Notes |
Rob Grunewald, Economist
Anusha Nath, Research Economist
OCTOBER 11, 2019
A Statewide Crisis:
Minnesota’s Education
Achievement Gaps
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Federal Reserve Bank of Minneapolis
ABSTRACT
While Minnesota’s educational disparities are well-known, this report
shows that these disparities are evident across race, ethnicity, and
socioeconomic status. They are equally deep statewide and between
school types. That is, disparities are not limited to Twin Cities metro
area schools or to traditional public schools. This is a challenge for all
of Minnesota.
This report documents patterns of disparities for three main outcomes—
performance on standardized tests, graduation rates, and indicators
of college readiness. Across these measures, achievement gaps have
persisted for decades despite policies implemented to promote equal
opportunity for education, including school choice, changes in teacher
evaluation systems and compensation, and equalizing per capita funding
across districts.
Still, despite Minnesota’s failing track record on closing education
achievement gaps, there is hope. Based on recent research studies, we
discuss examples in the United States where outcomes for minority
and low-income students have significantly improved.
Rob Grunewald and Anusha Nath
ROB GRUNEWALD is an economist with the Federal Reserve Bank of Minneapolis and its Community
Development department. He is a national leader on research and policy issues related to early childhood
development. Rob holds a B.A. in economics and religion from St. Olaf College and an M.A. in applied
economics from the University of Minnesota.
ANUSHA NATH is a research economist at the Federal Reserve Bank of Minneapolis and its Opportunity
& Inclusive Growth Institute. Her research focuses on development economics, labor market dynamics,
education, and human capital development. Anusha holds B.A. and M.A. degrees in economics from Delhi
University and a Ph.D. in economics from Boston University.
Thanks to Scott Dallman, Isabella Dougherty, and Dasom Ham for their excellent research assistance.
| 3 |
Federal Reserve Bank of Minneapolis
1 | Minnesota’s Challenges
and Opportunities
On average, Minnesota schools do well. The state ranks relatively high on standardized tests,
graduation rates, and college readiness. But hidden beneath these aggregates are huge disparities.
In fact, Minnesota has some of the largest achievement gaps by race, ethnicity, and socioeconomic
status in the nation.
Minnesota’s education achievement gaps have persisted for decades despite implementing
policies designed to close them. Historically, Minnesota has been a leader in adopting policies
that promote equal opportunity for education, especially when it comes to school choice. In 1988,
Minnesota became the first state in the United States to approve mandatory interdistrict open
enrollment. In 1991, it became the first state to approve charter schools. Other reforms include
school desegregation in Minneapolis and St. Paul public schools, changes in teacher evaluation
systems and compensation, and state-level funding equalization across school districts.
In this report, we examine patterns for three main outcomes—performance on standardized tests,
graduation rates, and indicators of college readiness. Standardized test scores and graduation
rates are used to measure outcome gaps for urban and rural school districts, across race, and by
socioeconomic status. College readiness assessments are used to measure outcome gaps across
race and income.
KEY PATTERNS ARE AS FOLLOWS:
• On average, Minnesota performs well compared with all other states on standardized test
scores, graduation rates, and college readiness. However, it has some of the largest gaps in
the nation on these measures by race and socioeconomic status.
• Racial and income gaps in standardized test scores and college readiness have increased
over time, while gaps in graduation rates have decreased.
• Even as graduation rates overall have increased in recent years, college readiness indicators
have declined. This demonstrates that Minnesota is graduating an increasing proportion of
students who are unprepared for college.
• On average, there is no gap between urban and rural school districts on standardized test
scores and graduation rates in recent years. However, there is a large variation in achievement
gaps across schools within rural districts and across schools within urban districts.
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Federal Reserve Bank of Minneapolis
• These gaps are not only racial; low-income white students significantly trail higher-income
white students across Minnesota.
• Variation in outcome gaps across schools also exist within the charter school system and
across schools within traditional public school districts.
• Minnesota has successfully reduced variation in education inputs, such as per capita
expenditures across districts and class sizes across schools. However, achievement gaps
across race and socioeconomic status have persisted for decades.
Despite Minnesota’s failure to close its education achievement gaps, there is hope—other places in
the nation have improved outcomes for minority and low-income students. In 2003, policymakers
in Louisiana took bold steps to make changes to the then failing system in New Orleans, which
led to gains in student achievement. In 2004, a high-poverty community in New York introduced
comprehensive approaches to education that improved outcomes for students. These example
indicate that closing achievement gaps is challenging, but possible.
2 | Background: School Districts and
Demographic Characteristics
Minnesota currently has 2,064 schools across 327 public operating elementary and secondary
independent districts and 164 charter schools. These school districts differ in their demographic
and socioeconomic characteristics.
CREATING DEMOGRAPHIC CHARACTERISTICS AT THE SCHOOL DISTRICT LEVEL. The main source of data
for population characteristics, demographics, and income and earnings is the annual American
Community Survey (ACS) conducted by the U.S. Census Bureau. Each year, more than 3.5 million
households across the country participate in the survey.
School district boundaries change over time. In order to establish patterns for the most recent
school district boundaries, we construct the data by overlaying school district boundary maps on
census tract boundary maps. For each school district, we calculate demographic characteristics
by taking an average across all census tracts that lie within the school district boundary. To map
the census tract boundaries into school district boundaries, we obtained school district boundary
data (shapefiles) from the Minnesota Department of Education (MDE) and the census tract
boundaries from the Census Bureau. For census tracts that overlap across two district boundaries,
we assign the census tract level data into the two districts by using population weights.
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Federal Reserve Bank of Minneapolis
DEFINING URBAN SCHOOL DISTRICTS. For each census tract, we define proportion urban as the number of persons in an urban area divided by the total number of persons in the 2010 census tract.
The percentage urban is proportion urban multiplied by 100. We aggregate this number to the
school district level using frequency weights. A school district is defined as urban if 80 percent of
its population lives in urban census tracts. Figure 1 highlights the urban school district boundaries;
the boundaries in bold depict urban school districts. By this definition, “urban” school districts
include those in the Twin Cities area and a few in Greater Minnesota.
DEMOGRAPHIC CHARACTERISTICS ACROSS SCHOOL DISTRICTS. Figure 2 maps the racial composition
of school districts using data from the ACS. Panels (a)-(c) map the proportion of minority
population, with darker shades indicating a higher proportion of minority population. The
highest proportion of Hispanic population is in the school districts in southern Minnesota, while
the highest proportion of American Indian population is in northern Minnesota. In contrast, the
highest proportion of African American population is in the Twin Cities metro area. Panel (d)
maps the proportion of white population across school districts in Minnesota, with darker shaded
districts depicting higher white population.
Defining urban school districts
Urban Public School District
1
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Federal Reserve Bank of Minneapolis
Panel (a) of Figure 3 depicts the median per capita income across school districts, and panel
(b) maps the percentage of population with access to broadband internet access. The latter is
a measure of infrastructure access and is a proxy for learning resources available to students
outside the classroom. School districts in the Twin Cities metro area have the highest median
per capita incomes, while districts in rural northern Minnesota have among the lowest. Access to
broadband connection is positively correlated to median per capita income—in school districts
where incomes are higher, access to broadband connections is higher.
