1. education

Urban-01-p.qxd
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
9/6/11
12:07
Page 67
6
Economics of Urban Education
Andrew McEachin and Dominic J. Brewer
Over the past 30 years, in the mass media and stakeholder and research communities, there been
increased interest in urban education. A controversial report, A Nation At Risk (National Commission
on Excellence in Education, 1983), fueled the current discussion about the underperformance of
American schools, especially in urban areas. The attention of countless policies, billions of dollars, and
innumerable person hours have been spent trying to fix urban schools. Since the Industrial Revolution,
there has been a population boom in America’s urban cities. The majority of individuals now live
in urban areas, and these areas have unique economic environments—environments that have
adverse effects on education. Considering that economic growth and prosperity are tied to the quality
of education within a country (Hanushek & Kimko, 2000), special attention must be given to the
unique economic environments that surround students, families, and other education stakeholders
in urban areas.
Education researchers are particularly concerned with the lower academic achievement of urban
students; but this discussion cannot occur without a finer understanding of the economic and social
conditions of these communities. The salient characteristics of urban centers include population
diversity and poverty, which also relate to challenges in employment, housing, and educational
attainment. A significant portion of education research suggests that family and student characteristics—e.g., ethnicity, language status, socioeconomic status, and housing stability—are significantly related to student achievement (Hanushek, 2003; Rothstein, 2004). The relationship between
these variables and education outcomes are typical questions in research on the economics of
education.
The agenda of this chapter is not to provide a primer on the theoretical or mathematical underpinnings of urban economics, nor do we exhaustively cover the research on the economics of education. Instead, we provide an overview of the economics of urban areas and its relevance to
education at a descriptive and conceptual level. First, we present the characteristics of urban centers
compared with suburban and rural areas. We offer descriptive data that describe the unique features
of urban communities, including population characteristics, economic inequality, labor market,
economy, housing, health, and educational attainment. Throughout the discussion we offer comparative data of these indicators across major metropolitan areas. Second, we present an overview of
research in urban education. Numerous studies explore characteristics such as race, socioeconomic
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 68
68 • Foundation
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
status, teacher labor markets, and housing as related to student achievement. Finally, we explore the
interrelationships between the economy of urban areas and education opportunities.
The Economics of Urban Areas in the United States
Prior to the Industrial Revolution, towns clustered around trading routes, typically near waterways.
Transportation was both expensive and resource-intensive. As new technologies increased the
efficiency of transportation (e.g., trains and motorboats) and production (the cotton gin, machines,
and internal combustion engines), cities and firms specialized in a few goods and created comparative advantages (O’Sullivan, 2007). From 1900 to 1990, the geographic destination of choice
drastically shifted. The total urban population more than tripled, increasing from 54 million to 184
million, while the rural population increased only 33%, from 45 million to 61 million. The diverse
economic, social, cultural, and ethnic nature of urban areas makes them both dynamic and
mandatory areas of study for education researchers, specifically in population diversity, poverty, and
education attainment.
Defining an urban area is a difficult and often arbitrary matter. The stereotypical metropolitan
areas such as New York City, Los Angeles, Detroit, and Chicago are clear examples of urban cities.
However, the term encompasses a wide array of communities. According to O’Sullivan (2007), the
U.S. Census Bureau designates urban areas as places with high population density. The Census
Bureau makes the distinction between an urbanized area and urban clusters. Urbanized areas are
census block groups with a density of 1,000 people per square mile. These communities may also
encompass surrounding areas of less density, but the total population for an urban area is typically
more than 50,000 residents. The census also designates urban clusters as smaller communities
between 2,500 and 50,000 people. Census areas not designated as urbanized or as an urban cluster
are designated as being rural.1 To complicate matters still more, the U.S. Census Bureau does not
utilize suburban as a geographic classification, although the educational research community often
writes about the urban, rural, and suburban triad.
Using these distinctions, 79% of the U.S. population were living in urban areas and 20% in rural
areas in the year 2000 (O’Sullivan, 2007). That such a significant proportion of the country’s
population is designated as “urban” necessitates particular attention to the challenges faced in these
communities. It also implies that the term urban may be too general for rigorous research and
conversation, and that deeper, more meaningful labels are needed. Using U.S. Census designations,
urban areas are further broken down into five subgroups. Table 6.1 shows the percent of the population in rural and urban areas and the percent of people living in the four urban subdesignations.
Nearly 70% of the population lived in urban areas with at least 50,000 people, implying that the
majority of the population lives in densely populated urban pockets. Even though one urban area
can have vastly different characteristics than another, urban areas share enough common characteristics (e.g., higher poverty rates, diverse populations, and health and housing issues) to warrant
special attention from educational researchers.
Population Diversity
Urban centers are areas of densely packed racial and economically diverse populations. Descriptive
data from eight well-known cities—Los Angeles, New York City, Atlanta, Detroit, Milwaukee, Dallas,
Chicago, and Philadelphia—highlight this population diversity. Table 6.2 displays the racial
demographics for the eight major urban cities. These cities characteristically have overrepresented
minority populations. These populations differ significantly from the national average; Detroit is at
the extreme, with 90% of its inhabitants belonging to a non-White racial category. This population
diversity adds one layer to the complex nature of large urban areas.
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 69
Economics of Urban Education • 69
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Table 6.1 Percent of U.S. Population Living in Urban vs. Rural Areas in 2000
Total population
Percent of U.S. total %
U.S.
