ADJUSTED POVERTY MEASURES

AND THE DISTRIBUTION OF TITLE

I AID: DOES TITLE I REALLY MAKE

THE RICH STATES RICHER?

抽象的
Federal and state governments in the United States
make extensive use of student poverty rates in com-
pensatory aid programs like Title I. 很遗憾, 这
measures of student poverty that drive funding alloca-
tions under such programs are biased because they fail
to reflect geographic differences in the cost of living.
In this study, we construct alternative poverty income
thresholds based on regional differences in the wage
level for low-skilled workers. We then examine the dis-
tribution of Title I revenues after adjusting poverty rates
for geographic differences in the cost of living and ad-
justing Title I revenues for geographic differences in the
purchasing power of school districts. Our findings turn
conventional wisdom on its head. We find that when
we fully adjust for regional differences, Title I funding
patterns disproportionately favor rural school districts
in low cost-of-living states. We conclude with policy rec-
ommendations for revising Title I funding formulas.

Bruce D. 贝克

(corresponding author)

Department of Educational

理论, 政策 &

Administration

Rutgers, The State University

of New Jersey

New Brunswick, 新泽西州 08901

Bruce.baker@gse.rutgers.edu

Lori Taylor

Bush School of Government

and Public Service

Texas A&M University

College Station, TX 77845

lltaylor@tamu.edu

Jesse Levin

Jay Chambers

Charles Blankenship

American Institutes for

研究

San Mateo, CA 94403

JLevin@air.org

JChambers@air.org

CBlankenship@air.org

394

土井:10.1162/EDFP_a_00103
© 2013 Association for Education Finance and Policy

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介绍
Federal and state governments in the United States make extensive use of stu-
dent poverty rates as indicators of educational need and subsequent drivers of
educational funding.1 The federal government uses Census Bureau estimates
of poverty in distributing Title I funds, 例如. And thirty-four states dis-
tribute categorical funds or additional dollars using student weights that are
based on measures of student poverty, such as the share of students eligible
for free or reduced price lunches under the National School Lunch Program
(Verstegen and Jordan 2009).

很遗憾, the measures of student poverty currently used for pol-
icy making (and policy analysis) are geographically biased. Despite well-
recognized differences in the cost of living from one state to another or from
one city to another within a state,2 the income thresholds currently used to
determine the numbers of students living in households that are below the
poverty level are not adjusted in any way for regional differences in the cost
of living. 那是, the current counts of students living in poverty, 哪个是
regularly used to determine how educational and other public dollars are dis-
tributed, do not reflect any adjustment for regional differences in the cost of
living. 例如, 美国. Census Bureau produces the Small Area Income
and Poverty Estimates (SAIPE) for school districts using a set of income thresh-
olds that increase with family size but are the same in rural Alabama as in New
York City.3 Meanwhile, the share of students eligible for free or reduced price
lunches, a proxy for student poverty that is commonly used in both research
and policy, depends on the same set of nationwide income thresholds as are
used to produce the SAIPE for school districts.4

The potential measurement problems arising from a lack of regional cost
of living adjustments in the poverty estimates are obvious.5 Poverty rates are
intended to measure the share of families living at or below a designated

1. Rothstein (2004) provides extensive documentation of the link between student achievement and
贫困, and numerous studies (看, 例如, Gronberg et al. 2004, 2005; Duncombe and Yinger
2005; 贝克 2006, 2009; Gronberg, Jansen, and Taylor 2011) have documented the higher cost of
education for economically disadvantaged (IE。, low-income) 学生.

2. 例如, Renwick (2009) estimates there is a 41 percent differential in the cost of living between
metropolitan areas of California and Iowa, 和 43 和 32 percent differentials in the cost of living
between metropolitan and non-metropolitan areas within California and Iowa, 分别. 这些
cost of living differences produced by Renwick are based on the Regional Price Parities index that
represents geographic differences in the prices of goods for the entire consumer basket of goods
and services.

3. For the official definition of poverty, visit www.census.gov/hhes/www/poverty/poverty.html.
4. Students are also deemed eligible for the school lunch program if they are foster children, runaways,

homeless, migrant, or otherwise categorically eligible (see USDA 2012).

5. This flaw in the way poverty is currently measured has been well recognized (看, 例如, Renwick
2009, 2011; Bartlett 2011; Short 2011; Marks et al. 2010). The issue has even been introduced into
political debate (看, 例如, the testimony of Douglas Besharov before Congress in support of the
Measuring American Poverty Act; Besharov 2007).