2
c
a b
d
Racial concentration across school districts
% Population: White
90 or more
80 - 90
70 - 80
60 - 70
50 - 60
Less than 50
Data not available
Urban School District
% Population: Black
Urban School District
% Population: Hispanic
Urban School District
% Population: American Indian
Urban School District
10 or more
8 - 10
6 - 8
4 - 6
2 - 4
Less than 2
Data not available
10 or more
8 - 10
6 - 8
4 - 6
2 - 4
Less than 2
Data not available
10 or more
8 - 10
6 - 8
4 - 6
2 - 4
Less than 2
Data not available
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Federal Reserve Bank of Minneapolis
3 | Outcome Gaps: Where and Who
Student outcomes are measured using three indicators, and each captures a different dimension
of a student’s ability. Test scores are proxies for how well students are learning in classrooms,
while graduation rates are more indicative of schools’ success in providing basic competencies to
their students. Indictors of college readiness capture how well schools prepare their students for
higher learning and careers. This section documents the patterns for these three outcomes. The
focus is on establishing trends, geographical variation, and disparities across racial groups and
incomes in Minnesota.
3.1 | Test Scores
The Minnesota Comprehensive Assessments (MCA) and the Minnesota Test of Academic Skills
(MTAS) are statewide tests that help districts measure student progress toward Minnesota’s academic
standards and also meet federal and state requirements for student assessments. According to the
MDE, most students who take a standardized test take the MCA, while students who receive special
education services and meet eligibility requirements may instead take the MTAS. In addition to
MCA scores, we use data from the National Assessment of Educational Progress (NAEP) to compare
Minnesota with other states. NAEP is a congressionally mandated project administered by the
National Center for Education Statistics (NCES) within the U.S. Department of Education.
Per capita income and infrastructure access across school districts
Median Per Capita Income
Urban School District
% Broadband Internet Access
Urban School District
37,000 - 75,000
34,000 - 37,000
32,000 - 34,000
30,000 - 32,000
29,000 - 30,000
28,500 - 29,000
27,500 - 28,500
27,000 - 27,500
25,000 - 27,000
Less than 25,000
Data not available
69.5 - 85.9
60.5 - 69.5
55.0 - 60.5
48.6 - 55.0
Less than 48.6
Data not available
3
a b
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Federal Reserve Bank of Minneapolis
Our analysis focuses on test scores for Grade 4 reading and Grade 8 math. We choose Grade 4
reading scores because reading proficiency at this stage is a key factor in students’ ability to learn
and achieve in subsequent grades. In terms of math, Grade 8 scores are a better predictor of college
and career readiness than Grade 4 scores. Figure 4 shows the time series for changes in test scores
in Minnesota. Panel (a) shows a sharp decline in MCA test scores after 2012. (This could be due
to a change in the testing system itself denoted by the red lines in panels (a) and (b) and should
not be taken as an indicator of worse performance.) In contrast, the NAEP testing system was
more homogenous during this period. Results from NAEP scores in panels (c) and (d) of Figure
4 show that Minnesota students perform much better than the national average. Although the
national average is catching up to the Minnesota average in reading, math scores in Minnesota
have trended consistently higher.
Minnesota test scores persistently higher
than the national average
420
430
440
450
460
470
Average Grade 4 Reading Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from the Minnesota
Department of Education
Grade 4 Reading MCA Score
830
840
850
860
870
Average Grade 8 Math Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from the Minnesota
Department of Education
Grade 8 Math MCA Score
210
215
220
225
230
Average Grade 4 Reading Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from the
National Center for Education Statistics (NCES)
Grade 4 Reading NAEP Scores
Average Grade 8 Math Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from the
National Center for Education Statistics (NCES)
Grade 8 Math NAEP Scores
Minnesota
U.S. Average
Minnesota
U.S. Average
260
270
280
290
300
4
c
a b
d
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Federal Reserve Bank of Minneapolis
RACIAL DISPARITIES IN MINNESOTA. Figure 5 documents the NAEP scores for whites, African Americans, Hispanics, and American Indians in Minnesota. Panel (a) shows that Grade 4 reading test
scores for whites are about 20 percent higher than those of African Americans and 18 percent
higher than those of Hispanics. These gaps have been persistent since 2002 (earliest available
data). However, the gap between whites and American Indians has increased by about 19 percent
over time. Similar patterns are observed for Grade 8 math scores as shown in panel (b). Panels (c)
and (d) plot average MCA III test scores across schools in Minnesota where schools are classified
by the percentage of minority students. The results show that for both Grade 4 reading and Grade 8
math, the average test scores are significantly lower in schools with higher proportions of minority
students.
In addition to average scores on state standardized tests, another measure of gaps in student
performance is the proportion of students who meet grade level proficiency standards. The
accompanying table shows large gaps on this measure between white and minority students on
the 2018 MCA III tests.1
Grade 4 Reading Grade 8 Math
White students 65% 65%
American Indian/Alaska Native students 31% 25%
Asian students 48% 63%
Black students 31% 29%
Hispanic students 32% 35%
Students eligible for free/reduced-price meals 36% 36%
All students 56% 57%
Proportion of students proficient at grade level
on MCA III tests in 2018
1 While the MDE recently reported achievement test score data for 2018-19, we use 2017-18 as the endpoint to remain consistent with the most recent data available for NAEP scores, high school graduation rates, and college readiness indicators.
| 10 |
Federal Reserve Bank of Minneapolis
Panel (a) of Figure 6 plots the distribution of Grade 4 reading test scores across schools by school
type at each decile of minority population. In both charter and traditional district public schools,
average test scores decrease as the proportion of children from minority groups increases. Overall,
median scores are lower for charter schools than for traditional public schools. Panel (b) shows
similar patterns for Grade 8 math scores.
Large racial disparities in test scores across students and schools
190
200
210
220
230
240
Average Grade 4 Reading Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
White
African American
Hispanic
American Indian
Source: Authors’ calculations based on data from the
National Center for Education Statistics (NCES)
Grade 4 Reading Score by Race
250
260
270
280
290
300
310
Average Grade 8 Math Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from the
National Center for Education Statistics (NCES)
Grade 8 Math Score by Race
430
440
450
460
470
Average Grade 4 Reading Score
10 20 30 40 50 60 70 80 90 100
% Student Population Minority
Source: Authors’ calculations based on MCA III data from
the Minnesota Department of Education
Grade 4 Reading Score by % Minority Students (2018)
830
840
850
860
Average Grade 8 Math Score
10 20 30 40 50 60 70 80 90 100
% Student Population Minority
Source: Authors’ calculations based on MCA III data from
the Minnesota Department of Education
Grade 8 Math Score by % Minority Students (2018)
5
White
African American
Hispanic
American Indian
5
c
a b
d
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Federal Reserve Bank of Minneapolis
Figure 6 also shows that variation among charter schools is larger than variation among traditional
district schools. The length of each box plot denotes the amount of variation across schools within
each decile. Since the boxes are larger for charter schools than traditional district public schools,
this suggests that there is larger variation across charter schools. The data also show that top
performing charter schools with a high percentage of minority students perform better than both
similar district schools and overall state averages.