Urban
Rural
285,230,516
225,956,060
59,274,456
100.00
79.22
20.78
Urbanized area >200,000 population
Urbanized area 50,000–199,999
Urban clusters 5,000–49,999
Urban clusters 2,500–4,999
166,215,889
29,584,626
25,438,275
4,717,270
58.27
10.37
8.90
1.65
Source: U.S. Department of Transportation, 2011
Table 6.2 Population Demographics in Geographic Boundaries of Urban Districts (2006)
Population
Percent
Minority
Non-White
%
Percent
White %
Percent
African
American
%
Percent of
American
Indian or
Native
Alaskan %
Percent
Asian %
Percent
Native
Hawaiian
or Pacific
Islander %
Percent
Hispanic
or Latino %
4,554,163
8,214,426
442,887
834,116
563,173
997,737
2,749,283
113,127
299,398,484
39.1
15.7
53.62
56.12
62.76
90.00
56.38
46.09
63.45
35.91
33.62
60.9
84.3
46.38
43.88
37.24
10.00
43.62
53.91
36.55
64.09
66.38
14.20
6.2
10.02
25.10
55.66
83.08
39.60
22.89
35.29
8.34
12.80
0.6
1.4
0.54
0.28
0.15
0.29
0.64
0.44
0.19
0.21
0.97
5.3
1.2
9.78
11.73
2.32
1.11
3.39
1.66
4.90
2.66
4.40
0.20
0.10
0.20
0.03
0.05
0.00
0.08
0.05
0.05
0.00
0.17
18.8
6.8
47.67
27.61
5.89
6.19
14.94
52.18
28.15
34.12
14.8
Source: State Education Data Center (2009)
Poverty and Economic Opportunity
Urban areas often see higher rates of poverty compared to suburban communities. In 2000, some
19.9% of people in central cities lived in poverty, while only 7.5% of those in the suburbs did so
(Glaeser, Kahn, & Rappaport, 2008). In 2006, the overall percentage of urban residents in poverty
was approximately 13.9%, compared with 13.3% overall in the United States. However, one must
note that the percent of individuals living in poverty in urban areas closely mirrors the national
average, because urban areas largely skew the mean. When central cities and suburbs are compared,
principal cities of metropolitan areas in 2006 have much higher poverty rates (17.7%) than the
national average and suburbs of metropolitan areas (9.6%) (U.S. Census, 2006).
Poverty rates are also highly correlated with unemployment. According to the tables in Appendix
A, individuals living below the poverty line in our reference cities are much more likely to be
unemployed. The situation is dire in Detroit, where 38% of working-age individuals living below the
poverty line are out of work, whereas the national average is approximately 5%.2 Individuals living
in poverty have less access to human capital, such as education or job training, over their lifetimes
(see Ehrenberg & Smith, 2008). Their diminished skill-sets lead to lower wages in the job market, all
else being equal, thus perpetuating the poverty cycle.
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 70
70 • Foundation
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Patterns of housing and home ownership also indicate differences in economic status among
urban residents. Home ownership is not randomly distributed; in fact, the opposite is true: large
pockets of rental property exists primarily in urban areas and principal cities. We highlight a few key
housing differences among urban residents, rural residents, and national averages (U.S. Census,
2006):
• In 2006, 14.4% of urban residents (16.1% for principal cities) lived in a different house
within the same state as they did in the year before—13.5% and 10.4% for United States
and rural averages respectively.
• In 2006, the median house price for urban areas was $203,700 ($195,600 for principal
cities)—$185,200 and $146,500 for the United States and rural averages respectively.
• In 2006, some 63% of urban housing was owner-occupied (53% for principal cities)—67%
and 82% for United States and rural averages respectively.
• In 2006, some 38% of residents with mortgages in urban areas (40% for principal cities)
spent more than 30% of their household income on monthly living expenses—37% and
33% for United States and rural averages respectively.
• In 2006, some 48% of renter-occupied units in urban areas (50% for principal cities) spent
more than 30% of their household income on rent and utilities—46% and 33% for United
States and rural averages respectively.
We can see that there are stark housing differences between urban (especially principal cities) and
rural areas. These differences are less extreme between urban areas and the national average, although
the national averages are skewed, since four times as many people live in urban areas than rural ones.
Urban residents are more likely to rent and to spend more than 30% of their household income on
living expenses. Home ownership has many benefits, including a stronger sense of community, an
increased propensity to vote, and a decrease in transience and mobility (Rothstein, 2004).
Educational Attainment
Educational attainment is a gateway to higher-paying jobs and is related to an increase in positive
outcomes, such as the propensity to vote, own a home, and pursue a healthier lifestyle (Oreopoulos,
2007; Rothstein, 2004). The aggregate attainment for individuals in urban and rural areas appears to
be approximately equal across geographic areas (see Table 6.3). However, within urban areas there
are clear attainment demarcations where the impoverished and minority populations attain lower
levels of education (see Appendix A). This poses a problem. Parent education level, especially the
mother’s education, is highly correlated with student achievement and is often one of the best predictors of this (Rothstein, 2004). Furthermore, individuals with higher levels of education amass
more wealth over the course of their lives than those with lower levels of education (Hanushek, 2009).
The Economics of Urban Education
Indicators such as population diversity, poverty, and socioeconomic status have commonly been
associated with a variety of educational outcomes. The variables have particular relationships to
student achievement, resources, and teacher allocation. In the following sections we explain how
scholars conceptualize the economics of education. We explain the statistical models (commonly
referred to as production function models) that researchers use to analyze how social and economic
factors relate to public schooling and achievement. We then examine some of the extant research that
is salient in understanding the particular challenges of education in urban settings.