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395

ADJUSTED POVERTY MEASURES AND TITLE I AID

standard of living. A family with the poverty threshold level of income ($22,113 for two parents and two children in 2010) would be more able to buy more goods and services—and therefore maintain a higher standard of living—in a low cost-of-living state like Iowa, 然而, than in a high cost-of-living state like California. 因此, the current regime, with its fixed poverty thresholds, tallies relatively too many children as living in poverty in states or regions with relatively low cost of living. The reverse is true in states or regions with relatively higher cost of living. The implications of this measurement error for education policy are large, and frequently overlooked. Most of the discussion about the bias in the poverty numbers naturally focuses on the implications for anti-poverty programs like the Supplemental Nutrition Assistance Program (SNAP), Temporary Assis- tance for Needy Families (TANF), or Medicaid. Key education policies are also affected. The question raised in this study is: What would be the implications for the distribution of federal poverty-based education aid if we regionally ad- justed the current poverty statistics reported by the U.S. Census Bureau to better reflect differences in the real (IE。, cost-adjusted) incidence of student poverty? THE LINK BETWEEN STUDENT POVERTY STATISTICS AND TITLE I Title I is the largest educational grant program in the United States, and is specifically designed as a source of compensatory funding for low-income children. In 2011–12, 美国. Department of Education appropriated $14.5
billion in federal aid through the Title I program (USDOE 2012).

There are four components to the Title I funding formula.6 The first two
成分, Basic grants and Concentration grants, allocate funding to each
school district based on Census Bureau estimates of the number of children
in poverty (IE。, the SAIPE) and the average per-pupil expenditure in the state.7
The higher the average per-pupil expenditure in the state, the larger is the Title
I allocation. Each of these two components allocates a constant dollar amount
per child in poverty within each state, but school districts must have at least
15 percent of the children in poverty to receive a Concentration grant. Basic
and Concentration grants constituted roughly 54 percent of Title I funding in
2012 (USDOE 2012).

Targeted Assistance grants are the third component of the Title I formula.
Targeted Assistance grants provide funding to school districts that increases as
the share of children in poverty increases and as average per-pupil expenditure

Information on the structure of Title 1 funding comes from USDOE (1965).

6.
7. The counts of children in poverty also include children in certain institutions for neglected or
delinquent children and youth or in certain foster homes, and children in families receiving TANF
payments above the poverty income level for a family of four (Riddle 2011).

396

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贝克, 泰勒, 莱文, Chambers, and Blankenship

in the state increases. 因此, districts with a higher share of students in
poverty receive larger Targeted Assistance grants per pupil than districts with
a smaller share of children in poverty. As with the Basic and Concentration
grant programs, the only determinants of school district aid are the state
average expenditure per pupil and the estimated number of students in poverty.
Targeted Assistance grants constituted approximately 23 percent of Title I
funding in 2012.

The remaining 23 percent of Title I funding comes from Education Finance
Incentive grants (EFIG), which depend not only on the number of children
in poverty and the state average per-pupil spending, but also on (1) a measure
of state fiscal effort (the percentage of per-capita income spent on elementary
and secondary education, relative to the national average) 和 (2) a measure
of state school funding equity (a weighted coefficient of variation in district
per-pupil expenditures, wherein children in poverty have a greater weight than
other children.8 States with higher fiscal effort receive larger EFIG allocations
than other states. 同时, states with lower funding equity (IE。, those with
a higher weighted coefficient of variation) receive smaller EFIG allocations
than other states, and the EFIG allocations are distributed more progressively
之内. The equity factor does not take state efforts at compensatory education
into account, so that a state where all of the districts had equal expenditures
per pupil would be deemed more equitable (and receive more Title I funding)
than a state where all of the low-poverty districts had equal expenditures per
pupil, and all of the high-poverty districts had higher expenditures per pupil.
更重要的是, the equity factor does not take regional differences in the
cost of education into account, so states that equalized nominal expenditures
would be deemed equitable, and states that adjusted nominal expenditures to
perfectly equalize the purchasing power of school districts would be deemed
inequitable.

Perceived flaws in the design of the Title I funding formulas have drawn
considerable attention lately (Carey and Roza 2008; 刘 2007, 2008; 磨坊主
2009A; Miller and Brown 2010a, 乙). Critics believe that Title I funding is
wrongly targeted because distribution of EFIG funding tends to favor wealthy
states and larger urban districts.9 In other words, the critics argue that Title
I funding makes rich states richer by allocating disproportionate funding to
states that have greater fiscal capacity, and that Title I funding favors large

8. School districts with fewer than 200 students are not included when calculating the weighted

coefficient of variation.

9. Additional criticisms of Title I funding point to the fact that three of the four formulas used to
allocate dollars do not take into account state fiscal effort and state-minimum provisions guarantee
relatively large allocations to states with small populations (see Miller 2009b).

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ADJUSTED POVERTY MEASURES AND TITLE I AID

districts in urban areas over comparably poor small districts in rural areas (刘
2007, 2008; Miller 2009a; Miller and Brown 2010a, 乙).