DISPARITIES ACROSS SOCIOECONOMIC BACKGROUND. Family median income is an ideal measure for
analyzing education outcomes by socioeconomic status. However, most schools do not provide
this information. Eligibility for free or reduced price lunch (FRPL) is often used as a proxy for the
socioeconomic status of families. A student from a household with an income at or below 130
percent of the poverty threshold ($33,475 for a family of four) is eligible for free lunch; a student
from a household with an income between 130 percent and 185 percent of the poverty threshold
($47,638 for a family of four) is eligible for reduced price lunch.
Panel (a) of Figure 7 shows that Grade 4 reading test scores for FRPL-eligible students are
significantly lower than the scores of higher-income students who are not eligible for FRPL. The
gap has been increasing over time, albeit slowly. There is a similar gap for Grade 8 math test
scores shown in panel (b), and it has been constant over time. In terms of proficiency, we can
compare non-FRPL and FRPL students for Grade 3 reading: 68 percent of non-FRPL students met
or exceeded the state reading standards compared with only 38 percent of FRPL students.2
Results
show a similar gap between FRPL and non-FRPL students for Grade 8 math.
Schools with a higher proportion of minority students have
lower test scores across both charter and district public schools
420
430
440
450
460
470
Charter Schools District Public Schools
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
Grade 4 Reading by % Minority Students (2018)
0-10% 10-20%
20-30% 30-40%
40-50% 50-60%
60-70% 70-80%
80-90% 90-100%
830
820
840
850
860
870
Charter Schools District Public Schools
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
Grade 8 Math by % Minority Students (2018)
0-10% 10-20%
20-30% 30-40%
40-50% 50-60%
60-70% 70-80%
80-90% 90-100%
6
a b
2 Non-FRPL student proficiency levels are not available for Grade 4.
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Federal Reserve Bank of Minneapolis
Panels (c) and (d) in Figure 7 plot average test scores across Minnesota schools by the percentage
of students who are eligible for FRPL. For both Grade 4 reading and Grade 8 math, average test
scores are significantly lower in schools with a higher proportion of FRPL-eligible students.
Panel (e) of Figure 7 plots the distribution of Grade 4 reading test scores across schools at each
decile of FRPL-eligible student population. In both charter and traditional public schools, average
student performance decreases as the proportion of FRPL-eligible students increases. A similar
pattern is observed in panel (f) for Grade 8 math.
420
430
440
450
460
470
Charter Schools District Public Schools
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
Grade 4 Reading Score by % FRPL-Eligible Students
(2018)
830
820
840
850
860
870
Charter Schools District Public Schools
Grade 8 Math Score by % FRPL-Eligible Students
(2018)
0-10% 10-20%
20-30% 30-40%
40-50% 50-60%
60-70% 70-80%
80-90% 90-100%
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
0-10% 10-20%
20-30% 30-40%
40-50% 50-60%
60-70% 70-80%
80-90% 90-100%
430
440
450
460
470
Average Grade 4 Reading Score
10 20 30 40 50 60 70 80 90 100
% Student Population FRPL
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
Grade 4 Reading Score by FRPL Eligibility (2018)
830
840
850
860
Average Grade 8 Math Score
10 20 30 40 50 60 70 80 90 100
% Student Population FRPL
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
Grade 8 Math Score by FRPL Eligibility (2018)
Schools with a higher proportion of low-income students have
lower test scores across both charter and district public schools
190
200
210
220
230
240
Average Grade 4 Reading Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
Non-FRPL
FRPL Eligible
Source: Authors’ calculations based on data from
the National Center for Education Statistics (NCES)
Grade 4 Reading Score by FRPL Eligibility
250
260
270
280
290
300
310
Average Grade 8 Math Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from
the National Center for Education Statistics (NCES)
Grade 8 Math Score by FRPL Eligibility
Non-FRPL
FRPL Eligible
7
c
a b
d
e f
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Federal Reserve Bank of Minneapolis
% Free-Reduced Price Lunch
Urban School District
0 - 22
22 - 34
34 - 44
44 - 60
60 - 85
School districts with a high percentage of low-socioeconomic
students are located in both urban and rural areas
8
URBAN-RURAL GAPS IN TEST SCORES. To study Minnesota’s statewide variation in education outcomes, we construct an indicator variable that classifies each school district as either an urban or a
rural school district. As explained in Section 2, this variable is constructed by aggregating population
data from the 2010 census at the census tract level to the school district level. If more than 80 percent
of the population is urban, then that school district is categorized as an urban school district.
Socioeconomic characteristics vary across the state’s school districts within both the urban category
and the rural category. Figure 8 shows that some urban school districts in southern Minnesota have
a majority of students eligible for FRPL. In contrast, in most urban areas to the west of Minneapolis,
fewer than 20 percent of students are eligible for FRPL. Rural school districts in northern Minnesota
have some of the highest proportions of FRPL-eligible students, while rural school districts in
southeastern Minnesota have a relatively lower proportion of FRPL-eligible students. These patterns
are similar to those observed for median household incomes in Figure 3 (a), Section 2.
420
430
440
450
460
470
Charter Schools District Public Schools
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
Grade 4 Reading Score by % FRPL-Eligible Students
(2018)
830
820
840
850
860
870
Charter Schools District Public Schools
Grade 8 Math Score by % FRPL-Eligible Students
(2018)
0-10% 10-20%
20-30% 30-40%
40-50% 50-60%
60-70% 70-80%
80-90% 90-100%
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
0-10% 10-20%
20-30% 30-40%
40-50% 50-60%
60-70% 70-80%
80-90% 90-100%
430
440
450
460
470
Average Grade 4 Reading Score
10 20 30 40 50 60 70 80 90 100
% Student Population FRPL
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
Grade 4 Reading Score by FRPL Eligibility (2018)
830
840
850
860
Average Grade 8 Math Score
10 20 30 40 50 60 70 80 90 100
% Student Population FRPL
Source: Authors’ calculations based on MCA III data
from the Minnesota Department of Education
Grade 8 Math Score by FRPL Eligibility (2018)
Schools with a higher proportion of low-income students have
lower test scores across both charter and district public schools
190
200
210
220
230
240
Average Grade 4 Reading Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
Non-FRPL
FRPL Eligible
Source: Authors’ calculations based on data from
the National Center for Education Statistics (NCES)
Grade 4 Reading Score by FRPL Eligibility
250
260
270
280
290
300
310
Average Grade 8 Math Score
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from
the National Center for Education Statistics (NCES)
Grade 8 Math Score by FRPL Eligibility
Non-FRPL
FRPL Eligible
7
c
a b
d
e f
| 14 |
Federal Reserve Bank of Minneapolis
In contrast to Minnesota’s racial and income gaps in test scores, the state has no urban-rural average
test score gap. Panel (a) of Figure 9 shows that average Grade 4 reading scores are nearly identical
across urban and rural school districts over time. Grade 8 math scores tell a similar story in panel
(b). Moreover, the variation in test scores across districts within the rural category is similar to
variation across districts in the urban category. Figure 10 plots these empirical distributions. The
standard deviation in Grade 4 reading scores across school districts in urban areas is 5.3 and in
rural areas is 4 (panel (a)). Kolmogorov-Smirnov tests show that these distributions are statistically
the same. Similarly for Grade 8 math scores, the distributions across urban and rural areas are
statistically the same with a variance of 6.1 points for urban districts and 5 points for rural districts
(panel (b)).