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 71
Economics of Urban Education • 71
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Table 6.3 Educational Attainment Urban vs. Rural (2006)
Percentage of people
aged 25+ who have
completed high school %
Percentage of people
aged 25+ who have
completed a bachelor’s
degree %
Percentage of people
aged 25+ who have
completed an advanced
degree %
United States
Urban
Rural
84
84
84
27
29
20
10
11
7
In metropolitan statistical area
In principal city
Not in principal city
85
82
86
29
30
29
11
11
10
In micropolitan statistical area
In principal city
82
82
18
20
6
8
An Economic Paradigm Applied to Education Research
Economists often conceptualize production functions (PFs) that hypothesize some relationship
between inputs and outputs (Varian, 1999). For example, one might reasonably hypothesize that the
more a student studies (the input), the more likely it is that he or she will achieve higher test scores
(the output). Education researchers and economists build on this simplistic example, using more
complex relationships to measure the effect of student, family, and school inputs on student
achievement. The following equation is an example of a traditional educational PF where a student’s
achievement is a function of four key areas of input:
Achievement = f (H, P, T, S)
where H represents the student’s home environment, P represents the student’s peer group, T
represents the quality of a student’s teachers, and S represents the student’s school environment
(Gottfried, 2009; Hanushek, 1979; Varian, 1999). The sum of a student’s inputs influences his or her
achievement in school. The previously discussed characteristics of urban areas (i.e., diversity, poverty,
socioeconomic status) are potential inputs in the production function. However, as discussed further
on, urban areas also exhibit differing levels of teacher quality and school resources. Such inputs are
areas of controversy and debate for researchers and education reformers.
Race and Education
Race and ethnicity are significant factors in understanding educational outcomes. Diverse student
bodies typically characterize public schools in urban areas. The exit of White students from urban
schools is one particular concern. Betts and Fairlie (2003) found that for every four immigrants
who arrive in U.S. public high schools, on average one student—usually a White student—transfers
to a private school, further compounding the racial achievement gap. The relationship between
White flight and immigration was strongest for immigrants who speak a language other than English
at home (Betts & Fairlie, 2003). School choice reforms, such as charter schools, also show evidence
of segregation in some states. For example, Ladd, Clotfelter, and Vigdor (2003) found that open
enrollment in North Carolina led to more segregation by both race and class than when students were
assigned to schools by catchments zones. The increase in immigrant students and decrease in White
students leave schools with segregated populations.
A major concern among educators, policymakers, and researchers is the persistent racial gap in
student achievement (Clotfelter, Ladd, & Vigdor, 2009; Hanushek & Rivkin, 2006; Fryer & Levit,
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 72
72 • Foundation
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
2004; Lee, 2002; Phillips & Chin, 2004). Traditionally, the debate centers on what to do about the
White–Black achievement gap (Rothstein, 2004), although noticeable gaps exist between other
minority groups as well. Whereas some research finds that the White-minority gaps grow as students
progress through school (Fryer & Levit, 2006; Hanushek & Rivkin, 2006), it remains constant over
time; there may be also differential effects by race (Clotfelter et al., 20093) or ability (Reardon &
Galindo, 2009). The achievement gap between White and minority students is estimated to be
between 0.5 and 1.0 standard deviations in math and slightly less in reading (Clotfelter et al., 2009).
aTabTable 6.4 displays the achievement gaps between White and Black, and White and Hispanic
students on the 2007 National Assessment of Educational Progress (NAEP) assessment. This
simplistic snapshot parallels the trends in previous research: the Black–White achievement gap tends
be larger than the Hispanic–White gap, and the Black–White gap appears to be growing over time
while the Hispanic–White gap remains fairly constant.
The gap also exists when we extend the analysis to educational attainment for populations 25 years
of age and older. Hispanic and Black individuals, on average, are less likely to attain post-high school
education (see Table 6.5). Considering that there is a strong correlation between parental education
level and college-going rates for children (Hanushek & Lindseth, 2009; Keane & Roemer, 2009), the
achievement gaps are likely to be perpetuated without some intervention. Appendix B denotes two
more important trends: more schooling leads to higher earnings and minority individuals earn less
than Whites and Asians, even when they have equal education levels. In the next section, the effects
Table 6.4 Achievement Gaps between Students, 2007, NAEP Fourth (Eighth) Grade
English
White–Black
White–Hispanic
Not eligible for free
lunch/Eligible for
free lunch
Non-English language
learner/English
language learner
LAUSD
Washington, DC
New York
Chicago
Boston
1 (43)
67 (NA)
26 (30)
33 (27)
25 (25)
37 (34)
52 (NA)
28 (29)
26 (11)
26 (34)
24(14)
28 (18)
31 (26)
23 (19)
18 (19)
35 (40)
-1 (NA)
28 (43)
22 (34)
18 (47)
32 (40)
54 (NA)
22 (30)
31 (39)
24 (42)
31 (32)
42 (NA)
18 (26)
25 (22)
19 (34)
18 (16)
20 (20)
17 (26)
22 (23)
13 (19)
25 (38)
5 (23)
24 (38)
16 (22)
7 (37)
Math
LAUSD
Washington, DC
New York
Chicago
Boston
Source: National Center for Education Statistics, 2008
Table 6.5 Educational Attainment by Race, 25 years of Age or Older in 2007
Total
White
Black
Hispanic
Asian
Percent at least
high school %
Percent at least
some college %
Percent at least
bachelor’s degree %
Percent at least
advanced degree %
85
89
80
61
86
54
59
46
32
68
28
30
17
13
50
10
11
6
4
20
Source: U.S. Census Bureau, American Community Survey, 2007
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 73
Economics of Urban Education • 73
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
of socioeconomic indicators–primarily parent education and poverty levels—on student achievement are discussed.