In a frequently cited work, 刘 (2008) examined regional disparities in the
Title I funding formulas. His work focused on the relationship between Title
I funding allocations, school district size, and state poverty rates. His analysis
adjusted Title I funding for regional differences in the cost of education using
the National Center for Education Statistics Comparable Wage Index (NCES-
CWI), but did not similarly adjust the poverty rates for regional differences
in the cost of living. He concluded that (1) “By allocating aid to states in
proportion to state per-pupil expenditures, Title I reinforces vast spending
inequalities between states to the detriment of poor children in high-poverty
司法管辖区,“ 和 (2) “small or mid-sized districts that serve half or more of
all poor children in areas of high poverty receive less aid than larger districts
with comparable poverty” (刘 2008, p. 973). Liu’s article and related papers
laid the groundwork for numerous other policy briefs to follow (Miller 2009a,
Miller and Brown 2010a, 乙).

In this study, we extend Liu’s analysis to control not only for regional
differences in the cost of education, but also for regional differences in the
poverty thresholds. In the first part of our analysis, we construct alternative
poverty measures based on regional differences in the prevailing wage for
workers with the typical characteristics of the working poor. In the second part
of our analysis, we use national data on all school districts from 2007–08,
2008–09, and 2009–10 to estimate the average Title I revenues per student
in poverty after adjusting poverty rates for geographic differences in the cost
of living and adjusting Title I revenues for geographic differences in the cost
of education. We evaluate the distribution of cost-adjusted Title I funding per
child in poverty by state and by locale within-state, and find that disparities
do exist. Our analysis, 然而, turns conventional wisdom on its head. 我们
find that when we fully adjust for regional differences, Title I funding patterns
disproportionately favor rural school districts in low cost-of-living states. 我们
conclude with policy recommendations for revising Title I funding formulas.

CONSTRUCTING ALTERNATIVE POVERTY MEASURES
We follow a three-step strategy for constructing alternative poverty measures
for all school districts in the contiguous lower forty-eight states. In the first step,
we estimate the prevailing wage for individuals with the typical characteristics
of the working poor in various labor markets across the United States. We use
those wage levels as our best estimates of the income thresholds for poverty
for each labor market. In the second step, we used those adjusted income
thresholds to recalculate child poverty rates for each labor market area. 这
final step is to use the difference between current and adjusted poverty rates

398

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贝克, 泰勒, 莱文, Chambers, and Blankenship

at the labor market level as a poverty adjustment factor to adjust school district
level poverty rates for all districts located within each labor market. We discuss
each step in turn.

Adjusting the Poverty Thresholds

Our approach to regional poverty adjustment uses hedonic wage analysis to
adjust the poverty income thresholds for regional differences in the cost of
living and the access to desirable local amenities (such as public services, 好的
气候, low crime rates, quality schools, or access to shopping and medical
设施). 本质上, we presume that if the prevailing wage in Chicago for
a worker with poverty-level characteristics is 10 percent above the national
average, then the poverty income threshold in Chicago should also be 10
percent above the national average.

There are three reasons that we use differences in prevailing wage levels as
our measure of regional differences in the cost of living rather than relying on
a market basket approach as in Renwick (2009), Marks et al. (2010), or Short
(2011). 第一的, differences in the prevailing wage reflect not only differences in
the price of food and shelter, but they also reflect any differences in important
community characteristics, such as climate, crime rates, or public amenities
(Roback 1982; Gyourko and Tracy 1989). 像这样, they provide a more com-
plete measure of the income needed to maintain a reasonable standard of
living in each community.

第二, market-basket approaches presume that all families choose the
same bundle of goods and services in all locations. Using differences in the
prevailing wage to measure regional differences in the cost of living allows for
the possibility that families may choose a more modest dwelling in amenity-
rich locations like San Francisco than they would choose in other parts of the
国家.

最后, wage data are available for all parts of the country, making it pos-
sible to develop cost-adjusted poverty thresholds for labor markets throughout
the forty-eight states under analysis.

We estimate the prevailing wage for the working poor using a hedonic
wage analysis modeled after Taylor and Fowler’s 2006 Comparable Wage
Index (CWI). The Taylor-Fowler CWI measures the prevailing wage for col-
lege graduates in 800 我们. 劳动力市场. Our current analysis estimates the
prevailing wage for workers who do not have a college degree. We take this
approach because most of the population living below the poverty threshold
does not have a college degree, and the geographic pattern of wages may be
different for college graduates than for other workers.

Following Taylor and Fowler (2006), we used a maximum likelihood re-
gression and data from the 2008, 2009, 和 2010 American Community

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399

ADJUSTED POVERTY MEASURES AND TITLE I AID

民意调查 (ACS) to generate estimates of the annual wage and salary income of
individuals who have at most an associate’s degree. The dependent variable
was the log of annual wage and salary earnings. The independent variables
were age, age squared, the amount of time worked, and a series of indicator
variables for gender, 种族, educational attainment, occupation-by-year interac-
系统蒸发散, and industry-by-year interactions.10 In addition, the estimation includes
an indicator variable for each labor market area and random effects by state.11
Appendix table A.1 presents coefficient estimates and standard errors from the
hedonic wage model.

