1O1.00 •7.20

113.90

44J.49 c 40.30

132.SO • 132.90

44.10 •2.79

109.9* 97.39

ISO-10 b

••.77 •9.4* c

44.90 49.4»

•9.47 41.M

•a.oo rs.49 >

104.OO • 44.30 a

100.SO 101.00 •

120.29 ••.SO •

104.43 103.90

100.03 49.23 •

•4.02 * ra.so

04.49 00.49

•5.41 • 43.04 •

•2.49 04.33

1O4.07 4 104.40 4

122.30 44.33 •

122.09 c 122.09 c

M««* vortt

Horth-C*rollni

tbjrtft-Oataot*

Ohio

r>hll*4*>lrjni*

PHUburqh

RhCMt-Ul4ni)

Soutn-CorolIA*

Soutlr¬J

100.09 •4-0*

109.02 44.90 C

137.73 b

131.4* b

44.14 44.00

•0.40 49.23

•0.24 44.07

111.44 b

43.79 07.00

44.00 44.40

104.40 44.90

124.20

10*.io

103.44 • 44.33 •

44.40 42.70

•4.40 44.49

•9.30 •2.43

49.04 44.09

104.20

44.10

•2.10 40.00

103.44 • 44.33 •

113.27 101.OO

47.42 42.TO

113.20

43.17 42.07 4

44.0O 44.10

49.41 c 43.04 e

'•SMS4

Dall*«

HOW* ton

Utah

Vraont

Virginia

hta«tiSf»4lon

•OOTttOO

Mnt-vtr«ini«

Wl«con*in

Myenino

Data •• not availaBle. to the indci UKI data from an jj:acier.t sure <or citvl

Data is the nine JU Alabama, cecause tnere are no MSA's in Mississippi

COL predicted using regression equation taaed on BLS sample A! eirUintJ in «,Mihon (1491) li uses data

on housing values, pet IJPHI personal income, and population chance iptfcmc to each latec cirv. The

resulting prediction lot each cut indicated if i i» U-ioic normalisation to a state«ide Bate oi 14ft To

accomplish IMI adjustment, J regression equation « JJ computed in each caie lor a neighboring citv that docs

nave ACCRA data, and Hal ratio oi tr-.e HLa tased ffOJWtlOII to the ACCRA estimate in the neighboring Ckfl

is used to normalise' tne BLS-ejuaiion predictions to tne same rase.

The data presented tv ACCRA data ii incomplete and ts not rerresemaiive. or n missing, so the rectorial

tiwlei (or the respective MSA * or Non-metropolitan areas is uied.

For NevaJa MSA s end Not)• metropolitan areas resrccltteiv, l***»9 .inj |44C ACCRA Jata is pooled

-17-
74

By Counties

Differences in the cost of living by counties in Illinois have been

estimated using the BLS-based regression equation and date on 1989 par

capita income, housing values, and population change for each county. The

results shown in Table 6 are normalized to a statewide average of 100

using first a mean weighted by the population of each county, and than an

unweighted swan giving equal weight to each county. The population

weighted mean is more relevant where expenditure are being distributed (as

in a state school aid formula), whereas the unweighted mean would be more

relevant to an individual trying to decide whether or not to move from one

location to another. However, when the counties are rank ordered, the

rank order is totally unaffected by the type of mean that is used for

normalization, and the percentage difference from the higheet to the

lowest is not significantly affected.

The pattern of estimated coet of living differencee within Illinois

is illustrated in the map in Figure 1 (normalized using the unweighted

nin|. There ie a 62 percent variation from the higheet (Lake County) to

the lowest (Johnson County) cost of living location, cloae to the 57

percent variation among the state-wide averages but smaller than the 74

percent variation between the larger cities (San Francisco, New York) and

Che loweet cost nonmetropolitan araaa (in Missouri, Kansas, and Oklahoma).

When Illinois counties are grouped by PMSA, MSA, and nonmetropolitan

areas as shown in Table 7, and rank ordered by cost of living within eech

group, the pattern discussed above based on the ACCRA data again emerges.

The population-weighted mean within each group of counties is within 1.6

percent of the ACCRA-baaed estimates in all cases aa shown at the bottom

of Table 7.

VI. Potential Applications to Education in Illinois

The implication of using regional coat differences such as those

presented here baaed in the cost of living in state school aid formulae

requires brief comment.

The coet of living index could be made specific to each school

district using the regression equation presented in this paper based on

the 1989 ACCRA data (Eqn 15). Personal income per capita is available by

school district baaed on state income tax returns. For 1990 the taxpayer

wee given a list of four digit school codes which greatly improves the

accuracy of the reporting. Percentage change in population is available

only by county, but since this variable is not significant, the countywide change may be a suitable proxy. Median houae prices are not

available by school district for recent years, but the Housing Census for

1990-91 may make these available eventually. The alternatives to this

would be to use the county cost of living estimates (Table 6) or the large

city metropolitan-nonmetropolitan averages relevant to each school

district.