Effects of Socioeconomic Indicators on Education
An important part of the education production function is the student’s out-of-school experience,
and a student’s socioeconomic status (SES) is a key proxy for the quality of that. One’s SES influences
the amount of human and social capital accumulated over one’s lifetime. Research consistently shows
that a student’s socioeconomic indicators are the strongest determinant of future education
achievement (Coleman, 1966; Hanushek, 2003; Rothstein, 2004). We discuss two main indicators
below: parental education levels and living in poverty.
A common socioeconomic input variable is parental education level in an education production
function (Hanushek, 2003). Beginning with the Coleman report, numerous research findings suggest
that parental education level is among the best predictors of student achievement (Coleman, 1966;
Rothstein, 2004). In particular, higher-education attainment for mothers is strongly related to higher
achievement in their children. Various theories may help to explain this relationship. The concept of
human capital, or habitus, is one common discussion point. Parents may transmit prior experiences
and knowledge that is critical for academic success to their children. Thus, parents with higher levels
of human capital (i.e., education attainment) may influence the success of their child’s academic
achievement (Altonji & Dunn, 1996; Ehrenberg & Smith, 2008).
The idea of social capital might also explain the relationship between parents and students (see
Dika & Singh, 2002; Portes, 1998). Parents with higher social capital have wider networks of
relationships to whom they can turn for assistance as their children progress through schooling.
Regardless of theoretical underpinnings, parental education level is a commonly used socioeconomic
variable in the economics of education. The levels of human and social capital have a palpable
differentiating effect on student achievement, as seen in Figure 6.1, which, using the 2007 eighthgrade reading NAEP scores as a snapshot, shows clear demarcations.
Urban, lower-SES students also enter school with a smaller skill set than their higher-SES
counterparts. Although all students learn some basic numeracy and literacy skills at home, the quality
of out-of-school exposure is not equally dispersed (Entwisle, Alexander, & Olson, 1994; Slaughter &
Epps, 1987). These out-of-school differences lead to different learning rates. Alexander, Entwisle, and
Olson (2001) found that SES predicted the seasonal learning curves of urban elementary school
students. While in school, students, regardless of SES, learned at similar rates, keeping the achievement gap between classes static. However, the class achievement gap widened over the summer as
the upper-SES children made moderate gains while the lower-SES children remained stagnant (see
also Cooper, Nye, Charlton, Lindsay, and Greathouse, 1996, for a literature review of the effects SES
have on seasonal learning).
The effects of poverty also manifest themselves in two related areas: childhood nutrition and
health. In a study of the effects of malnourishment on early childhood academic achievement,
Glewwe, Jacoby, and King (2001) found that individual learning endowments, home environment,
and parental characteristics did not fully explain the variation in academic outcomes between
nourished and malnourished students. Stopping short of causality, their findings imply that proper
nourishment has a positive effect on student achievement.
Another nutritional phenomenon affecting low-income children is food insecurity, or where
a child is not sure where his or her next meal will come from. In 1999, some 42% of children in
impoverished households were food-insecure, and there are stronger negative effects of food
insecurity among Hispanic and African American children than among other groups (Winicki &
Jemison, 2003). Although the association between food insecurity and academic achievement may
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 74
74 • Foundation
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
280
> High school
High school
Some college
College graduate
270
260
250
240
230
220
210
National
Atlanta
Chicago
DC
Los Angeles
New York City
Figure 6.1 Eighth-grade Reading NAEP Scores by Parent Education, 2007
represent a spurious correlation (e.g., other home, environmental, or parental characteristics may
play a role), research indicates that food insecurity can have lasting developmental consequences for
children (Jyoti, Frongillo, & Jones, 2005). Food insecurity is also associated with lower income and
lower levels of education among pregnant women (Laraia, Siega-Riz, Gundersen, & Dole, 2006). Poor
health or poor health decisions are also associated with lower levels of education attainment (Hurre,
Aro, Rahkonen, & Komulainen, 2006).
School Resources and Finance
The previous sections reviewed the literature on out-of-school inputs of a student’s production
function, but what about the within-school inputs? How do inputs within a student’s school and
district influence his or her performance? Do disparities in funding levels—which theoretically affect
school inputs such as class sizes and teacher salaries—differentially affect student achievement? In
this section we review the literature on the relationship between school resources and finances, and
student achievement.
Over the past three decades, school spending in the United States has approximately quadrupled,
while student achievement has remained constant. Over the same time period, student–teacher ratios
have decreased, and the average experience of teachers has increased, along with the percent of
teachers with at least a master’s degree and teachers’ salaries (Hanushek, 2009). On the surface, these
improvements seem beneficial for student achievement, especially in urban areas, where districts
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 75
Economics of Urban Education • 75
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
receive more per-pupil funding than suburban schools to compensate for their disadvantaged
student populations (Rubenstein, Schwartz, Stiefel, & Amor, 2007). Decreases in student–teacher
ratios, however, are associated with rent-seeking teachers’ unions, which have a negative impact on
student achievement (Eberts, 1983; Hoxby, 1997; Moe, 2009). Furthermore, teachers’ education
levels, salaries, and years of experience are poor proxies for teacher quality (Goldhaber & Brewer,
2000; Hanushek, 2003). Tacit assumptions that seem logical on the surface actually lead to equivocal
results.