As with the Taylor-Fowler CWI, we used the regression estimates to con-
struct a Poverty-CWI.12 The Poverty-CWI captures the differences in wage lev-
els required to compensate workers for differences in the labor market specific
属性, such as the prices of goods, 服务, and other amenities (气候,
crime rates, access to quality schools, medical facilities, parks, 博物馆, ETC。)
associated with different regions within a state. The fact that individual wages
not only contain information on the price levels of goods and services, 但是也
capture the perceived amenities offered in different locations, distinguishes
a comparable wage index from other cost indices based on a basket of goods
(例如, the familiar consumer price index or regional price parity rental prices).
It is our use of labor market analysis that distinguishes this study from previ-
ous attempts (例如, Renwick 2009, 2011) to adjust poverty rates for geographic
differences in the “cost of living.”

有效, the Poverty-CWI provides an estimate of the differences in the
total perceived cost of living in different regions/labor markets as reflected
in labor market outcomes. The index is centered at 1.00 representing the
national average, so that deviations from this figure denote how much more or
less in percentage terms it costs to compensate workers to live and/or work in

10. The analysis also includes the interaction between gender and age to allow for the possibility that

the relationship between age and earnings is different for women than for men.

11. The labor markets used to estimate our hedonic wage model and the NCES-CWI are based on “place-
of-work areas” as defined by the Census Bureau. Census place-of-work areas are geographic regions
designed to contain at least 100,000 人. The place-of-work areas do not cross state boundaries
and generally follow the boundaries of county groups, single counties, or Census-defined places
(Ruggles et al. 2004). Counties in sparsely populated parts of a state are clustered together into
a single Census place-of-work area. Each labor market in the CWI is either a single place-of-work
area or a cluster of the place-of-work areas that constitute a metropolitan area. Whenever possible,
Taylor and Fowler (2006) aggregated place-of-work areas in metropolitan areas to correspond to
Core Based Statistical Areas (CBSAs). Place-of-work areas that straddled more than one CBSA were
treated as separate labor markets. Because of differences between the Census and the ACS, 我们的
analysis includes 778 劳动力市场.

12. 因此, we calculate the least squares mean, or population marginal mean, for each labor market,
and then divide each market-specific predicted wage by the national average predicted wage to yield
the Poverty-CWI. This would be equivalent to identifying the demographic characteristics of the
average person earning the poverty threshold annual income, and then predicting the wage for
such a person in every labor market.

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贝克, 泰勒, 莱文, Chambers, and Blankenship

数字 1. Nationwide Map of the Poverty Comparable Wage Index (CWI)

different labor markets. 例如, a value for a given labor market of 1.25
indicates that it costs approximately 25 percent more than the national average
to hire a comparable worker in this location, whereas a value of 0.75 indicates
that it would cost 25 percent less than the national average to hire a similar
staff person. 数字 1 provides a map of the Poverty-CWI across all counties
in the mainland United States, with darker counties representing higher cost
areas and lighter ones representing lower cost areas.

We next calculated the cost-adjusted poverty thresholds for each labor
market by multiplying the Census poverty thresholds for each family configu-
ration by the corresponding Poverty-CWI value for each labor market. 那里
are different poverty thresholds for different family configurations. To be pre-
cise, there are forty-eight family configurations differentiated by overall size
of family and number of children under the age of eighteen, each with its
own unadjusted poverty threshold set by the Census. Because there are forty-
eight existing family configuration-specific thresholds, there are forty-eight
cost-adjusted poverty thresholds in each of the 778 labor market areas.13

Developing Poverty Adjustment Factors Using Unadjusted and

Cost-Adjusted Poverty Counts

The second step uses the existing unadjusted (Census) poverty thresholds and
the new cost-adjusted poverty thresholds to count the number of school-aged

13. A list of the forty-eight family configurations and the corresponding poverty thresholds for 2009

is provided in appendix table B.1.

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ADJUSTED POVERTY MEASURES AND TITLE I AID

children living in families below these levels in each labor market, 根据
到 2008, 2009, 和 2010 ACS (the same data as were used to estimate
the Poverty-CWI). The total number of children living in poverty within each
labor market can thus be summed to provide the cost-adjusted and unadjusted
poverty counts in each labor market.

The Poverty Adjustment Factor (PAF) is calculated for each labor market
by taking the ratio of the cost-adjusted to unadjusted counts of students living
in families in poverty. A PAF value greater than 1.00 indicates that the current
unadjusted (Census) poverty rate for the given labor market underestimates
the true incidence of poverty, whereas values less than 1.00 show the current
unadjusted poverty measure overstates the true amount of poverty. 考试用-
普莱, a PAF value of 1.15 indicates that the cost-adjusted poverty rate (the true
relative poverty for a given labor market) 是 15 percent higher than the unad-
justed poverty rate, and a PAF of 0.85 indicates that the cost-adjusted poverty
rate is 15 percent lower than the unadjusted poverty rate.