On average no difference in
test scores across urban and
rural areas since 2006
420
430
440
450
460
470
Average Grade 4 Reading Score
2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from the Minnesota Department of
Education; urban school districts defined as having more than 80% in urban area
Grade 4 Reading Score by Urban and Rural School Districts
830
840
850
860
870
Average Grade 8 Reading Score
2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from the Minnesota Department of
Education; urban school districts defined as having more than 80% in urban area
Grade 8 Math Score by Urban and Rural School Districts
Urban
Rural
Urban
Rural
9
a
b
| 15 |
Federal Reserve Bank of Minneapolis
3.2 | Graduation Rates
Minnesota’s high school graduation rate has gradually increased from 82.5 percent in 2003 to 86
percent in 2017, as measured by the percentage of students who complete high school in four
years, according to data from the MDE (Figure 11).
Variation across districts within rural areas is similar
to the variation with in urban areas
0
.02
.04
.06
.08
.1
% of Districts at Score Level
430 440 450 460 470
Source: Authors’ calculations based on data from the Minnesota
Department of Education; urban school districts defined as having
more than 80% in urban area
Distribution of Grade 4 Reading Scores (2018)
0
.02
.04
.06
.08
% of Districts at Score Level
830 840 850 860 870
Source: Authors’ calculations based on data from the Minnesota
Department of Education; urban school districts defined as having
more than 80% in urban area
Distribution of Grade 8 Math Scores (2018)
Urban
Rural
Urban
Rural
10
a b
Minnesota’s graduation rates
have been gradually increasing
50
60
70
80
90
100
Average Graduation Rate
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from
the Minnesota Department of Education
Graduation Rate
11
| 16 |
Federal Reserve Bank of Minneapolis
RACIAL DISPARITIES IN GRADUATION RATES. In contrast to the persistence of racial gaps in test
scores, gaps in graduation rates have been reduced over time. Panel (a) of Figure 12 shows that
the white-black gap has decreased from 35 percentage points in 2003 to about 14 percentage
points in 2018. There was a similar decline for Hispanic students, but a much smaller decrease
for American Indian students. Despite these decreases, racial gaps are still large. The 2018 crosssectional distribution of graduation rates across schools depicted in panel (b) shows that average
graduation rates are lower in schools with a larger proportion of minority students.
Graduation rate gaps by race have gradually
decreased but remain wide
50
60
70
80
90
100
Average Graduation Rate
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Graduation Rate by Race
0
20
40
60
80
100
Average Graduation Rate
0 20 40 60 80 100
% Student Population Minority
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Graduation Rate by % Minority Students (2018)
White
African American
Hispanic
American Indian
12
a b
| 17 |
Federal Reserve Bank of Minneapolis
DISPARITIES ACROSS STUDENTS WITH DIFFERENT SOCIOECONOMIC BACKGROUNDS. Patterns in graduation rate gaps by FRPL eligibility are similar to those by race. Panel (a) of Figure 13 shows that
the graduation rate for FRPL-eligible students was 72 percent in 2003, about 11 percentage points
lower than average. The difference in 2018 is close to 7 percentage points. Panel (b) shows the
cross-sectional distribution across schools: Average 2018 graduation rates are significantly lower
in schools with a higher proportion of students who are eligible for FRPL.
URBAN-RURAL GAPS IN GRADUATION RATES. As previously defined, if more than 80 percent of its
population is urban, according to the 2010 census, then a school district is characterized as an
urban school district. Many of those urban school districts are in Greater Minnesota.
Graduation rates have been consistently higher in rural school districts compared with urban
districts in Minnesota. Panel (a) of Figure 14 shows that between 2006 and 2018, the graduation
rate increased from 87 percent to 92 percent for rural school districts in Minnesota and from 82
percent to 89.5 percent for urban school districts. However, panel (b) shows that the distribution
of graduation rates across schools in urban districts is similar to the distribution in rural areas—
both overall and for schools that have a majority of students eligible for FRPL. Panel (c) shows that
the distributions for rural and urban areas look similar for each race category.
As we found with test scores, the urban-rural graduation rate gap in Minnesota is small compared
with gaps across racial and income groups.
Graduation rate gaps by socioeconomic status have remained wide
50
60
70
80
90
100
Average Graduation Rate
2002 2004 2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Graduation Rate by FRPL Eligibility
0
20
40
60
80
100
Average Graduation Rate
0 20 40 60 80 100
% Student Population Free-Reduced Price Lunch
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Graduation Rate by FRPL Eligibility (2018)
All Students
Free-Reduced Price Lunch
13
a b
| 18 |
Federal Reserve Bank of Minneapolis
Rural graduation rates higher than urban over time,
but gap has closed in recent years
50
60
70
80
90
100
Average Graduation Rate
2006 2008 2010 2012 2014 2016 2018
Source: Authors’ calculations based on data from
the Minnesota Department of Education; urban school
districts defined as having more than 80% in urban area
Graduation Rate by Urban and Rural School Districts
0
20
40
60
80
100
Rural Urban
Source: Authors’ calculations based on data from
the Minnesota Department of Education; urban school
districts defined as having more than 80% in urban area
Graduation Rate by FRPL Eligibility (2018)
All Students
Free-Reduced Price Lunch
0
20
40
60
80
100
Rural Urban
Source: Authors’ calculations based on data from
the Minnesota Department of Education; urban school
districts defined as having more than 80% in urban area
Graduation Rate by Race (2018)
American Indian
Black
Hispanic
White, non-Hispanic
Urban
Rural
14
a
b c
| 19 |
Federal Reserve Bank of Minneapolis
3.3 | College Readiness
College readiness measures are signals of a student’s ability to successfully complete first-year
math and English courses at postsecondary institutions. We use two measures of college readiness.
The first measure is based on high school assessments. It is calculated as the percentage of students
who score at or above the college- and career-ready (CCR) threshold level on high school assessments
(mainly on SAT or ACT tests). In Figure 15, an ACT composite score of 21 is the minimum threshold
for college readiness. Panels (a) and (b) of Figure 15 show the distribution of students who meet
college readiness benchmarks across states, as measured by ACT exam scores.3 Minnesota has
the highest proportion of students in the nation who meet the college readiness benchmarks for
reading (45 percent) and is among the top three states for college readiness in math (46 percent).