- la -
75

Table o

Coit of living bv Councv in Illinois

19S9 1989 1969 1989

COUNTY Normalized Normalized COUNTY Normalized Normalized

Weighted COL Unweighted COL 'Weighted COL Unweighted

Adams 90.73 102.53 Lee 89.27 100.89

Alexander 78.23 88.41 Livingston 90.19 101.92

Bond 84.62 95.86 Logan 87.35 98.71

Boons 93.9* 106.17 Macon 92.48 104.51

Brown 89.07 100.66 Macoupin 85.05 96.11

Bureau M.Si 102.68 Madison 89.43 101.06

Calhoun 84.09 95.04 Marion 86.91 95.96

Carroll 88.49 100.00 Marshall 92.88 104.97

Cass 86.16 97.37 Mason 86.19 99.67

Champaign 90.39 102.IS Massac 80.05 90.46

Christian 87.44 98.82 McDonough 85.68 96.83

Clark 63.46 94.32 McHenry 95.84 108.31

Clay 84.32 95.29 McLean 92.19 104.19 -

Clinton 89.28 100.90 Menaxd 88.92 100.49

Coles 8S.37 96.48 Mercer 89.13 100.73

Cook 105.32 119.03 Monroe 98.60 111.43

Crawford 89.04 100.63 Hontogomery 85.08 96.15

Cumberland 81.98 92.64 Morgan 88.55 100.07

Da Kalb 92.92 105.02 Moultrie 86.93 98.24

De Witt 87.54 98.93 Ogle 92.52 104.56

Douglas 84.38 95.36 Peoria 95.88 108.35

Ou Page 113.54 128.32 Perry 85.91 97.09

Edgar 86.07 97.27 Piatt 91.01 102.85

Edwards 85.38 96.49 Pike 63.67 94.33

Effingham 86.99 98.31 Pope 7 6.9C 86.90

Fayette 82.24 92.94 Pulaski 76.18 86.09

Ford 91.26 103.14 Putnam 94.87 107.21

Frinklin 83.73 94.62 Randolph 83.70 94.59

Fulton 88.19 99.66 Richland 86.80 100.35

Oallatin 64.23 95.19 Rock Island 93.60 105.78

Greene 60.99 91.53 Saline 87.01 98.34

Grundy 95.52 107.95 Sangamon 93.86 106.07

Hamilton 81.41 92.01 Schuyler 85.19 96.27

Hancock 86.10 97.31 Scott 84.41 95.39

Hardin 78.06 88.22 Shelby 84.52 95.52

Henderson 63.63 94.51 Stark 92.37 104.39

Henry 90.76 102.57 Stephenson 93.61 105.79

Iroquios 67.56 98.95 St. Clair 86.97 98.29

Jackson 65.49 96.62 Tazewell 93.92 106.14

Jasper 85.34 96.45 Union 63.24 94.07

Jefferson 87.62 99.02 Vermilion 86.69 97.97

Jersey 84.22 95.18 Vabash 86.26 99.75

Jo Daviess 86.60 100.35 Warren 89.05 100.64

Johnson 70.42 79.56 Washington 67.44 98.82

Kane 100.05 113.07 Wayne 87.33 98.70

Kankakee 69.16 100.76 White 87.20 96.54

Kendall 102.30 115.61 Whiteside 90.22 101.96

Knox 64.42 95.41 Will 95.90 106.3t

La Salle 69.88 101.58 Williamson 84.T2 95.06

Lake 114.39 129.27 Winnebago 95.28 107.68

Lawrence 87.87 99.30 Woodford 92.00 103.97

Population-Weighted COL Mean

Unweighted COL Mean

100

100

"J
76

. of m-t.aa« Count* .rwi fcrrayn•fl e•lM..,

ittf Ittt l*t* •aah own M•M•<

towauhtad

cot

:«p * of Total

top

MM

1 L*k. lit.1? 4T9100 4.4* •St.**

3 Ou Fata lit.11 '40400 10.12 1111.0*

3 Cook lit.01 M44J0O '01' •44*.91

tit.fti 11400 o.ai 5* 71

3 RAM 11 JO? 314*00 4.» 4*1.04

7 Hill 1««.1« 144100 4.ft* 904.41

101.11 1T1100 3.10 14ft.ft*

10 OtWrfT 101.tt 32900 0.44 47.11

Total Population 1444100 100.00

4aamlatla«i-Hal«Bta4 MMfi lift, ft*

NSk

111.41 32100 1.01 111.17

t P«urn 101. N ItllOO l.lt 104.13

11 UMMbM* 107.4ft 291100 11.ft? 114*.11

104.11 10100 1.11 144.71

14 Ta» •ii 104.1. 114100 ft.Tl 40*.11

1ft ftl•l•l 10 4.01 1I0O00 t.l* •Tft.ftt

10ft.Tt 41100 l.lt lit.49

1? IbMh [alanal 10ft.lt 199400 T.14 T9ft.44

11 RMW 104 51 111T00 ft.** ttl.ftl

13 fteLaan 104.lt 124100 ft.Tl 1*4.44