桌子 1 summarizes the average PAFs across labor markets within regions
(Regional Educational Laboratories) established for research purposes by the
Institute of Education Sciences.14 For the analyses herein as elaborated in the
following section, we merge our poverty adjustment factors to school district
level data, where school districts are clustered within labor markets. 最终,
our intent is to discern the distribution of Title I resources to local education
机构, with respect to the estimated poverty rates in those local education
areas and their location. The summaries in table 2 are based on local edu-
cation area enrollment-weighted averages, using district-level enrollment data
over the three-year period from 2008 到 2010.

平均而言, school districts within states belonging to the Northwest Re-
gional Educational Laboratory have a poverty adjustment factor of 1.03. 那
是, the cost-adjusted poverty in districts in these states is, 一般, 三
percent higher than the originally stated poverty. Districts in states in the Mid-
Atlantic and Northeast regions experience the largest average cost adjustments
to their poverty rates (12 和 13 百分, 分别) with the Western states
close behind at 11 百分. Districts in the Southeast, Southwest, and Central
(Plains/Mountain) have downward average cost adjustments to their poverty
rates all on the order of 7 到 8 百分.

The expected pattern also holds for differences across locale within region.
Whereas districts in northeastern metropolitan areas receive an average up-
ward cost adjustment to their poverty estimates of 16 百分, districts in rural
areas in those states receive an average downward adjustment of their poverty

14. A map of the Regional Educational Laboratories regions can be found at http://ies.ed.gov/ncee/

edlabs/regions/.

402

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贝克, 泰勒, 莱文, Chambers, and Blankenship

桌子 1. Regional and Locale Distributions of Poverty Adjustment Factors (PAF)

Region

Appalachiaa

Mean PAF
Standard Deviation

Centralb

Mean PAF
Standard Deviation

Mid-Atlanticc

Mean PAF
Standard Deviation

Midwestd

Mean PAF
Standard Deviation

Northeaste

Mean PAF
Standard Deviation

Northwestf

Mean PAF
Standard Deviation

Southeastg

Mean PAF
Standard Deviation

Southwesth

Mean PAF
Standard Deviation

Westi

Mean PAF
Standard Deviation

Average Across States

Mean PAF
Standard Deviation

Metropolitan

Micropolitan

Rural

Average
Across Locales

1.04
0.19

0.98
0.11

1.14
0.15

1.04
0.10

1.16
0.14

1.07
0.14

0.95
0.08

0.97
0.11

1.12
0.14

1.05
0.15

0.83
0.12

0.79
0.16

0.89
0.14

0.86
0.12

0.94
0.07

0.89
0.13

0.84
0.11

0.75
0.13

0.87
0.11

0.84
0.13

0.81
0.12

0.77
0.17

0.83
0.11

0.82
0.12

0.89
0.13

0.87
0.12

0.81
0.10

0.73
0.13

0.86
0.08

0.80
0.13

0.98
0.20

0.92
0.16

1.12
0.17

1.00
0.13

1.13
0.16

1.03
0.16

0.93
0.10

0.93
0.14

1.11
0.15

1.01
0.17

Notes: Each cell contains school district level enrollment-weighted average PAF across labor mar-
kets. Data from 2007–08, 2008–09, and 2009–10, 我们. Census Bureau Fiscal Survey of Local
政府, Elementary and Secondary School Finances (www.census.gov/govs/school/).
aAppalachia: Kentucky, Tennessee, 弗吉尼亚州, West Virginia.
bCentral: 科罗拉多州, 堪萨斯州, Missouri, Nebraska, North Dakota, South Dakota, Wyoming.
cMid-Atlantic: Delaware, Maryland, 宾夕法尼亚州, New Jersey.
dMidwest: 伊利诺伊州, 印第安纳州, 爱荷华州, Michigan, Minnesota, 俄亥俄州, 威斯康星州.
eNortheast: 康涅狄格州, Maine, 马萨诸塞州, 新罕布什尔, 纽约, Rhode Island, Vermont.
fNorthwest: Idaho, Montana, Oregon, 华盛顿.
gSoutheast: Alabama, Florida, 乔治亚州, Mississippi, North Carolina, South Carolina.
hSouthwest: Arkansas, Louisiana, New Mexico, Oklahoma, 德克萨斯州.
iWest: Arizona, 加利福尼亚州, Nevada, 犹他州.