Minnesota ranks high on college readiness assessments,
but has one of the worst gaps by race and ethnicity
26
27
30
30
34
34
35
38
38
40
40
40
41
41
41
42
42
45
0 10 20 30 40 50
% Meeting College Readiness Benchmarks
NV
MS
HI
SC
AL
NC
LA
TN
WY
AR
KY
MO
MT
ND
WI
CO
IL
MN
College Readiness: Reading
20
21
21
21 23 25
25
26
27 29
30
30
31
31
32
32
33
33
33
33 35 37
37
38
38
38
39 41
41 46 55 60
0 20 40 60
% Meeting College Readiness Benchmarks
MS
NV
WA WV
AL
NJ
SC
LA
MD
HI
TN
DE
KY
AR
NC
CA
ID
OR
WY
MO
MI
VT
MT
ND
SD
CT
NH
WI
MN
CO
DC
College Readiness: Math
19
21
26
27
29
30
30
31
31
32
32
32
32
34
35
35
36
38
0 10 20 30 40
Dierence in Percentage of Students Meeting Benchmarks
MT
WY
NV
KY
MS
HI
TN
CO
MO
AL
LA
NC
SC
MN
AR
WI
ND
IL
White-Black Gap in College Readiness: Reading
10 14 17 22
22 24
24
2425
25
25262728
28
2829
29
29
2930
30.5
31
31
31 33 35 3839
39
0 10 20 30 40
Dierence in Percentage of Students Meeting Benchmarks
WV
WA
SD
NJ
VT
KY
MS
WY
AL
NV
OR
LA
NH
HI
SC
TN
DE
IL
MO
MT
CA
ND
AR
CO
MI
NC
MD
CT
MN
WI
White-Black Gap in College Readiness: Math
13
14
15
15
17
18
19
19
19
19
20
22
23
23
23
27
28
31
0 10 20 30
Dierence in Percentage of Students Meeting Benchmarks
LA
MS
KY
MT
ND
MO
AL
SC
TN
WY
AR
NV
HI
NC
WI
MN
IL
CO
White-Hispanic Gap in College Readiness: Reading
3 9 11
11 13 15 16
16
16
16 17
17 18
18
18 19 20
20
20.5
21 22
2223
2324
24
24.5 2728 32 34
0 10 20 30 40
Dierence in Percentage of Students Meeting Benchmarks
WV
MS
LA
WA
KY
AL
MO
SC
SD
TN
VT
WY
AR
MT
OR
NJ
HI
NV
IL
MI
ID
NH
NC
ND
CA
DE
CO
WI
MD
MN
CT
White-Hispanic Gap in College Readiness: Math
15
c
a b
d
e f
IL
| 20 |
Federal Reserve Bank of Minneapolis
Although Minnesota, on average, does relatively well in preparing students for college and career,
there are large disparities in outcomes across racial and income groups. In Figure 15, panels
(c)-(f) show that Minnesota has among the largest college readiness gaps by race and ethnicity.
Figure 16 plots the percentage of students meeting the college readiness threshold. In contrast to
Figure 15, here the threshold is a composite score of 20 and is based on data from ACT Research.
Panel (a) shows that the percentage of students meeting this threshold decreased from 2014 to
2018 across race and ethnicity.
Among whites, the percentage of students meeting the threshold decreased from 81 percent to 69
percent, for Hispanics 49 percent to 26 percent, American Indians 52 percent to 28 percent, and
African Americans 32 percent to 25 percent. The college readiness indicator for Asian students
remained relatively steady.
There are also large gaps in college readiness across income groups, which have substantially
widened from 2014 to 2018 (panel (b) of Figure 16). For students with household income greater
than $100k, 87 percent met the threshold in 2014, dropping to 83 percent in 2018. For students
with household income less than $36,000, 51 percent met the threshold in 2014, dropping to 36
percent in 2018.
Minnesota ranks high on college readiness assessments,
but has one of the worst gaps by race and ethnicity
26
27
30
30
34
34
35
38
38
40
40
40
41
41
41
42
42
45
0 10 20 30 40 50
% Meeting College Readiness Benchmarks
NV
MS
HI
SC
AL
NC
LA
TN
WY
AR
KY
MO
MT
ND
WI
CO
IL
MN
College Readiness: Reading
20
21
21
21 23 25
25
26
27 29
30
30
31
31
32
32
33
33
33
33 35 37
37
38
38
38
39 41
41 46 55 60
0 20 40 60
% Meeting College Readiness Benchmarks
MS
NV
WA WV
AL
NJ
SC
LA
MD
HI
TN
DE
KY
AR
NC
CA
ID
OR
WY
MO
MI
VT
MT
ND
SD
CT
NH
WI
MN
CO
DC
College Readiness: Math
19
21
26
27
29
30
30
31
31
32
32
32
32
34
35
35
36
38
0 10 20 30 40
Dierence in Percentage of Students Meeting Benchmarks
MT
WY
NV
KY
MS
HI
TN
CO
MO
AL
LA
NC
SC
MN
AR
WI
ND
IL
White-Black Gap in College Readiness: Reading
10 14 17 22
22 24
24
2425
25
25262728
28
2829
29
29
2930
30.5
31
31
31 33 35 3839
39
0 10 20 30 40
Dierence in Percentage of Students Meeting Benchmarks
WV
WA
SD
NJ
VT
KY
MS
WY
AL
NV
OR
LA
NH
HI
SC
TN
DE
IL
MO
MT
CA
ND
AR
CO
MI
NC
MD
CT
MN
WI
White-Black Gap in College Readiness: Math
13
14
15
15
17
18
19
19
19
19
20
22
23
23
23
27
28
31
0 10 20 30
Dierence in Percentage of Students Meeting Benchmarks
LA
MS
KY
MT
ND
MO
AL
SC
TN
WY
AR
NV
HI
NC
WI
MN
IL
CO
White-Hispanic Gap in College Readiness: Reading
3 9 11
11 13 15 16
16
16
16 17
17 18
18
18 19 20
20
20.5
21 22
2223
2324
24
24.5 2728 32 34
0 10 20 30 40
Dierence in Percentage of Students Meeting Benchmarks
WV
MS
LA
WA
KY
AL
MO
SC
SD
TN
VT
WY
AR
MT
OR
NJ
HI
NV
IL
MI
ID
NH
NC
ND
CA
DE
CO
WI
MD
MN
CT
White-Hispanic Gap in College Readiness: Math
15
c
a b
d
e f
IL
3 See ACT Research (https://www.act.org/content/act/en/research/services-and-resources/data-and-visualization.html) and
Achieve Inc. (https://eric.ed.gov/?id=ED582094).
| 21 |
Federal Reserve Bank of Minneapolis
The second measure of college readiness shows whether college-enrolled students take remedial or
developmental courses. “Developmental education” refers to programs offered by postsecondary
institutions to prepare students for success in college courses,4 often revisiting content that was
taught in high school. College readiness gaps are also large using this measure.
According to the Minnesota Office of Higher Education, in 2014, 49 percent of African American
college students enrolled in developmental education, while only 19 percent of whites enrolled. The
corresponding figures for Hispanics, Asians, and American Indians were 40 percent, 36 percent,
and 30 percent, respectively. There is also a large gap by socioeconomic status: 36 percent of FRPL
students enrolled in a developmental course compared with 17 percent of non-FRPL students.
The fact that the graduation rates recently have been increasing while college readiness indicators
have declined demonstrates that Minnesota is graduating an increasing proportion of students
who are unprepared for college.
Fewer students prepared for college over time
and gaps across race and income larger
0
20
40
60
80
100
% Meeting College
Readiness Threshold
2014 2015 2016 2017 2018
White
Asian
Hispanic
American Indian
African American
Source: ACT Research
College Readiness by Race
0
20
40
60
80
100
% Meeting College
Readiness Threshold
2014 2015 2016 2017 2018
$100k or more
$60k to $100k
$36k to $60k
Less than $36k
Source: ACT Research
College Readiness by Income
16
a b
4 See the 2014 Getting Prepared report based on data from Minnesota Statewide Longitudinal Education Data Systems
(http://www.ohe.state.mn.us/pdf/GettingPrepared2014.pdf).
| 22 |
Federal Reserve Bank of Minneapolis
4 | Learning from Success Stories
There are lessons to be learned from innovations in other states and cities to improve outcomes
for all students and close achievement gaps.