14 Ww-jford 101.TT 11*00 l.ftl lftT.01

102.IT 91100 1.4ft IftO.M

10 Q•itti 101.1ft 112100 T.tO •07.0ft

Ik Madia** 101.Oft 191100 11.ft* 11T0.C1

39 Ql«tM 100.to 14100 l.ft* lift.41

10C.I* titoo 4.4* 491.ftft

ftl •*. Clair tft.lt 1C9TM 11.M 1314.97

•4 J.r.ay tft.lt 10*00 O.tft 10.01

TM«1 Po«^la t tor. 1111100 100.00

toa*a*tlan-iJat*ht*4 Man 10ft. 09

•OMOTTItOKL ITW MM

12 PKMi 10T.31 9T00 0.1ft 10.70

It to r.lb 10ft.01 T4O00 l.ftl 404.ft*

IT Kinhill 104.97 11100 0.44 41 41

10 0«1« 104.>« 4*140 l.ftl 141.70

13 lUrt 104.lt 4400 0.31 14.U

IS Ford 103.14 14700 0.7* 74.17

i« ri*tt 102.19 14100 O.ftl •1.T1

11 IdTMU 101.41 J4IOC l.ftft 1*9.*9

39 ftaaaa 101.11 41400 1.40 34ft.11

11 Whllatlaa 101.tt 43900 1.14 120.1*

13 Llvinaiion 101.tl 40400 1.01 :o4.*a

3J La Sail* 101.St 10*100 i.i* 547.ft*

34 La* 100.tl 14000 1.T9 :i*.i*

11 Marear 100.71 14100 0 11 91.10

)1 9r*w» 100.44 9000 0.19 :i.l*

40 Warn* 100.44 10100 1.01 101.41

41 Crawford 100.41 10100 1.01 101.tl

41 Kanarrf 100.4T 11T0Q 0.9* 49.07

41 Jo Oaviaia 100.19 11100 1.1* lift.47

44 tUhland 100.19 14*00 O.ftft • 9.10

100.01 11100 i.ai 1*7.»1

ftU K«(i.

mft-UcnaltttJ co*intT-«»«*a (•*»*•) MM 11*,ft* lit.ft* Mi 101.1*

Vt.ll 101.ft*

t».37

for •»<*• | MM *"*» to^t*^.. 4jU|

taa IIIIJWL* N«p (fLfjwn 11

SI to Vltt

94 Chrlattaa

99 MiMHiw

ft* La*aa

ft* Uay^a

ft* WMt*

9* ftallna

40 Kfinii.a.

41 Maul in*

•1 Clay

•1 0*1latin

*ft WlUiaaaw

•4 C*lhana»

*7 rriahlln

*0 >Uw

11 Clar*

*2 Union

91 fn't't

*4 CvMfcarlaAd

99 Haulun

*4 Craait*

99 Hardin

100 tot*

101 tolaak*.

19*9 iftta 19*1

ralluiJ toa. |fj t-

-*i|ht*4 top cot.

100.00 17100 on 97.91 tft.T* 11*00 o.at ftft. 1ft

Tft.ftT 17 400 0.47 •T.ll ft*.** IBftOO 1.41 1*2.T*

11-10 14900 O.Sft •4.12

11.02 17400 i.oa 1*4.07

tft.ftt 11*00 l.M lftT.O*

tft.ftl 17 100 O.ftft 14 11

ft*.11 1*440 1.79 174.7V

94.ft! 1*444 o.rt TT.4S

It. 71 iicoe l.M lftft.71

11 . 7 0 17404 O.tft •ft. 24 ftft. ftft 17100 O.tft 99.19

It. 3* 17«00 1.40 in. a*

9t.ll llftOO l.*4 1*1.01

9t.l4 14400 0.71 71.07

97. ft* 10400 4.ftft 449.94

97.17 11400 0.70 49.00

I'll llftOO Lift 111.44

9T.2T 10*00 1.01 1M.10

97.09 11700 1.0* 10».Oft

*4.ftl 14100 1.T1 144.1ft **.*! 11400 l.M 10«.11

94.49 74O0 0.40 19.M

ftft. 4* 91100 2.41 lftl.03

44.Oft 11200 0 14 44.17

14.11 TOM 0.M 3T.T]

94.1ft ll*O0 l.M 1*4.11

94.11 4 1100 1.4T 11T.M

tft.94 4S1M 1.11 IM.Tft ftft. ft* 14100 0.01 TT.94

tft.ftl 11*40 1.1ft 111.29

1941 9*3O0 l.M 141.40

ftft. 1ft 4100 0.31 19. 14

79.14 1*400 O.ftft 91.M

IS. 11 14100 07ft 71.1*

ftft. 1ft 7240 0.14 34.44

ftft.44 It 100 1.41 2TT.9T

ftft.04 MM 0.29 24. ft

ftft.ftl .3000 1.11 19*.4*

tft.ftt IftftOO l.tft Iftft.lft

tft.ftl •MO O.ftft 42.24

•ft. 11 17*40 0.M ». r.

•4.11 IftftM 0.91 17.72

*4.4T 19000 O.ftO aa.oa

91. ftft llftOO 1.10 102.24

91.44 10900 0.9S 10.71

91.01 •«oo 0.4» 41.14

91.SI 14000 O.ftO 73.91

90.44 11000 O.Tft 44.17

tft.ftl llftOO O.ft* 11. OB

ta.ii uu 0.17 13.4ft

•4.90 UN 0.11 lft-11

•4.04 ISM 0.41 3ft. 17

79. ft* 111M O.ftT 4*. 39

1990400 100.00

(Tabla 9i

UO.u10«.94

97.21

In footaota ft. Tabla 9. TTia 4CCM data

rapart a enlr ftatnaanra (C0O1241. •ALcfti

La net llftaly io a* rapraaaacatlaa.