Research consistently suggests that the amount of school funding is not related to increases in
student achievement. The level of funding is not a condition of student achievement; quality is
(Hanushek, 2003; Rubenstein et al., 2007). Teacher quality is the strongest within-school determinant of student achievement (Hanushek, 2003), but it is also very difficult to define. Typical methods
of categorizing teacher quality—experience, credentials, education—have little effect on student
achievement (Goldhaber & Brewer, 2000). Instead of emphasizing the levels in school inputs, production function research has shown that policymakers and education stakeholders should focus on
the quality of the inputs.
Teacher Quality and Distribution
Although the level of school inputs has a negligible effect on students, the quality of the inputs,
especially teacher quality, is a strong determinant of student achievement (Hanushek, 2003). Thus,
questions pertaining to teacher attrition, retention, and recruitment become critical (Glazerman,
Mayer, & Decker, 2006; Boyd, Grossman, Lankford, Loeb, & Wyckoff, 2006; Rockoff, 2004). A
similarly important consideration is the distribution of teachers with respect to quality. Are good
teachers spread evenly over geographic areas, regardless of the population’s race, SES, and other
background characteristics? Is the concentration of certain teachers conditioned on school, community, and student characteristics? Empirical research suggests that the dispersion of teachers is not
random (Clotfelter, Ladd, & Vigdor, 2006; Rivkin, Hanushek, & Kain, 2005; Lankford, Loeb, &
Wyckoff, 2002). In general, urban and low-performing districts struggle to recruite and retain
teachers (Hanushek, Kain, & Rivkin, 2004).
Research findings suggest that urban, minority, and lower-achieving students often receive less
experienced and less qualified teachers (Clotfelter et al., 2006; Lankford et al., 2002; Jacob, 2007).
Such distribution patterns among teachers occur for several reasons. Lankford et al. (2002) find that
teachers often leave difficult teaching environments (e.g., urban and low-performing schools) for
more appealing schools. The palpable trend of teacher flight has led to the creation of philanthropic
and social endeavors (e.g., Teach for America, The New Teacher Project, and the New York City
Teaching Fellows) that attempt to increase the demand for hard-to-staff schools (Glazerman et al.,
2006; Boyd et al., 2006). These last investigators also find that teachers prefer to work closer to where
they live, so that schools in the suburbs and wealthier communities have an easier time hiring higherquality teachers.
Although possibly less visible, the problem of teacher sorting can also occur within schools. As
suggested by Clofelter et al. (2006), more experienced teachers can influence their placements by
leveraging their seniority, often avoiding more difficult positions. In effect, the more experienced
teachers avoid the less able students, yet these are the students who need these teachers the most
(Rivkin et al., 2005). In turn, placing inexperienced teachers with the neediest students leads to higher
attrition rates among new teachers (Murnane & Steele, 2007). These distributional effects make
addressing issues of student achievement and teacher recruitment and retention in urban schools
very difficult to address.
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 76
76 • Foundation
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Conclusion
In this chapter we highlight the unique characteristics of urban areas and their implications for public
schools. Urban schools have diverse student bodies from different cultural, language, and learning
backgrounds. Students are also more likely to experience hardships related to lower economic and
educational opportunities. These out-of-school factors of urban centers play a significant role in the
academic success of schools and students. For education researchers, social and economic variables
are significant variables in education production functions. For educators and district leaders, the
characteristics of urban settings are unique challenges that must be addressed if schools are to be
successful in educating students.
Public schools can make a difference in helping to improve the academic achievement of urban
students. However, research in the economics of education suggests that the amount of money and
resources put into public schools does not relate to student achievement. In lay terms, merely
throwing money at the problem will not lead to improved student outcomes. Rather, how districts
use their funding to improve the quality of education promises to have more positive effects. In
particular, improving teacher quality and distributing effective teachers to needy urban areas are two
strategies that may improve student achievement.
As researchers and educators consider the lessons from research on the economics of education,
one must also remember the interconnected quality of variables. Population diversity, poverty,
socioeconomic status, and education attainment are interrelated factors. These variables do not
function in isolation from one another. Economic opportunity, social and cultural contexts, and
educational outcomes combine to create a cycle of hardship and lower achievement among urban
students. Similarly, education reforms that aim to improve student achievement must address the
complex challenges posed by urban centers. Policymakers and educators must consider strategies to
address the learning needs of diverse student populations and distribute high-quality resources such
as effective teachers to areas of need. While these recommendations sound straightforward, such
educational change will undoubtedly fly against the established politics, organizational culture, and
policies that currently govern public education. The challenges are significant, but scholarly work in
the economics of education also teaches us that the benefits of successfully educating urban students
can be great.
