Center on Education Policy and Workforce Competitiveness
Working Paper:
Understanding Rural Teacher Recruitment and
the Role of Community Amenities
Luke C. Miller*
Attracting and keeping teachers is “the main problem of rural school districts” according to the
American Association of School Administrators. In addition to low salaries, rural school administrators frequently cite poor community amenities as a contributing factor to their teacher
recruitment challenges. This paper is the first attempt to test the community amenity hypotheses in a multivariate framework using administrative data on teacher employment patterns. I
employ a McFadden discrete-choice model to analyze administrative data from New York State
on nine cohorts of first-time teachers who began their careers in any New York State public
school between 1994 and 2002. In doing so, it improves our understanding of who chooses
to teach in rural schools. Teachers with stronger academic preparation are shown to be less
likely to become rural teachers. Results generally confirm the community amenities revealing
teachers to preferring relatively less geographically and professionally isolated rural communities as well as rural communities with relatively more shopping venues. Policy implications for
diminishing their negative influence and enhancing their positive influence are discussed.
* University of Virginia
405 Emmet Street, P.O. Box 400277
Charlottesville, VA 22904
[email protected]
Updated 6 September 2012.
Center on Education Policy and Workforce Competitiveness
University of Virginia
PO Box 400879
Charlottesville, VA 22904
CEPWC working papers are available for comment and discussion only. They have not been peer-reviewed.
Do not cite or quote without author permission.
I am tremendously grateful to the research team of Don Boyd, Hamilton Lankford, Susanna Loeb, and James
Wyckoff for providing me access to the New York teacher data and to Sean Reardon for his help improving the
specification of the job search model. Daphna Bassok, Pam Grossman, Eric Hanushek, and Susanna Loeb provided
useful feedback on earlier versions. All errors are attributable to the author.
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Miller – Rural Teacher Recruitment
UNDERSTANDING RURAL TEACHER RECRUITMENT AND THE ROLE OF COMMUNITY
AMENITIES
By Luke C. Miller, University of Virginia
1.
Introduction
Attracting and keeping teachers is “the main problem of rural school districts” according to
the American Association of School Administrators (cited in McClure et al., 2003). Teachers are the
most important educational input over which schools have direct influence. As efforts to improve
teacher quality increase, there is a growing belief that important differences between rural and nonrural teacher labor markets pose unique challenges to recruiting quality educators to rural schools.
And that these differences are overlooked by state and federal policymakers. For example, the
federal government, several years after the implementation of the No Child Left Behind Act,
responded to such criticism and awarded rural schools a three-year extension to ensure all their
teachers were highly qualified as the law required (Paige, 2004). Similarly, the school improvement
models embraced by Race to the Top have been criticized for presuming the rural context could
support the approaches tested in more urbanized communities (Miller & Hansen, 2010). A handful
of states fund programs to incentivize teachers to accept positions in rural school in recognition of
labor market differences (Loeb & Miller, 2006). Despite the wide-held belief, there are very few
studies that quantify the rural teacher recruitment problem. The present study analyzes nine years of
administrative data from New York State to identify who chooses to become a rural teacher. It also
tests the hypothesis that poor community amenities contribute to the recruitment problem.
Rural school administrators frequently cite poor community amenities as a contributing
factor to their teacher recruitment challenges. In a recent national survey, superintendents identified
the geographic and social isolation of their communities as significant problems in attracting
qualified candidates (Hammer et al., 2005). Elsewhere, rural superintendents also indicated their
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Miller – Rural Teacher Recruitment
schools were less alluring to teacher candidates because of the lack of adequate housing and poor
economic health of the surrounding area (Schwarzbeck et al., 2003). Small, rural communities are
frequently described as “tight knit”. Consequently, candidates from away, who lack a close personal
or familial connection to the rural community, are viewed as being harder to attract to and retain in
rural schools (Bornfeld et al., 1997; Murphy & Angleski, 1996/1997). Lack of shopping
opportunities and poor access to medical care are other amenities thought to place rural schools at a
competitive disadvantage with non-rural schools for teachers’ labor (Boylan et al., 1993; Kleinflield
& McDiarmid, 1986; Murphy & Angleski, 1996/1997).
This paper is the first attempt to test these hypotheses in a multivariate framework using
administrative data on teacher employment patterns. I borrow the McFadden discrete-choice model
developed by Boyd, Lankford, Loeb, and Wyckoff (2005a) to analyze administrative data from New
York State on nine cohorts of beginning teachers in order to address two key issues concerning rural
teacher recruitment: who chooses to teach in rural schools and the role of community amenities in
teachers’ decisions to be a rural teacher. Boyd and colleagues (hereafter referred to as BLLW) reveal
teacher labor markets to be geographically small with teachers revealing strong preference for jobs
close to their hometown. I adapt this model measure a teacher’s probability of selecting a rural
versus non-rural region and assess the influence of distance, teacher characteristics, and community
amenities on this probability. For example, are teachers with stronger academic credentials more or
less likely to teach in rural schools than teachers with other credentials? How does the influence of
distance from family and social networks on the probability of becoming a rural teacher vary across
hometown community type? Do rural schools have a harder time recruiting beginning teachers who
grew up in communities with richer or poorer community amenities? New York State serves as the
case study.