20
77

1989 Cost of Living

By County

(Unweighted Average)

90 lo 100

100 to 115

Abov. 115
78

There are however pros and con* cf making adjustments for regional

coat differences, especially where the currant differences in expenditure

par pupil (and loaa of pupil equity) ara aa large aa they ara in Illinois.

Regional coat differences reflect the coata to taachara and administrators

of living in each area, aa well aa othar geographical price differences.

Ona way in which such an index would moat likely be used, ia to convert

the "nominal" expenditure per pupil in each district to "real" terms,

removing the main affecta of regional price differences (that can be done

by dividing expenditure per pupil in each district by the index). For

example, the $13,600 per pupil apent in Winnetka, when converted to real

term* (constant dollars), ia approximately 311,889, whereas the $2,500

apent per pupil in a "poor" district, ainca coata are lower there,

converts to about $2,874 in "real terms." If an adjustment for regional

coat differences were introduced into the school aid formula, in the waya

described above, to adjuat all axpenditurea per pupil to a conatant dollar

baaia, and nothing elae were done, then the effect would be to provide

more state aid to the higher income districts, and less atate aid to the

poorest districts. Since there ia a considerable problem in Illinois with

pupil equity, aa dramatically portrayed by the range from the wealthier

districts spending $13,600 per pupil, aa compared to the poorer districts

that ara spending $2,000, or $2,500 par pupil, thia act alone would juet

increaaa the amount of pupil inequity that now exiata.

Othar compensatory adjustments could be made in the school-aid

formula, auch aa ualng per capita personal income rather than equalised

assessed valuationa (KAV) aa the means of ability-to-pay of the families

in each diatrict, and introducing a much higher atate-financed floor or

foundation level of expenditure per pupil. Then the adverae affecta of

adjuating for regional coat diffarencea on the current level of pupil

inequity would be counterbalanced.

VII. Conclusions

There are large differences of 57 percent in the cost of living among

states and 35 percent between large urban and smaller cities within each

state. The basic pattern of differences between higher coata in Eaatern

Seaboard urban areas, California, Alaska, and Hawaii, and lower coats in

Southern and rural areas tends to persist over time. Thia ia largely

becauee the larger urban areas and bedroom suburbs are typified by higher

reaidential land costs, and higher fuel and othar houeing coata. These

are also related to the higher per capita incomes. There may alao be some

nonmonetary benefits of living in these areas that at least partially

juatify some of the coat differences. But over time recent changea in the

geographical patterns appear to be related to the 1983-89 industrial

recovery affecting the northeast, which will likely be moderated by the

1990-91 recession. Lower oil prices later in the '80's affected the south

in a different way, and the continuing farm recession holds living coata

lower in the midwest farm states. In 1980-85 the industrial atatea were

hurt more severely than the oil producing and western statea. But pricee

appear to have been somewhat inflexible downward there, and these areaa

also re-covered more quickly than the agricultural statea and rural areaa,

where land and housing prices remain somewhat lower.

22
79

Part of the income difference* among areaa--roughly a third•are

purely nominal differences in monetary aalariea, given that there »re

differences in the coat of living. In tha absence of a money illuaion,

employere aa vail aa employees intereetad in maintaining a parity between

services that are purcnaaea or provided in different areas within states

or between states must make some kind of adjustment implicitly for

differences in tha coat of living aa wall aa in nonmonetary amenitiea. A

geographical coat of living index ia one atap toward making such

adjustments soaawhat more explicit.

- 23
80

NOTES

'Population change and the price of houaing may both be endogenoua to

a limited extent. That ia, with respect to population change, peraona may

be attracted to areas where living costa are lower (e.g., plants locating

in Tennessee or the rural south). But the data an migration suggest that

thia effect ia amall in relation to the movements towards the aunbelt and

to outside the suburbs by more affluent and retired people during the 60a

(e.g., to Florida, Arizona, New Mexico, Nevada, and New Hampehire). The

ACCRA regreaaiona in Table 4 however suggest that even thia effect ia

insignificant by 1989. With respect to the median price of houaing, thia

ia to some extent a function of per capita income (see Henderahott and

Thibodeau, 1990). But to treat population change and median house prices

aa endogenoua would require specification of a number of additional

factora affecting population change other than the COL, and affecting

houaing demand other than just Y, going considerably beyond the acope of

thia paper.

2There ia no alternative to thia BLS method for updating coat of

living comparisons over time. These data are routinely uaed in atudiea

of geographic coat of living variations.

3A xerox of the rather large data aet that underliea theae predictiona

ia available from the author on written request encloaing S5 to cover the

coat of proceaaing and mailing. The data for per capita peraonal income

and population change are available for all states. The values of H are

the mean of the large city and smaller metropolitan areas that are sampled

within each state, maintaining conaiatency insofar aa possible throughout

the 1981-90 period (since more areaa appear in the data in the later

years). For the few atatee where there are no values of H in the National

Realtors Association (1990) data, values from the 1980 Houaing Cenaua were

uaed to establish a ratio of housing valuea to thoaa in an adjacent atate

for which there are good data. Assuming this ratio to adjacent areaa

remains unchanged, the valuaa for the missing locations were then

estimated.