As stated earlier, when it comes to inputs, Minnesota has done well to provide more equal access
across school districts. Figure 17 shows the distribution of inputs across schools and school
districts. Panels (a) and (b) show per pupil expenditure on regular instruction across school
districts in Minnesota. We use instruction expenditure instead of total expenditure because the
former captures the value of inputs going directly into classroom teaching. Per pupil instruction
expenditure increases as the proportion of children from minority groups increases. Similarly,
per pupil instruction expenditure increases as the proportion of students who qualify for FRPL
increases. In panels (c) and (d), we see that in schools with a higher share of minority or FRPL
students, the student-teacher ratio is slightly smaller. On the one hand, Minnesota has been
successful in ensuring equity in per pupil instruction expenditure and class size. On the other
hand, in panels (e) and (f), we see that schools with a higher proportion of minority or FRPLeligible students have less experienced teachers.
5
0
10
15
20
25
30
0 .2 .4 .6 .8 1
Minnesota has equalized funding and class size by
race and income, but not teacher experience
2,000
4,000
6,000
8,000
10,000
Per Pupil Expenditure
on Regular Instruction
0 .2 .4 .6 .8 1
% Student Population Minority
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Per Pupil Regular Instruction Expenditures (2017)
0
5,000
10,000
15,000
Per Pupil Expenditure
on Regular Instruction
0 .2 .4 .6 .8 1
% Student Population Free-Reduced Price Lunch
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Per Pupil Regular Instruction Expenditures (2017)
5
0
10
15
20
25
30
Student-Teacher Ratio
0 .2 .4 .6 .8 1
% Student Population Minority
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Student-Teacher Ratio Across Schools (2018)
Student-Teacher Ratio
% Student Population Free-Reduced Price Lunch
Source: Author’s calculations based on data
from the Minnesota Department of Education
Student-Teacher Ratio Across Schools (2018)
Years
0
5
10
15
20
25
0 .2 .4 .6 .8 1
% Student Population Minority
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Average Years of Teacher Experience (2018)
Years
% Student Population Free-Reduced Price Lunch
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Average Years of Teacher Experience (2018)
0
5
10
15
20
25
0 .2 .4 .6 .8 1
17
c
a b
d
e f
| 23 |
Federal Reserve Bank of Minneapolis
Despite several reforms and equalizing funding and class sizes, not only has Minnesota failed
to reduce gaps in education outcomes, it has among the worst track records in the nation. Here
we examine where Minnesota ranks among states in closing achievement gaps and identify
states that have shown signs of closing them. Panel (a) of Figure 18 shows the ratio of white-black
students’ Grade 4 reading scores on the NAEP in 2003 (x-axis) and 2017 (y-axis). States below the
red 45-degree line have closed gaps from 2003 to 2017, while gaps widened for states above the
45-degree line during the same time period. For both 2003 and 2017, Minnesota had some of the
widest gaps in the country. Since Minnesota is close to the 45-degree line, gaps have not changed
much over this time period. Similarly, for Grade 8 math scores in panel (b), Minnesota had the
second-largest gap in both 2003 and 2017.5
5
0
10
15
20
25
30
0 .2 .4 .6 .8 1
Minnesota has equalized funding and class size by
race and income, but not teacher experience
2,000
4,000
6,000
8,000
10,000
Per Pupil Expenditure
on Regular Instruction
0 .2 .4 .6 .8 1
% Student Population Minority
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Per Pupil Regular Instruction Expenditures (2017)
0
5,000
10,000
15,000
Per Pupil Expenditure
on Regular Instruction
0 .2 .4 .6 .8 1
% Student Population Free-Reduced Price Lunch
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Per Pupil Regular Instruction Expenditures (2017)
5
0
10
15
20
25
30
Student-Teacher Ratio
0 .2 .4 .6 .8 1
% Student Population Minority
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Student-Teacher Ratio Across Schools (2018)
Student-Teacher Ratio
% Student Population Free-Reduced Price Lunch
Source: Author’s calculations based on data
from the Minnesota Department of Education
Student-Teacher Ratio Across Schools (2018)
Years
0
5
10
15
20
25
0 .2 .4 .6 .8 1
% Student Population Minority
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Average Years of Teacher Experience (2018)
Years
% Student Population Free-Reduced Price Lunch
Source: Authors’ calculations based on data
from the Minnesota Department of Education
Average Years of Teacher Experience (2018)
0
5
10
15
20
25
0 .2 .4 .6 .8 1
17
c
a b
d
e f
| 24 |
Federal Reserve Bank of Minneapolis
Minnesota ranks high in achievement gap
levels and persistence
AL
AK
AZ AR
CA
CO CT
DE
FL GA
IL
IN
IA
KS
KY
LA
MD
MA
MI
MS
MO
NE
NV
NJ NY NC
OH
OK
PA
RI
SC
TN
TX
VA
WA
WV
WI MN
2017 Test Score Gap
1
1.05
1.1
1.15
1.2
1 1.05 1.1 1.15 1.2
2003 Test Score Gap
Source: Authors’ calculations based on data
from the National Center for Education Statistics
Grade 4 Reading Score Gap - White-Black
AL
AK
AZ
AR
CA
CO
CT
DE
GA FL
IL
IN
IA KS
KY
LA
MD
MA
MI
MS
MO
NV NE
NJ NY
NC
OH
OK
PA
RI
SC
TN
TX
VA
WA
WV
MN WI
2017 Test Score Gap
1 1.05 1.1 1.15 1.2
2003 Test Score Gap
Source: Authors’ calculations based on data
from the National Center for Education Statistics
Grade 8 Math Score Gap - White-Black
AK
AZ
AR
CA CO CTDE
DC
FL
GA
IL
IN
IA
KS
KY
MA
MI
MS
NV
NJ
NM
NY NC
OK
OR
PARI
SC
TX
WA
WV
MN
1
1.05
1.1
1.15
1.2
1.25
1.3
2017 Test Score Gap
1 1.05 1.1 1.15 1.2 1.25 1.3
2003 Test Score Gap
Source: Authors’ calculations based on data
from the National Center for Education Statistics
Grade 4 Reading Score Gap - White-Hispanic
1
1.05
1.1
1.15
1.2
AK AL
AZ
AR
CA
CO CT
DE
DC
FL
GA
HI
ID
IL
IN IA KS KY LA
ME
MD
MA
MI
MS MO
MT
NE NV
NH
NJ
NM NY
NC
ND
OH
OK
OR
PA
RI
SC SD
TN
UT TX VT
WA VA
WV
WI
WY
MN
2017 Test Score Gap
1 1.05 1.1 1.15 1.2
2003 Test Score Gap
Source: Authors’ calculations based on data
from the National Center for Education Statistics
Grade 8 Math Score Gap - Non-FRPL-FRPL
1
1.05
1.1
1.15
1.2
AL
AK AZ
AR
CA
CO CT
DE
FL
GA
HI
ID
IL
IN
IA
KY KS
LA
ME MD
MA
MI
MS
MO
MT
NE
NV
NH
NJ
NM
NY NC
ND
OH
OK
OR RI PA
SC
SD
TN TX VT UT VA
WA
WV
WI
WY
MN
2017 Test Score Gap
2003 Test Score Gap
Source: Authors’ calculations based on data
from the National Center for Education Statistics
Grade 4 Reading Score Gap - Non-FRPL-FRPL
1
1 1.05 1.1 1.15 1.2
1.05
1.1
1.15
1.2
AK
AZ
AR
CA CO
CT
DE
FL
GA
HI
ID
IL
IN
IA
KS
KY
MA
MI
MS
NV
NH
NM
NY NC
ND
OK
OR
PA
RI
SC
UT
VT
WA
WV
WY
MN
2017 Test Score Gap
2003 Test Score Gap
Source: Authors’ calculations based on data
from the National Center for Education Statistics
Grade 8 Math Score Gap - White-Hispanic
1 1.05 1.1 1.15 1.2
1.05
1.1
1.15
1.2
1
18
c
a b
d
e f
| 25 |
Federal Reserve Bank of Minneapolis
Meanwhile, Indiana stands out from other states in closing white-black achievement gaps for
both Grade 4 reading and Grade 8 math between 2003 and 2017 and had some of the smallest
gaps in 2017. In addition, Oklahoma made progress in closing white-black achievement gaps and
had relatively small gaps in 2017, while West Virginia posted relatively small gaps in both 2003
and 2017.