Appendix A
Table 6.6 Employment Statistics (Age 16 Years and Older) for Atlanta
Percent of total
population %
Percent in the
labor force %
Percent
employed %
Percent
unemployed %
Education (25–64)
High school
High school graduate
Some college
At least BA/BS
11
24
26
38
66
76
82
86
60
70
76
83
11
8
6
3
Below poverty (20–64)
10
56
41
27
White alone (16+)
Blacks
Hispanics
Asians
52
33
9
5
70
72
76
69
66
63
71
65
5
12
6
5
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 77
Economics of Urban Education • 77
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Table 6.7 Employment Statistics (Age 16 Years and Older) for Chicago
Education (25–64)
High school
High school graduate
Some college
At least BA/BS
Percent of total
population %
Percent in the
labor force %
Percent
employed %
Percent
unemployed %
13
25
27
35
64
75
81
86
57
67
76
83
11
8
7
3
Below poverty (20–64)
10
50
35
30
Whites alone (16+)
Blacks
Hispanics
Asians
57
19
18
6
68
61
71
68
64
51
65
64
5
17
8
6
Percent of total
population %
Percent in the
labor force %
Percent
employed %
Percent
unemployed %
Education (25–64)
High school
High school graduate
Some college
At least BA/BS
20
23
27
30
70
77
82
85
65
71
78
83
7
7
5
3
Below poverty (20–64)
12
56
44
22
White alone (16+)
Black
Hispanic
Asian
51
16
27
5
69
69
73
67
66
60
68
63
5
13
7
6
Table 6.8 Employment Statistics (Age 16 Years and Older) for Dallas
Table 6.9 Employment Statistics (Age 16 Years and Older) for Detroit
Percent of total
population %
Percent in the
labor force %
Percent
employed %
Percent
unemployed %
Education (25–64)
HS
HS graduate
Some college
At least BA/BS
11
28
32
29
54
70
79
84
43
62
73
81
21
12
8
4
Below poverty (20–64)
12
49
30
38
Whites alone (16+)
Blacks
Hispanics
Asians
67
25
3
3
65
59
68
68
60
47
59
64
8
20
14
6
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 78
78 • Foundation
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Table 6.10 Employment Statistics (Age 16 Years and Older) for Los Angeles Urbanized Area
Education (25–64)
High school
High school graduate
Some college
At least BA/BS
Percent of total
population %
Percent in the
labor force %
Percent
employed %
Percent
unemployed %
23
22
25
29
67
74
79
84
62
70
75
81
7
6
5
4
Below poverty (20–64)
12
49
40
20
Whites alone (16+)
Blacks
Hispanics
Asians
34
7
41
15
64
61
68
61
60
54
63
58
5
11
7
5
Table 6.11 Employment Statistics (Age 16 years and Older) for Milwaukee Urbanized Area
Percent of total
population %
Percent in the
labor force %
Percent
employed %
Percent
unemployed %
Education (25–64)
High school
High school graduate
Some college
At least BA/BS
11
28
29
32
60
76
82
87
51
70
77
84
14
8
5
3
Below poverty (20–64)
12
52
37
28
Whites alone (16+)
Blacks
Hispanics
Asians
72
16
8
3
68
62
70
67
64
51
63
63
5
18
10
7
Table 6.12 Employment Statistics (Age 16 Years and Older) for New York City Urbanized Area
Percent of total
population %
Percent in the
labor force %
Percent
employed %
Percent
unemployed %
Education (25–64)
High school
High school grad
Some college
At least BA/BS
14
27
22
38
60
74
80
85
55
69
75
82
10
6
6
3
Below poverty (20–64)
11
43
32
25
Whites alone (16+)
Blacks
Hispanics
Asians
52
17
20
9
63
63
65
65
60
56
60
62
5
11
9
5
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 79
Economics of Urban Education • 79
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Table 6.13 Employment Statistics (Age 16 Years and Older) for Philadelphia Urbanized Area
Education (25–64)
High school
High school graduate
Some college
At least BA/BS
Percent of total
population %
Percent in the
labor force %
Percent
employed %
Percent
unemployed %
10
31
25
35
54
74
82
86
47
69
78
84
13
7
5
3
Below poverty (20–64)
11
43
34
15
Whites alone (16+)
Blacks
Hispanics
Asians
68
21
6
4
66
60
63
66
63
52
57
62
5
14
9
5
Appendix B
Refer to Table 6.4 on page 80
Notes
1.
2.
3.
Metropolitan areas include at least one urbanized area with at least 50,000 people, and micropolitan areas include at least
one urban cluster of between 10,000 and 50,000 people. Also, the largest principal city in each metropolitan or
micropolitan area is known as a principal city (O’Sullivan, 2007).
Unemployment rates capture only people who are actively seeking employment; thus, these rates are likely a lower
bound to the true estimate of out-of-work individuals.
Clotfelter, Ladd, & Vigdor (2009) found that the White–Hispanic gap remained stagnant over time, whereas the
White–Black gap grew by 11% over a six-year span.
References
Alexander, K. L., Entwisle, D. R., & Olson, L. S. (2001). Schools, achievement, and inequality: A seasonal perspective.
Educational Evaluation and Policy Analysis, 23(2), 171–191.
Altonji, J. G., & Dunn, T. A. (1996). The effects of family characteristics on the return to education. The Review of Economics
and Statistics, 78(4), 692–704.
Betts, J. R., & Fairlie, R. W. (2003). Does immigration induce ‘native flight’ from public schools into private schools. Journal
of Public Economics, 87, 987– 1012.
Boyd, D., Grossman, P., Lankford, H., Loeb, S., & Wyckoff, J. (2006). How changes in entry requirements alter the teacher
workforce and affect student achievement. Education Finance and Policy, 1(2), 176–216.
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. (2006). Teacher–student matching and the assessment of teacher effectiveness. The
Journal of Human Resources, 41(4), 778–820.
Clotfelter, C. T., Ladd, H. F., & Vigdor, J. (2009). The academic achievement gap in grades 3 to 8. Review of Economics &
Statistics, 91(2), 398–419.