"The results shown use the simple unweighted mean, which would be the

index relevant for individuals considering moving. An alternative

normalization was done using a mean computed by weighting the COL index

for each state by its population. Governments or firms allocating funds

in waya that depend on the total population of each area (e.g., school

dietnets) are likely to find the population-weighted mean uaed for

normalization more relevant. The rank order of the COL among statea

however is unaffected, and the percentage difference in the COL among

areaa ia not aignificantly affected by use of the population weighted va.

nonweighted mean. Table 3a containing the normalized COL by aetting the

population weighted mean equal to 100 is available from the author on

request.

-24-
81

References

Alonso, William, and H. Fa jar* (1970), -Coat o( Living by Urban Size,"

In*titut* of Urban and Regional Davalopment, Univereity of

California, Working Paper 128 (Berkeley), 19 pp.

Alonso, William (1970), The Economic* of Urban Size,' Regional Science

Association Paper*• XXVI, pp. 67-83.

American chamber of commerce Raaaarchare Aaan (1990). ACCRA Coat of

Living Index. Fourth Quarter, 1989, Vol. 22, No. 4, Louisville

Chamber of Commerce, Louiaville, Kx.

Ando, Albert, and Franco Hodigliani (1963), "The 'Life Cycle' Hypotheaia

of Saving: Aggregate implication* and Teat*,- American Economic

BasifiH, Vol. LII1 (March), aa reprinted in AEA Reading* in Busings*

Cvclea. pp. 398-426.

Blomquiat, Glenn, Hark Berger, and John Hoehn (1988), -New Estimates of

the Quality of Life in Urban Areas,- American Economic Review

(March), pp. 88-107.

Barro, Stephen M. (1981), -Educational Price Indicea: A State of the Art

Review,- AIU Policy Reaearch, 1701 K Street, N.H., Washington, D.C.

(March), pp. 1-41.

Fournier, Gary M., and David W. Raamuaen (1986), "Salariee in Public

Education! The Impact of Geographical Coat of Living

Differential*,- Public Finance Quarterly. Vol. 14, No. 2 (April

1986), pp. 179-198.

Haworth, C. T., and D. w. Raamuasen (1973), Determinants of Metropolitan

Coat of Living Variation*," Southern Economic Journal. XL (October),

pp. 182-92; J. P. Mattila (1976), "Comment," SEJ. XLI1 (April);

Haworth & Raamuasen (1976), "Reply," ibid.

Hendershott, Patric H. and T. G. Thibodeau (1990), -The Relationships

Between Hedian and Constant Quality House Price*: Implications for

Setting FHA Loan Limit*," AREUEA Journal. Vol. 18, No. 3, pp. 323-

333.

Hickrod, G. Alan et al. (1987), "Documenting a Disaster: Equity and

Adequacy in Illinois School Finance, 1973 through 1988,"

MacArtnur/spencer Series No. 4, Center for the Study of Educetional

Finance, Illinois State University, Normal, Illinois (December), 43

pp.

Israeli, O. (1983), "Effect of Variations in Cost of Living and City Size

on the Rate of Return to Schooling," Quarterly Review of Economics

and Business. Vol. 23, No. 4, Winter 1983.

-25-
82

Israeli, O. (1977), -Differentiale in Nominal wagea and Prices Batntn

CltieB," Orban Studies. Oct. 1977, 14, pp. 175-90.

McMahon, Walter W., and Carroll Melton (1978), "Meaaunna Coat of Living

Variation,- Induatrial Ralatlona. Vol. 17, Mo. 3 (October), pp. 324-

332.

McMahon, H. w. and Carroll Malton (1977), -A Cost of Living Index for

Illinois Countiea and School Districts,* in Parenectlvea on Illlnoia

School Finance. Carol E. Hanea, ad., Illlnoia Office of Education,

Springfield (November 1977), pp. 74-113.

National Aaaociation of Realtors* (1990), -existing Home Sales,- Realtors

Journal, March 1990, July 1987, and earlier issues, Washington, O.c.

Roback, Jennifer (1988), -Wagea, Ratea, and Aasnitiesi Differences Among

Workers and Regions,- Economic Inquiry (January), pp. 23-42.

Simmons, Jaaaa et al. (1973), Florida Cost of Llvlno Research Study.

Tallahaaaee, Fla.i State University of Florida.

Sherwood, Mark (197S), "Family Budgeta and Geographic Differencea in Price

Levela,- Monthly Labor Review. XCVIII (April), pp. 8-15.

D.S. Bureau of Labor Statiatica (1982), "Family Budgets for 1981: Final

Report,' Monthly Labor Review, (also all later issues for the CPI),

U.S. Department of Labor, Waahington, D.C.

U.S. Department of Commerce (1990), 'Projections of the Population of

States by Age, Sex, and Race, 1989-2010,* Current Population

Reports, Series P-25, No. 1035 and earlier isauea. Bureau of the

Census, U.S. Department of Commerce, Waahington, D.C.

U.S. Department of Commerce (1980), Cenaua of Houaino. Bureau of the

Censua, Waahington, D.C.

U.S. Department of Commerce (1990), Survey of Current Buaineaa. and

various earlier issues, Waahington, D.C.

U.S. Department of Commerce (1988), -Current Population Reporta,* Bureau

of the Census, Series P-25, No. 1010, Waahington, D.C.