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403

ADJUSTED POVERTY MEASURES AND TITLE I AID

桌子 2. State Poverty Rankings/Rates for the 48 Contiguous States and the District of Columbia, 和
and Without Adjustment, Sorted from Largest to Smallest Change

状态

District of Columbia

Nevada

纽约

New Jersey

加利福尼亚州

马萨诸塞州

Maryland

伊利诺伊州

康涅狄格州

华盛顿

Rhode Island

弗吉尼亚州

新罕布什尔

Delaware

威斯康星州

科罗拉多州

宾夕法尼亚州

Vermont

Minnesota

Oregon

Arizona

Michigan

俄亥俄州

犹他州

Wyoming

印第安纳州

乔治亚州

Florida

德克萨斯州

North Dakota

North Carolina

Nebraska

爱荷华州

堪萨斯州

Missouri

Tennessee

Unadjusted %
Poverty (SAIPE)

Cost-Adjusted % 改变, Poverty

Poverty

%

Rank

Adjusted
Poverty Rank

28

16

18

12

18

11

10

16

10

14

16

13

9

14

14

14

15

12

11

17

20

18

18

12

11

17

20

19

22

12

20

13

13

14

17

21

32

19

21

14

20

13

12

18

12

15

17

13

9

14

14

14

15

11

11

17

20

17

17

11

10

16

19

18

21

10

18

11

11

12

15

19

5

3

3

3

2

2

2

2

2

1

1

1

0

0

0

0

0

0

0

0

0

0

–1

–1

–1

–1

–1

–1

–1

–1

–2

–2

–2

–2

–2

–2

2

27

18

42

17

45

48

25

47

33

26

39

49

32

35

36

28

43

44

22

14

19

20

41

46

24

12

16

9

40

13

37

38

34

23

11

1

13

5

31

6

35

40

17

38

26

23

34

49

30

32

33

27

42

43

22

7

20

21

44

48

25

9

19

4

47

16

45

46

39

28

11

404

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贝克, 泰勒, 莱文, Chambers, and Blankenship

桌子 2. Continued.

状态

Maine

New Mexico

South Carolina

Montana

Idaho

Alabama

Louisiana

Kentucky

South Dakota

West Virginia

Oklahoma

Mississippi

Arkansas

Unadjusted %
Poverty (SAIPE)

Cost-Adjusted %
Poverty

改变,
%

Poverty
Rank

Adjusted
Poverty Rank

15

24

21

17

15

22

23

22

15

23

20

28

23

12

21

19

15

12

19

19

19

12

19

16

24

18

–2

–2

–2

–3

–3

–3

–3

–3

–3

–4

–4

–4

–5

31

3

10

21

29

8

5

7

30

6

15

1

4

37

3

14

29

36

12

8

10

41

15

24

2

18

笔记: Data from 2007–08, 2008–09, and 2009–10, 我们. Census Bureau Fiscal Survey of Local
政府, Elementary and Secondary School Finances (www.census.gov/govs/school/).

的比率 11 百分. The largest downward adjustments to poverty are found in
rural labor markets in the Central and Southwestern states.

Nationally, the PAFs indicate that measured rates of child poverty are
5 percent too low in metropolitan areas, 和 20 percent too high in rural
社区, 一般. 像这样, the PAFs clearly show that the geographic
bias embedded in the SAIPE or the free and reduced price lunch statistics is
large and economically meaningful.

Adjusting School District Poverty Rates

The final step of this part of the analysis applies the calculated PAF to the
unadjusted poverty rates for each school district in the labor market.15 This
approach assumes that the relative (proportional) adjustment in the counts
and proportions of students living in families below the poverty threshold
are constant across districts within a given labor market. 这个假设
is necessary to apply the adjustments of poverty rates for jurisdictions such
as districts, which are more granular than that at which the CWI and PAF
are calculated (IE。, the labor market). 桌子 2 summarizes the state average

15. As with the CWI, school districts are matched to labor market areas based on the counties in
which the school districts are located, as indicated in the NCES Common Core of Data (看
http://nces.ed.gov/ccd).

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405

ADJUSTED POVERTY MEASURES AND TITLE I AID

poverty and adjusted poverty rates based on enrollment weighted calculations
using district level data. The findings show that in states with high adjustment
factors—states requiring higher nominal income to achieve comparable real
poverty thresholds—average district-level poverty rates are adjusted upward by
2 到 3 百分点. New Jersey, 例如, goes from an average district
poverty rate of 12 percent to an average district poverty rate of 14 百分. 经过
对比, states in regions where lower nominal incomes to achieve comparable
real poverty thresholds experience reductions in estimated poverty of similar
magnitude.

These seemingly subtle overall shifts in poverty lead to significant reshuf-
fling of the rank order of states in terms of poverty. 例如, 纽约
State ranks eighteenth in unadjusted poverty, but it ranks fifth in cost-adjusted
贫困. 加利福尼亚州, which ranks seventeenth in unadjusted poverty, ranks
sixth after the cost adjustments are applied. 的确, all of the states where the
enrollment-weighted average PAF is greater than 1.00 experience an upward
cost adjustment in both their poverty rates and their poverty ranks. Most states
where the average PAF is less than 1.00 experience a downward adjustment
in their poverty ranks. A few states, 然而, such as Georgia and Texas, 前任-
perience a downward cost adjustment to their poverty rates but an upward
adjustment in their poverty ranks.