All states showed signs of closing the white-Hispanic achievement gap for Grade 4 reading scores
between 2003 and 2017 (Figure 18, panel (c)), while a number of states made some progress in
closing the white-Hispanic achievement gap for Grade 8 math scores (panel (d)). However, in
Minnesota, the white-Hispanic achievement gap increased for Grade 8 math scores and the state
had among the highest achievement gaps in 2017. Florida, Oklahoma, and Michigan were among
states that showed signs of reducing white-Hispanic achievement gaps and also had relatively
small gaps in 2017.
Across most states, the achievement gap between students who qualify for FRPL and students
who don’t qualify has remained relatively steady from 2013 to 2017, including in Minnesota
(panels (e) and (f)). In 2017, Minnesota was among the states with the largest achievement gaps,
while Wyoming, West Virginia, and Delaware had some of the smallest achievement gaps on this
measure in both 2003 and 2017.
Figure 18 shows that throughout the country, many states struggle with persistent education
achievement gaps based on race, ethnicity, and socioeconomic status. At the same time, the
data indicate some states have shown signs of closing these gaps, even though no state has fully
closed them.
In the rest of this section, we review initiatives at the state, school district, and school levels that
serve as examples of success in boosting outcomes for children from minority groups or lowincome families. We are not intending to endorse specific solutions, but rather to highlight that
achievement gaps are not a given. They can be reduced or closed.
5 State NEAP scores are based on a sample of schools and students. Therefore, state-level values are estimates. See the NAEP website for information on standard errors (https://nces.ed.gov/nationsreportcard/). Figure 18 does not include standard error estimates.
| 26 |
Federal Reserve Bank of Minneapolis
4.1 | Taking Bold Steps: New Orleans
In October 2003, Louisiana passed a state constitutional amendment that led to the establishment
of the Recovery School District (RSD), which allows the state to take over failing schools, as
determined by test scores and other performance measures. In the first year after the amendment,
17 schools statewide were deemed eligible for takeover; 16 of these were in New Orleans. At the end
of the 2004-05 school year, more than 63 percent of the public schools in New Orleans had been
deemed likely eligible for takeover in subsequent years. In August 2005, the destruction caused
by Hurricane Katrina created the context to place the majority of public schools in New Orleans
under the administration of the RSD. A special session of the Louisiana legislature redefined
performance thresholds by which schools and districts were identified as failing. As a result, 114
low-performing Orleans Parish School Board (OPSB) schools were placed in the state-run RSD,
which was charged with operating the schools for an initial period of five years.
The OPSB retained control of only 17 of the schools (out of 131) it operated before Katrina. The
RSD takeovers resulted in each of the existing public schools, including its facilities and staff,
coming under charter management. Importantly, these takeovers guaranteed seats for incumbent
students, “grandfathering” them into the new school.
Abdulkadiroglu et al. (2016) evaluate the causal effects of the RSD on students’ achievement using
an instrumental variables strategy that exploits the grandfathering provisions used initially to
fill takeover seats. They conclude that the school takeovers in the RSD appear to have generated
substantial achievement gains for a highly disadvantaged student population. The takeover effects
were larger in Grade 7 and Grade 8 compared with earlier grades and were larger in the first two
years of the takeover than in later years
Harris and Larsen (2016) also found significant results. They compare outcomes before and after
Hurricane Katrina and reforms in New Orleans with data from a matched comparison group
that experienced hurricane damage but not the school reforms. The study finds a large positive
cumulative effect over time on achievement, where achievement is measured with a scale score
that incorporates English language arts, math, science, and social studies.
4.2 | Involve and Improve: New York and Boston
Harlem Children’s Zone (HCZ) is a 97-block area in Harlem that offers a number of “community”
and “school” programs. Community programs are available to anyone living near HCZ, while
school-related services are provided to the students who attend the Promise Academy charter
schools. The Promise Academy schools began in the fall of 2004 with the opening of the
Promise Academy elementary and middle schools. In 2005, the Promise Academy II elementary
school opened.
The Promise Academy has an extended school day and year, with after-school tutoring and
additional classes on Saturdays for children who need remediation in math and English skills. It
| 27 |
Federal Reserve Bank of Minneapolis
emphasizes the recruitment and retention of high-quality teachers and uses a test-score valueadded measure to offer incentives to and evaluate current teachers. Teachers are evaluated annually
and are provided support so that their time is spent teaching and not doing administrative tasks.
The Promise Academy is similar to other No Excuses charter schools6 with three exceptions: (1)
the Promise Academy does not require parents or students to sign a behavioral contract, (2) the
Promise Academy enrolls students at a younger age (3 years old), and (3) a wide range of additional
services are available to HCZ students that are not available in other charter schools, including
free medical, dental, and mental health services; student incentives for achievement; meals; and
support for parents in the form of food baskets, meals, bus services, and the like.
Dobbie and Fryer (2011) show that students who enroll in the middle school gain about 0.2
standard deviations in math per year. Students in the Promise Academy elementary school gain
approximately 0.2 standard deviations in both math and English language arts per year.
Dobbie and Fryer (2013) show on the one hand that traditionally collected input measures—
class size, per pupil expenditure, teacher certification, and teacher training—are not correlated
with school effectiveness. On the other hand, an index of five policies suggested by qualitative
research—frequent teacher feedback, the use of data to guide instruction, high-dosage tutoring,
increased instructional time, and high expectations—explains approximately 45 percent of the
variation in school effectiveness.
Six years after being selected through a lottery to enroll, Promise Academy middle school students
scored 0.28 standard deviations higher on academic achievement outcomes and 0.31 standard
deviations higher on a measure of on-time benchmarks.7 Moreover, females are 10.1 percentage
points less likely to be pregnant as teenagers, and males are 4.4 percentage points less likely to be
incarcerated.
While the HCZ program focuses on both school and community interventions, Boston College’s
City Connects program focuses on providing comprehensive support services that assess
individual elementary school students’ academic, social/emotional, family, and health needs, and
connects them to relevant community-based services. The program assists schools by connecting
them with community agencies and service providers, and streamlining student referral and case
management.