Ward, Jamea G. (1987), -The Concept of Adequacy in Illinois School

Finance,* MacArthur/Spencer Series No. 5, Center for the Study of

Educational Finance, Illlnoia State University, Normal, Illlnoia, 21

pp.

Ward, Janes G. (1988), -City Schools, Rural Schools," MacArthur/Spencer

Series No. 6, Center for the study of Educational Finance, Illinois

Stats University, Normal, Illinois, 8 pp.
.01 .56 .43 .32

1.00 .02 -.31 -.07

1.00 -.13 .24

1.00 -.11

1.00

Appendix A

•IIT.). correlations Among tha Explanatory Varlablaa

Correlation H«tri« i 1981 Statawide BLS-Baaed Regression:

X AC B s e

Far capita Incoaa X 1.00 -.24 .38 .31 .22

S-Xr. Population change IP 1.00 .22 -.S3 .08

Valua of Bouaing H 1.00 -.47 -.00

climate (1-aortharn) C 1.00 -.27

Population Laval 1.00

Correlation Matrix: 1989 Statawlda ACCHA-Baaad Paoraaaloni (n>34)

X 1.00

a*

H

C

P

Taata for Chanoa in tha Structure

1981 and 1989 Statawlda BLS-Baa«d Raoreaalonai

m (BSS.-gSS,)/* _ (2100-1811)/4 , .,-

ESSm/ (W»»-2>:) 1811/(22*22-6) *'""

* < P..JI <»**>

Tharafora wa cannot rajact tha null hypothaaia {i.a., tha

coafflcionta are not significantly different; thara ia no

evidence of change in tha structure).

1981 MSAe and 1981 Statawlda Regressions!

r > 0.230 Ft_M • 2.626 (95% level)

T < rt JJ, null hypothaaia not rejected (i.a., no aignifleant

diffaranca in tha structure.

1981 BLS-Basad and 1989 ACCRA-Baaed Statewide Regressions

P - 11.32, tha null hypothesis must be rejected. At laaat ona

of tha coafficiants (undoubtedly tha coafflciant for

AP) between tha two aquations is significantly

diffarant.

-27-
84

Appendix B

The 1981 cose of living (or MSAa updated by use of the changes in the

CPI from the base year lead to the following regression results. The

problem in using only MSA data is that the geographical boundaries for

the MSAa change over time, and people who work in the MSAB settle

outside the MSA boundaries. The result is that the change in population

(AP) for the MSA is misleading, as is the per capita income (Y), and the

regressions, although in the earns pattern as those in Table 1, are lee*

meaningful•

(B-l) 1982 COL - .002Y t . 090H - .72AP .417

(1.18) (.89) (-3.37)

(B-2) 1983 COL - -002Y * . 131H - .82&P .SOS

(1.33) (1.09) (-3.38)

(B-3) 19B4 COL - .002X * .23H - 1.22AP .5 40

Another problem is that the per capita income (Y) for the Honolulu

MSA is misleading. At any given time, there are many relatively well to

do American and Japanese tourists occupying expensive beach front

hotels, as well aa villas in Oahu outside Honolulu. The per capita

income of these persons and the value of the hotels undoubtedly affect

the local cost of living, even though their income is not included in

the measures of per capita income for the Honolulu MSA. To at least

partially avoid the distortion that this causes, the per capita income

for San Francisco, which is slightly above the per capita income of

full-time Honolulu residents, was used in all of the BLS-based

regressions to reflect the higher per capita income of tourists from the

continental U.S. and of persons living outside the SHSA who work in

Honolulu and affect the cost of living there. The alternative of

deleting Hawaii from the sample seems less desirable since all the rest

of the data for Hawaii is reasonable and deletion would further reduce

the sample size, raising the standard errors. As shown in Table 4, the

estimate for Hawaii using this BLS-based prediction equation is very

close (-0.84%) to the ACCRA-baaed estimate.

-as-
85

U.S. DEPARTMENT OF EDUCATION

OFFICE OF EDUCATIONAL RESEARCH AND IMPROVEMENT

NATIONAL CENTER FOR EDUCATION STATISTICS

SEP 28 M

Honorable Dale E. Kildee

Chairman, Commillee

on Education and Labor

House of Representatives

Washington, DC 20515-6101

Dear Mr. Chairman:

I would like to thank you and Chairman Sawyer for the opportunity to testify before your

joint committee hearing. I apologize for my tardiness.in responding to your questions. I

have provided answers for most of the questions addressed to me. There were several

questions I did not address because the Census Bureau would be the most direct source

of answers to these questions.

If I can be of any further assistance, please do not hesitate to contact me.

Sincerely,

ft

Emerson J. Elliott

Commissioner

Enclosure
1. "Additional questions for Emerson Elliott*

Q. What steps is the Department of Education taking to obtain school district

boundary information from the counties in California that have neglected to

participate so far in the census mapping project

A. At present, we are taking no action to obtain school district boundary information

for the non-participating California counties. It must be remembered that the

California State Department of Education declined to participate in the School

District Mapping project. Only with the intervention of Dr. Anne Hafner, then an

employee of the South West Regional Educational Research Laboratory, were the

counties afforded the opportunity to map their districts and provide the

boundaries to the Bureau of the Census. Some chose not to avail themselves of

this opportunity. Only through action by the California State Department of

Education or by the non-participating counties themselves could the missing

boundary information be provided.
87

'Sample questions for Census Bureau and NCES*

'Data from 1990 Census for LEA'S'

1.