EVALUATING THE DISTRIBUTION OF TITLE I FUNDING
Previous studies critiquing the distribution of Title I funding across states have
relied on a single year of data (see Carey and Roza 2008; 刘 2008). We rely on
the most recent three-year panel (2008 到 2010) of local public school district
fiscal data from the U.S. Census Bureau’s fiscal survey of local governments
(F-33).16 Using panel data ensures that our analysis is not distorted by one-time
anomalies in the annual data.

Our analysis focuses specifically on the distribution of Federal Compen-
satory Aid to local public school districts. We evaluate the distribution of
(1) Federal Title I Revenue per Enrolled Pupil, (2) Federal Title I Revenue
per Pupil in Poverty, (3) Federal Title I Revenue per Pupil in Cost-Adjusted
Poverty, 和 (4) Cost-Adjusted Federal Title I Revenue per Pupil Adjusted
for Geographic Differences in the Cost of Education (NCES-CWI) using Cost-
Adjusted Poverty. In order to present a relatively simplified summary of Title
I revenues, we evaluate those revenues by state and region. Prior research
suggests that rich Northeastern states, such as New Jersey, 纽约, 或者

16. 很遗憾, data on Title I expenditures were surprisingly inconsistent for districts in some
states for certain years, requiring these states be eliminated from our analyses for the years in
问题. The following states were excluded for the following years: Georgia in 2008 和 2009,
Ohio in 2008 和 2009, Kentucky in all three years, and North Carolina in 2008 和 2010.

406

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贝克, 泰勒, 莱文, Chambers, and Blankenship

马萨诸塞州, make out particularly well in Title I funding per pupil in need,
whereas Southeastern states appear to be at a particular disadvantage.

Within each region, we also evaluate the distribution of Title I funding
by locale, specifically by metropolitan, micropolitan, and rural or other areas
within states and regions.17 Prior research suggests the highest per-pupil al-
locations of Title I aid occur in districts in metropolitan areas, with lower
allocations in micropolitan, and especially rural, districts. 但再一次, these dis-
parities might be moderated after accounting both for the lower of the Title
I dollar in metropolitan areas and for the higher poverty rates after adjusting
poverty-income thresholds.

桌子 3 summarizes the average Title I expenditures per pupil in poverty,
cost-adjusted for regional variation in the value of the Title I dollar per pupil in
unadjusted poverty (as in Liu 2008), and with adjustments to both poverty and
purchasing power. Viewing the unadjusted data on Title I funding per child
in poverty, it would appear that the highest rates of Title I allocation are in
Northeastern metropolitan area school districts (在 $1,867 per child in poverty). 相比之下, the lowest Title I allocations are in Appalachian micropolitan districts and Western state rural districts, at less than $1,220 per pupil. 其他
字, the unadjusted pattern of Title I expenditures is largely consistent with
previous research findings.

When adjusting for differences in purchasing power (as in Liu 2008) 这
Title I funding landscape shifts. We find that the highest Title I allocations
are in micropolitan districts in Central states and rural districts in Central,
Western, and Northwestern states, and the lowest Title I allocations per child
in poverty are in metropolitan areas in Western states.

Adjusting for differences in poverty thresholds but not for differences in
school district purchasing power yields the third set of columns in table 3.
再次, we find that Title I allocations are highest in micropolitan districts in
Central states, and lowest in Western metropolitan areas, 一般.

Taking the final step and adjusting not only the purchasing power but also
the poverty rates, we find that the pattern uncovered by the purchasing power
adjustments is amplified. The highest, by far, Title I allotments per child in
poverty are in micropolitan and rural districts in Central states and the lowest

17. Metropolitan and micropolitan statistical areas (metro and micro areas) are geographic entities
defined by the Office of Management and Budget for use by federal statistical agencies in collecting,
tabulating, and publishing federal statistics. The term “Core Based Statistical Area” is a collective
term for both metro and micro areas. A metro area contains a core urban area of 50,000 或者
more population, and a micro area contains an urban core of at least 10,000 (but less than
50,000) 人口. Each metro or micro area consists of one or more counties and includes the
counties containing the core urban area, as well as any adjacent counties that have a high degree
of social and economic integration (as measured by commuting to work) with the urban core (看
http://www.census.gov/population/metro/).

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407

ADJUSTED POVERTY MEASURES AND TITLE I AID

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H

ADJUSTED POVERTY MEASURES AND TITLE I AID

Title I allotments are in districts in metropolitan areas in Western states. 在
average, Title I allotments in metropolitan areas in the Western states are less
than half of the Title I allotment in rural areas of the Central, Mid-Atlantic,
Northeast, Northwest, or Southwest regions. 平均而言, within each region,
districts in metropolitan areas have significantly lower cost-adjusted Title I
funding per adjusted-poverty child than do micropolitan or rural districts.
These findings run in stark contrast with those popularized in policy reports
intended to influence Title I re-authorization.