A recent report suggests that children who attended City Connects through Grade 5 closed half
of the achievement gap in English and two-thirds of the achievement gap in math by Grade 8
relative to the Massachusetts state average. After controlling for school and student characteristics
6 The No Excuses school model focuses on reading and math achievement, enforces high behavioral expectations through a formal discipline system, and increases instruction time relative to traditional public schools. Teachers receive more feedback about their teaching compared with peers in
traditional schools and regularly use data from student assessments to modify instruction. Moreover, school days and school years are typically longer
than those in traditional public schools (Dobbie and Fryer 2013).
7 The on-time benchmarks index constructed by the authors consists of two variables: whether a student graduated from high school in four years
and whether he or she enrolled in college immediately after graduation.
| 28 |
Federal Reserve Bank of Minneapolis
and pre-existing academic achievement differences, students who attended a City Connects
elementary school outperformed peers on Grade 6 to Grade 8 achievement tests, with effect sizes
ranging from 0.29 to 0.67.8 In addition, children who attended a City Connects school had lower
high school dropout rates compared with children who did not attend a City Connects school,
adjusting for child and family background characteristics.
4.3 | Lessons from High-Performing Disadvantaged School Districts
A recent report by the Program Evaluation Division of the North Carolina General Assembly9
uses a nationwide database to identify high-performing school districts that predominately serve
disadvantaged students. Across more than 11,000 school districts with complete achievement
and socioeconomic data in the Stanford Education Data Archive, the report identifies 18 percent
as districts serving predominately disadvantaged students based on districts that are in the top
quartile of FRPL eligibility and in the bottom quartile of a composite measure of socioeconomic
status. Of these almost 2,000 schools, only 94 performed at grade level or better over a seven-year
period (2009-15) between Grade 3 and Grade 8 on math and English language arts achievement
tests.
These findings are further evidence that schools with disadvantaged students struggle to attain
high performance. The report also looks closely at the high-performing disadvantaged districts
to learn what characteristics they share. First, on average high-performing disadvantaged
districts outperform other disadvantaged districts by Grade 3. After Grade 3, the high-performing
districts maintain their advantage with similar growth rates in improvement as lower-performing
disadvantaged districts.
Second, the authors conducted case studies of 12 of the high-performing disadvantaged districts to
learn more about their common features. Consistent with relatively strong Grade 3 achievement,
all of the districts prioritized providing early education. The high-performing districts also focused
on increasing or maximizing student learning time; attracting, developing, and retaining highquality teachers; using data and coaching to improve instruction; seeking additional outside
resources, and promoting a local school board focus on policy and academic achievement.
8 See The Impact of City Connects: Student Outcomes, Progress Report 2016 (https://www.bc.edu/content/dam/files/schools/lsoe/cityconnects/pdf/
City%20Connects%20Progress%20Report%202016.pdf).
9 See North Carolina Should Focus on Early Childhood Learning in Order to Raise Achievement in Predominantly Disadvantaged School Districts, Final
Report to the Joint Legislative Program Evaluation Oversight Committee, May 2019 (https://www.ncleg.net/PED/Reports/documents/Disadvantaged_
Schools/DisadvantagedSchools_Report.pdf ).
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4.4 | Common Themes
A few common themes emerge across these successful school districts and schools. First, schools
are given greater autonomy. In New Orleans, the schools under the OPSB were replaced with
independent schools that were directly accountable to the state’s RSD. In New York, the Promise
Academy was given autonomy in implementing its own community and school programs. The
report on high-achieving disadvantaged districts finds that school principals were given autonomy
to lead, which helped attract, develop, and retain high-quality teachers.
Second, there is a focus on school quality. Research on the Promise Academy demonstrated that
flexibility in teacher recruitment and retention combined with improvements in pedagogical
methods led to better outcomes. A common theme in the high-performing disadvantaged districts
study is a focus on school quality, including maximizing student learning time and using data and
coaching to improve instruction.
Third, support services for students and their families correlate with enhanced education
outcomes. Students in the Boston Connects program receive individualized services that are
associated with gains in achievement test scores and reductions in dropout rates. Meanwhile,
providing a variety of student and family supports is a key strategy to advancing student outcomes
in the Harlem Children’s Zone.
These examples indicate that closing achievement gaps is challenging, but possible.
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5 | Conclusion
This report highlights the extent of education achievement gaps in Minnesota. Cross-sectional
and time-series patterns are examined for three main outcomes—performance on standardized
test scores, graduation rates, and indicators of college readiness. The focus is on documenting
disparities across racial groups, students of different socioeconomic backgrounds, types of schools,
and urban and rural school districts. However, this report does not identify the underlying causes
of these achievement gaps across demographic and socioeconomic groups.
AGAIN, THE FOLLOWING PATTERNS ARE HIGHLIGHTED.
• On average, Minnesota performs well compared with all other states on standardized test
scores, graduation rates, and college readiness. However, it has some of the largest gaps in
the nation on these measures by race and socioeconomic status.
• Racial and income gaps in standardized test scores and college readiness have increased
over time, while gaps in graduation rates have decreased.
• Even as graduation rates overall have increased in recent years, college readiness indicators
have declined. This demonstrates that Minnesota is graduating an increasing proportion of
students who are unprepared for college.
• On average, there is no gap between urban and rural school districts on standardized test
scores and graduation rates in recent years. However, there is a large variation achievement
gaps across schools within rural districts and across schools within urban districts.
• These gaps are not only racial; low-income white students significantly trail higher-income
white students across Minnesota.
• Variation in outcome gaps across schools also exist within the charter school system and
across schools within traditional public school districts.
• Minnesota has successfully reduced variation in education inputs, such as per capita
expenditures across districts and class sizes across schools. However, achievement gaps
across race and socioeconomic status have persisted for decades.
In addition to these patterns, this report provides examples of success within K-12 schools for
improving outcomes for minority and low-income students. The main takeaway from these
examples is that achievement gaps are not a given. They can be reduced or closed.
Policymakers and practitioners can use the analysis in this report to motivate discussion about
how to address these persistent achievement gaps. Minnesota has failed to close achievement
gaps for decades, but there is hope that the state can break this trend and provide an education
that works for all Minnesota students.
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References
Abdulkadiroglu, Atila, Joshua D. Angrist, Peter D. Hull, and Parag A. Pathak. 2016. “Charters
without lotteries: Testing takeovers in New Orleans and Boston.” American Economic Review 106
(7): 1878-1920.
Dobbie, Will, and Roland G. Fryer Jr. 2011. “Are high-quality schools enough to increase
achievement among the poor? Evidence from the Harlem Children’s Zone.” American Economic
Journal: Applied Economics 3 (3): 158-87.
Dobbie, Will, and Roland G. Fryer Jr. 2013. “Getting beneath the veil of effective schools: Evidence
from New York City.” American Economic Journal: Applied Economics 5 (4): 28-60.
Harris, Douglas N., and Matthew F. Larsen. 2016. “The effects of the New Orleans post-Katrina
school reforms on student academic outcomes.” Technical Report. Education Research Alliance
for New Orleans.
Vaughan, Debra, Laura Mogg, Jill Zimmerman, and Tara O'Neill. 2011. Transforming Public
Education in New Orleans: The Recovery School District. Cowen Institute.
90 HENNEPIN AVE, MINNEAPOLIS, MN 55401 |