Q. What is your judgment regarding the appropriateness of using the 1990 Census

LEA data as the primary basis for making Chapter 1 basic or concentration

grants. Are the LEA data sufficiently reliable for this purpose?

A Unless something is done to improve the reliability of the estimates for the

thousands of small districts, the LEA data are not sufficiently reliable to be used

as the primary basis for making Chapter 1 basic or concentration grants.

2.

Q. What special problems have you encountered in attempting to compile the 1990

Census for California LEAs? Are the California data of the same quality as that

for other states except for a few non-participating counties? How might LEA

data now be obtained for any counties that did not earlier prepare LEA maps for

use by the Census Bureau and NCES?

A Other than the problems we had in getting the mapping done, we have had no

special problems in compiling the data for California. The data appear to be of

comparable quality as similar size districts in other States. On the other band, in

the other 49 States, the state education agencies provided preliminary data to the

school districts which then had an opportunity to question and challenge those

data. The State Education Agency in California did not circulate these

preliminary data and there were no challenges from California.

3.

Q. If you feel the LEA data are not sufficiently reliable to be used by the federal

government as the primary basis for making Chapter 1 basic and concentration

grants, what is your basis for this determination? Why are the LEA data less

reliable than county data for making Chapter 1 allocations.

What has been gained from the expenditure of an estimated $6 million of this

compilation if the resulting data are insufficiently reliable to serve as the primary

basis for Chapter 1?

A I believe that the data are not sufficiently reliable because the amount of random

(sampling) error is too large for a large number of districts. Counties, on average,

are much larger (in terms of population) than school districts. Therefore, the

number of counties with large sampling error on the estimate of poor children is

small in comparison with school districts.
Although there is a substantial amount of random error in many of the district

estimates of children in poverty, some 40 or so states do make use of them in

various ways in making the subcounty allocations for Chapter 1 grants. In

addition to their use for fund allocations, the large special tabulation of the census

by school district is the basis for the Congressional!)- mandated report "concerning

the social and economic status of children who reside in the areas served by

different educational agencies". There is also keen interest in this tabulation by

States, LEA's and educational researchers for addressing numerous issues.

4.

Q. Specifically with respect to sub-county allocations of Chapter 1 basic and

concentration grants, which are now made by the states using a variety of

indicators of low income, what would be the advantages and disadvantages of

using 1990 census LEA data on poor children for sub-county allocations compared

to the current use of data such as free and reduced school lunch participation?

A. The advantage of using 1990 Census data for LEA's to do the subcounty

allocations is that you would get rid of various biases which can creep into the

process if you use other data. For example, if the rate of application for the

lunch programs for similar families varies across districts, then the use of the

lunch program numbers will under-allocate money to a district where poor

families tend not to apply for some reason. The cost of getting rid of this bias is

the introduction of more random error. If all or the vast majority of districts in a

State are fairly large, the amount of random error introduced will be small and,

therefore, there is a real advantage to using the census LEA data rather than the

lunch program data. If there are many small districts, using the school lunch data

may result in a more equitable distribution among the various districts. At

present, each State is free to make this choice given the particular situation in

their State.

5.

Q. If the 1990 Census LEA data were sufficiently reliable, would it not be preferable

to use them rather than county data to allocate Chapter 1 basic and especially

concentration grants, so that aid can be more precisely targeted on high poverty

areas?

With respect to concentration grants in particular, would it not be desirable to

direct grants to the small-to-medium size, high poverty LEAs that do not now

receive grants because they are in low poverty counties, or to central city LEAs

that now are combined with their surrounding, more affluent, suburbs when

treated on a county basis.
A. Whether or not the allocation of basic grants would be improved depends upon

the amount of error that is reduced by eliminating the bias compared to the

amount of random error which is introduced. If there were minimal random error,

then it would generally be preferable to use the census estimates. With respect to

concentration grants, the random error associated with sampling will affect the

targeting of small to medium size LEA's if the census estimates are used.

6.

Q. Are there ways in which the 1990 Census LEA data could be used as a partial

basis for improving the target of Chapter 1 grants on high poverty areas without

switching completely to making allocations by LEA from the Federal level? For

example, might grants be calculated at an LEA level, but states be given

discretion over the actual allocation of funds among the smallest LEAs?

A We do not see any practical way to use the 1990 Census LEA data on a partial

basis to improve targeting of funds on high poverty areas. However, we believe

that the Department's reauthorization proposal would address the situation posed

in your question. Under our proposal, the Department would continue to

determine county allocations based primarily on census data. A State would have

the option to distribute Basic and Concentration Grant funds directly to LEAs

without regard to counties using the best available measure of poverty it chooses.

The State educational agency (SEA) would have the flexibility to use a more

current poverty measure such as counts from the Aid for Families with Dependent

Children (AFDC) program or the free/reduced-price lunch program to allocate

the funds it receives under the Federal formula directly to LEAs. The State

would not have to rely on district level census estimates that quickly have become

out-of-date and present questions of reliability for small school districts because of

random variability inherent in the one-in-six sample used by the Bureau of the

Census. The current law requires that an SEA allocating funds directly to LEAs

use the same factors as the Federal formula, which locks it into using census

mapping data. Our proposal would only require that an SEA using this procedure

demonstrate to the Secretary that doing so will result in a reasonable allocation of

funds.
90

7.