Coleman, C. T., Campbell, E., & Hobson, C. (1966). Equality of educational opportunity. Washington, DC: Department of
Health, Education, and Welfare.
Cooper, H., Nye, B., Charlton, K., Lindsay, J., & Greathouse, S. (1996). The effects of summer vacation on achievement test
scores: A narrative and meta-analytic review. Review of Educational Research, 66(3), 227–268.
Dika, S. L., & Singh, K. (2002). Applications of social capital in educational literature: A critical synthesis. Review of
Educational Research, 72(1), 31–60.
Eberts, R. W. (1983). How unions affect management decisions: Evidence from public schools. Journal of Labor Research, 4(3),
239–247.
Ehrenberg, R. G., & Smith, R. S. (2008). Modern labor economics: Theory and public policy. New York: Addison-Wesley.
Entwisle, D.R., Alexander, K.L., & Olson, L.S. (1994). The gender gap in math: Its possible origins in neighborhood effects.
American Sociological Review, 59, 822–838.
Fryer, R. G., & Levit, S. D. (2004). Understanding the Black–White test score gap in the first two years of school. Review of
Economics and Statistics, 86(2), 247–281.
Fryer, R. G. & Levit, S. D. (2006). The Black–White test score gap through third grade. American Law and Economics Review,
8(2), 249–281.
T&F - 1st proofs - not for distribution
65
52
46
49
51
180
610
123
48
84
61
103
69
202
393
213
33,452
40,481
27,276
35,609
36,763
28,071
37,940
24,602
41,568
46,789
35,769
43,731
45,690
34,671
47,336
29,749
26,125
30,381
23,446
24,220
22,040
27,180
20,341
20,192
21,311
16,163
19,640
18,804
24,964
108
161
382
551
100
111
110
86
120
197
447
125
121
137
116
84
34,903
36,647
29,690
30,106
27,838
37,632
27,477
29,253
29,052
23,322
24,639
23,836
32,882
32,436
21,219
26,894
Earnings
High school
graduate
111
76
273
347
288
167
90
99
99
225
347
197
105
63
64
52
Margin of
error1 (±)
41,793
42,081
35,236
39,800
36,218
46,562
34,745
34,291
34,663
30,034
32,160
30,801
40,769
41,035
27,046
32,874
Earnings
60
62
212
700
217
121
122
92
101
193
277
162
60
83
69
82
Margin of
error1 (±)
Some college or
associate’s degree
58,288
59,644
47,163
65,279
45,396
65,011
47,333
47,904
48,667
41,972
46,957
40,068
56,118
67,397
38,628
46,805
Earnings
323
195
410
688
401
272
137
198
193
290
463
346
136
227
156
103
Margin of
error1 (±)
Bachelor’s degree
76,578
77,617
61,174
82,200
61,395
88,840
61,229
61,496
61,681
54,527
70,280
52,268
75,140
77,219
50,937
61,287
Earnings
281
304
466
707
624
454
180
125
130
912
777
561
243
347
133
113
Margin of
error1 (±)
Advanced degree
Source: U.S. Census Bureau, American Community Survey 2007
12:07
22,602
14,202
19,405
Margin of
error1 (±)
Earnings
Earnings
Margin of
error1 (±)
Not a high school
graduate
Total
9/6/11
1 A margin of error is a measure of an estimates variability. The larger the margin of error in relation to the size of the estimate, the loss reliable the estimate. When added to and
subtracted from the estimate, the margin of error forms the 90-percent confidence interval
All workers
Sex
Male
Female
Race and Hispanic origin
White alone
Non-Hispanic White alone
Black alone
Asian alone
Hispanic (any race)
Full-time, year-round workers
Sex
Male
Female
Race and Hispanic origin
White alone
Non-Hispanic White alone
Black alone
Asian alone
Hispanic (any race)
Characteristic
Table 6.14 Median earnings for workers aged 25 and over by educational attainment, work status, sex, and race and Hispanic origin, 2007 (Earnings in dollars)
Urban-01-p.qxd
Page 80
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 81
Economics of Urban Education • 81
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Glaeser, E. L., Kahn, M. E., & Rappaport, J. (2008). Why do the poor live in cities? The role of public transportation. Journal
of Urban Economics, 63, 1–24.
Glazerman, S., Mayer, D., & Decker, P. (2006). Alternative routes to teaching: The impacts of Teach for America on student
achievement and other outcomes. Journal of Policy Analysis and Management, 25(1), 75–96.
Glewwe, P., Jacoby, H. G., King, E. M. (2001). Early childhood nutrition and academic achievement: A longitudinal analysis.
Journal of Public Economics, 81, 345–368.
Goldhaber, D., & Brewer, D. J. (2000). Does teacher certification matter? High school teacher certification status and student
achievement. Educational Evaluation and Policy Analysis, 22(2), 129–145.
Gottfried, M. A. (2009). Excused versus unexcused: How student absences in elementary school affect academic achievement.
Educational Evaluation and Policy Analysis, 31, 392–419.
Hanushek, E. A. (1979). Conceptual and empirical issues in the estimation of educational production functions. Journal of
Human Resources, 14(3), 351–388.
Hanushek, E. A. (2003). The failure of input-based school policies. Economic Journal¸113, F64–F98.
Hanushek, E. A., & Kimko, D. D. (2000). Schooling, labor-force quality, and the growth of nations. The American Economic
Review, 90(5), 1184–1208.