桌子 4 drills down to examine Title I allocations in six large, diverse states.
再次, the pattern is clear. Unadjusted, Title I revenues per poverty pupil
appear to favor metropolitan districts over rural ones in five of the six states.
(Texas is the lone exception.) In stark contrast, fully adjusting the allocations
reveals that current Title I policy actually strongly favors rural and micropolitan
districts in all six states.

此外, as table 5 说明, we find no evidence that Title I alloca-
tions are systematically lower for small districts than for large ones. If anything,
our evidence suggests that once we fully adjust the allocations for regional dif-
ferences there is either a negative correlation or no correlation between school
district size and the Title I allocations per pupil.

CONCLUSIONS AND POLICY RECOMMENDATIONS
Our analysis demonstrates that it is feasible to estimate, with publicly available
数据, cost-adjusted poverty measures for all school districts in the nation. 它
also demonstrates that a failure to make adjustments for regional differences
in the cost of living leads to inaccurate measures of the percentage of students
who are really living below the poverty threshold. We show that adjusting
the poverty thresholds to account for differences in the cost of living can
have large effects on our perceptions of relative poverty. Absent adjustment,
the child poverty rates in California and New York are the seventeenth and
eighteenth highest in the nation, 分别, whereas after adjustment they
are sixth and fifth.

Our analysis also casts considerable doubt on conventional wisdom sug-
gesting that Title I over-subsidizes districts in rich states and larger districts
in metro areas. We find that—if anything—Title I fails to adequately support
economically disadvantaged students in metropolitan areas.

像这样, our analysis suggests that the Title I formula components that
have been heavily criticized, in particular the role of state average per-pupil
expenditures in determining allotments, are not leading to demonstrably in-
equitable outcomes of the sort found in prior work. Although it seems il-
logical on its face to provide states with poverty-based funding according to
their own level of spending, there is no doubt that Title I aid should account

410

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411

ADJUSTED POVERTY MEASURES AND TITLE I AID

桌子 5. Correlations Between School District Enrollment and Title I Revenue Distributions in Selected
Large Diverse States

Title I District
Revenue per
Poverty Pupil

CWI Adjusted
Title I District
Revenue per
Poverty Pupil

Title I District
Revenue per
Cost-Adjusted
Poverty Pupil

CWI Adjusted Title I
District Revenue per
Cost-Adjusted
Poverty Pupil

加利福尼亚州

Florida

伊利诺伊州

纽约

宾夕法尼亚州

0.0306

0.1119

0.0599

0.0412

0.1034

−0.0041

−0.0873

0.0290

0.0173

0.0578

0.0025

−0.1290

0.0230

0.0225

0.0322

德克萨斯州

−0.0545

−0.0835

−0.0834

−0.0287

−0.2450

−0.0030

−0.0003

−0.0061

−0.0989

Notes: Data from 2007–08, 2008–09, and 2009–10, 我们. Census Bureau Fiscal Survey of Local
政府, Elementary and Secondary School Finances (www.census.gov/govs/school/).
Regional cost adjustment based on updated Education Comparable Wage Index for 2008 到 2010
(http://bush.tamu.edu/research/faculty/Taylor_CWI/).

for regional differences in the cost of education, and little doubt that cur-
rent poverty measures fail to accurately reflect the geographic distribution of
student need. A fair Title I funding formula that incorporated appropriate
adjustments for geographic differences in the cost of education and in the
poverty thresholds might still appear to favor “richer” states and school dis-
tricts in large urban areas. A strong and positive correlation between fiscal
capacity and Title I aid is not sufficient evidence that the Title I formulas are
flawed.

There is other, more persuasive evidence that the existing Title I formu-
las are flawed. Our analysis demonstrates that the inaccuracy embedded in
existing measures of student poverty is large and economically meaningful.
Because Title I relies on biased measures of student need and fails to adjust
for regional differences in the cost of education, it strongly favors school dis-
tricts in low cost-of-living areas at the expense of school districts in high cost-
of-living areas. Given that low-income minority students disproportionately
attend school districts in high cost-of-living areas, this pattern is particularly
disquieting.

幸运的是, it should be relatively straightforward to resolve the flaws in
the Title I funding formulas. 第一的, the baseline measure of student poverty
should be changed to incorporate geographic differences in the cost of living.
We favor adjusting the poverty thresholds using labor market analysis because
that approach provides a more complete picture of regional differences in the
cost of living. 尽管如此, other strategies such as the market-basket adjust-
ments proposed by Meyer and Sullivan (2012) or Renwick (2009) also have

412

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