Q. Criticism of the potential accuracy of the data compiled from the 1990 Census for

LEAs has focused on the small population size of many LEAs and resulting

likelihood of population sampling errors, since only one out of six households is

surveyed overall with respect to income. While the average size of LEAs is

inevitably smaller than that of counties, since there approximately 5 times as many

LEAs as counties. In 1990, 772 counties-25% of all counties-had fewer than

2,000 school-age children, while 309 counties-almost 10%-had fewer than 1,000

such children. What are the 95% confidence intervals for your pool school-age

child estimates for some of these small counties? Are the Census data for these

counties also "unreliable"?

A (to be answered by the Census Bureau)

8.

O. In your testimony, you mentioned the James-Stein method as a potential way to

reduce the sampling error for small jurisdictions. Would this be effective in rural

areas? Would it be helpful to combine small population areas in some cases?

And how would you choose a threshold in size in which the numbers are

adequately reliable?

A One of the nice properties about the James-Stein (J-S) procedures is that one can

develop them in all areas. They would be most effective for LEA's which have a

large amount of sampling error. Thus the estimates for small districts, be they

urban or rural, would be the most improved. The James-Stein estimate is a

weighted average of two other estimates. One is the census sample estimates.

The other is an estimate based on the overall relationship between the census

sample estimate and an administrative number which is thought to be a good

predictor of what the census is trying to measure. In this case, children receiving

free or reduced price school lunch might be thought to be a good predictor of

"school age children in poverty families." For large districts, the error in the

census sample estimate is small. In such cases, its weight will be large and the J-S

estimate will be very close to the census estimate. On the other hand, The J-S

estimate for very small districts will be dominated by the other estimate because

the census estimate is unreliable and therefore it will have a small weight. Thus

there is no need to choose a threshold or make a judgment of which sample

estimates are "adequately reliable."
91

'Census Data Undue Techniques'

'Questions for any witness*

1.

Q. What would be the most reliable and cost-effective way to update Census data on

children in poor families, whether at the county level or the LEA level, between

now and the next decennial Census, so that we can minimize the likelihood of

large population shifts, and attendant Chapter 1 allocation changes in the future?

A. (should be answered by the Census Bureau)

2.

Q. There is legislation that would require changing county data on poor school age

children by the statewide rate of increase or decrease in all school-age children.

What is your opinion of this proposal? Is its implicit assumption-that local trends

in school-age poor children closely track statewide changes in total school-age

population-valid? Are there states where, during the 1980s, total and poor school

age populations moved in opposite directions?

A In general, using a State rate of change as a proxy for the rate of change in a

given county is a procedure which is used as a last resort It is employed as a

special case or as a fall-back procedure because of a special circumstance. If the

Census Bureau is not able to develop acceptable county level updates, Congress

may want to consider a formula which allocates Chapter 1 money to States rather

than county areas. States would then be responsible for the allocation to

individual school districts based on Congressional Department of Education

guidelines and the best available and local data. The Census Bureau should be

able to produce annually, acceptable estimates in the change in children in

poverty families for States. Most, if not all, States will then work out a solution

for updating the sub-State allocations to reflect changing economic conditions.

3.

Q. What would be the advantages and disadvantages of conducting a mid-decade

census in 1995?

A (should be answered by the Census Bureau)
92

For Witnesses from Census Bureau and NCES

1. What is the current status of the proposal that has been worked on by the Census

Bureau staff to provide updated county-level estimates of children in poor families

biannually beginning in 199S? Does the Department of Education intend to

support and contribute to this project? What are current estimates of its cost, and

the probable reliability of the population estimate it would produce?

The Department of Education is very interested in this project to update the

decennial estimates. We have pledged to provide $100,000 per year for several

years to help get the project moving. The remaining points of the question should i

be answered by the Census Bureau.

2.

Q. Do you keep up-to-date the LEA maps prepared for the purpose of compiling the

1990 Census by LEA?

A The LEA maps that served as a basis for the purpose of compiling the 1990

Census by LEA were paper maps provided by the Bureau of the Census on which

the state education agencies entered the school district boundaries. Those

boundaries have been converted to "TIGER" Line files, a computer-based system

of digitized geography. The computer tapes containing these digitized boundaries

are being provided to each state. Each state may up-date the TIGER line files

to reflect changes in school district boundaries. NCES plans to set up a program

later in the decade to have each State update its boundaries for a special

tabulation of the 2000 Census.
Question Asked bv Congressman Gunderson during the Hearing:

1.

Q. To your knowledge, is there any state that doesn't have some kind of system for

determining on a regular basis the income levels of people by school district?

A. Based on discussion with knowledgeable people in several states as well as the

Council of Chief State School Officers, I found no evidence that states routinely

have systems for determining, on a regular basis, the income level of people by

school district. On the basis of these discussions, I think that:

1) it is unlikely that many state education agencies collect income data

themselves;

2) within some states, education uses district-level income data collected by

another agency in allocating state aid;

3) other states may have the capacity to collect income data through tax rolls

or to provide "poverty" estimates from AFDC rolls.

o