Hanushek, E. A. & Lindseth, A. A. (2009). Schoolhouses, courthouses, and statehouses: Solving the funding-achievement puzzle
in America’s public schools. Princeton, NJ: Princeton University Press.
Hanushek, E. A. & Rivkin, S. G. (2006). School quality and the black–white achievement gap. NBER working paper no. 12651.
Hanushek, E. A., Kain, J. F., & Rivkin, S. G. (2004). Why public schools lose teachers. The Journal of Human Resources, 39,
326–354.
Hoxby, C. M. (1996). How do teachers unions affect education production? Quarterly Journal of Economics, 111, 671–718.
Hurre, T., Aro, H., Rahkonen, O., & Komulainen, E. (2006). Health, lifestyle, family and school factors in adolescence:
Predicting adult educational level. Educational Research, 48(1), 41–53.
Jacob, B. (2007). The challenges of staffing urban schools with effective teachers. The Future of Children, 17(1), 129–153.
Jyoti, D. F., Frongillo, E. A., & Jones, S. J. (2005). Food insecurity affects school children’s academic performance, weight gain,
and social skills. The Journal of Nutrition, 135(12), 2831–2839.
Keane, M. P. & Roemer, J. E. (2009). Assessing policies to equalize opportunity using an equilibrium model of educational
and occupational choices. Journal of Public Economics, 93, 879–898.
Ladd, H.,F., Clotfelter, C.T., & Vigdor, J. (2003). Segregation and resegregation in North Carolina’s public school classrooms.
North Carolina Law Review, 81(4), 1463–1511.
Lankford, H., Loeb, S., & Wyckoff, J. (2002). Teacher sorting and the plight of urban schools: A descriptive analysis.
Educational Evaluation and Policy Analysis, 24(1), 37–62.
Laraia, B., Siega-Riz, A., Gundersen, C., & Dole, N. (2006). Psychosocial factors and socioeconomic indicators are associated
with household food insecurity among pregnant women. The Journal of Nutrition, 136, 177–182.
Lee, J. (2002). Racial and ethnic achievement gap trends: Reversing the progress toward equity? Educational Researcher, 31,
3–12.
Moe, T. M. (2009). Collective bargaining and the performance of the public schools, American Journal of Political Science,
53(1), 156–174.
Murnane, R. & Steele, J. (2007). What is the problem? The challenge of providing effective teachers for all children. The Future
of Children, 17(1), 15–43.
National Center for Education Statistics (2008). Home page. Accessed July 24, 2008 at http://nces.ed.gov/nationsreportcard/
National Commission on Excellence in Education (1983). A nation at risk: The imperative for educational reform (ED 226
006). Washington, DC: Author.
Oreopoulos, P. (2007). Do dropouts drop out too soon? Wealth, health, and happiness from compulsory schooling. Journal
of Public Economics, 91, 2213–2229.
O’Sullivan, A. (2007). Urban economics. New York: McGraw-Hill.
Philips, M. & Chin, T. (2004). School inequality: What do we know? In K. Neckerman (Ed.), Social inequality: New York:
Russell Sage Foundation.
Portes, A. (1998). Social capital: Its origins and applications in modern sociology. Annual Review of Sociology, 24, 1–24.
Reardon, S. F. & Galindo, C. (2009). The Hispanic–White achievement gap in math and reading in the elementary grades.
American Education Research Journal, 46(3), 853–891.
Rivkin, S. G., Hanushek, E. A., & Kain, J. F. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2),
417–458.
Rockoff, J. (2004). The impact of individual teachers on student achievement: Evidence from American Economic Review,
94(20), 247–252.
Rothstein, R. (2004). Class and Schools: Using Social, Economic, and Educational Reform to Close the Black–White Achievement
Gap. Washington DC: Economic Policy Institute.
Rubenstein, R., Schwartz, A. E., Stiefel, L., Amor, H. B. H. (2007). From districts to schools: The distribution of resources
across schools in big city school districts. Economics of Education Review, 26, 532–545.
Slaughter, D. & Epps, E. (1987). The home environment and academic achievement of black children and youth: An overview.
The Journal of Negro Education, 56(1), 3–20.
State Education Data Center (Dist.) (2009). Student demographics and achievement. Washington, DC: Council of Chief State
School Officers. Accessed October 1, 2009 at www.SchoolDataDirect.org
U.S. Census Bureau (2006). 2006 Population estimates, detailed tables. Retrieved May 17, 2011 from: http:factfinder.census.
gov/servlet/DTGeoSearchByListServlet?ds_name=PEP_2006_EST&_lang=en&_ts=323353710288
T&F - 1st proofs - not for distribution
Urban-01-p.qxd
9/6/11
12:07
Page 82
82 • Foundation
1
2
3
4
5
6
7
8
9
0
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
U.S. Census Bureau (2007). Income, earnings, and poverty data from the 2007 American community survey. Retrieved May 17,
2011 from: www.census.gov/prod/2008pubs/acs-09.pdf
U. S. Department of Transportation (2011). Census 2000 populations statistics. Retrieved 17 May, 2011 from: www.fhwa.
dot.gov/planning/census/cps2k.htm
Varian, H. R. (1999). Intermediate microeconomics: A modern approach. New York: W. W. Norton.
Winicki, J. & Jemison, K. (2003). Food insecurity and hunger in the kindergarten classroom: Its effect on learning and growth.
Contemporary Economic Policy, 21(2), 145–157.
T&F - 1st proofs - not for distribution