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Miller – Rural Teacher Recruitment
While not thought of as a rural state, New York offers a useful setting in which to study
rural teacher recruitment. More than 50 percent of students attending school in rural communities
do so in just 12 states which tend to be the most populous, most urban states (Johnson & Strange,
2007). New York’s share of the country’s rural student population ranks eighth largest. Therefore, to
ignore the rural context in less rural states is to ignore the educational environment for a substantial
share of America’s rural students. Additionally, the close proximity of many of New York’s rural
communities to suburban and urban schools heightens the competition between rural and non-rural
districts for teachers’ labor and creates a diversity of rural areas to study with some close to urban
areas and others much farther removed. Close proximity to non-rural schools offering more
desirable working conditions has been reported by rural school districts as a hurdle to attracting
quality teachers (Hammer et al., 2005).
The remainder of this paper is organized into five sections. In the first section, I review the
available evidence of the rural teacher recruitment problem, nationally and in New York State, as
well as the implications of recent trends in rural population and composition. I present descriptive
statistics that show how the teachers recruited by New York’s rural schools differ from those
recruited by suburban and urban schools. I discuss the extensive data analyzed in the second section
and the methodology employed to model beginning teachers’ job search presented in greater detail
in the third section. I present my results in section four followed by a discussion of their policy
implications. Section six concludes.
2.
The Rural Recruitment Problem
The teacher sorting literature suggests challenges for rural school staffing. A general finding
across both urban and rural schools is that students living in poverty, minority students, and lower
academic performing students have lower quality teachers on average (Bonesrønning et al., 2005;
Boyd et al., 2002; Clotfelter et al., 2005; Lankford et al., 2002). Rural poverty rivals that of urban
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areas, rural populations are growing increasingly diverse, and rural students underperform their
suburban peers (Provasnik et al., 2007).
Difficulties recruiting teacher to rural schools has yielded several notable differences in the
characteristics of rural and non-rural teachers. Rural teachers are less likely to belong to minority
races/ethnicities (Provasnik et al., 2007). Rural teachers have been found more likely to be novice
teachers and less likely to hold a master’s degree (Jimerson, 2004; Provasnik et al., 2007; Reichardt,
2001; Reichardt, 2002; Reichardt et al., 2003). In Texas, rural schools have a higher percentage of
out-of-field teachers compared to non-rural schools (Jimerson, 2004). Between 1996 and 2000, the
percentage of rural Wyoming teachers not fully certified grew at a faster rate to a higher percentage
(8 percent) than teachers in non-rural communities (Reichardt, 2002). The Wyoming report also
found a greater percentage of rural positions vacant in 2000 than in non-rural schools (Reichardt,
2002). Rural schools in Texas were more likely to use administrators to fill their vacancies than nonrural schools (Jimerson, 2004).
The rural recruitment problem is not unique to teachers. Many rural communities, including
those in New York, are struggling to entice their youth to remain and attract new residents. A
Brookings Institution report on the population trends of Upstate New York (i.e., the 52 counties not
contained in the New York City Metropolitan Statistical Area) labeled the region “the third-slowest
growing ‘State’” (Pendall, 2003). The population grew 1.1 percent during the 1990s. Only West
Virginia and North Dakota grew at slower rates. Digging deeper into the population trends, the
North Country region, the rural northernmost seven counties, which grew 0.3 percent would have
posted a decline were it not for the increase in the prison population. Additionally, more people
moved out of Upstate during the 1990s than moved in (1.7 versus 1.3 million). The North Country
experienced net migration losses with every region in upstate New York. These trends present
challenges at recruiting professionals such as teachers to rural communities.
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Two other Brookings Institution reports show the population trends track closely with
trends in income, poverty, and economic opportunities during the 1990s. Most indicators show the
Upstate economy worsened. Real personal income grew 9 percent compared with 26.4 percent in
the 1980s; per capita personal income grew at such a slow rate so that by 2000 it was 11 percent
lower than the national rate versus 7 percent lower in 1990; and employment gains in the 1990s were
less than half what they were in the 1980s (Pendall et al., 2004). The Upstate economy also
underperformed relative to the nation during the 1990s. Per capital personal income grew at half the
national rate; hourly wages were lower compared to national figures even after controlling for age,
race, sex, and educational attainment; and, poverty rates increased for families, individuals, and
children while they decreased nationally for all three groups (Pendall & Christopherson, 2004). They
conclude these less-than-encouraging economic conditions in which “the best-educated, and
highest-skilled Upstate workers earn low wages compared to similar workers elsewhere in the Unites
States” are “encouraging these workers to leave for regions where they can anticipate higher
earnings” (Pendall & Christopherson, 2004, p. 2). Commentary on the connections between this socalled brain drain and rural education percolate throughout the literature (Howley, 1997;
Edmundson, 2003; Carr & Kefalas, 2009).
It is within this challenging environment rural schools recruit teachers. Teacher labor
markets have been shown to be geographically small and smaller relative to most other professions,
which works in the rural schools’ favor (Boyd, Lankford, Loeb, & Wyckoff, 2005a; Reininger, 2006).
However, poorer community amenities such as higher poverty rates and depressed economic activity
counterbalance, potentially exacerbating the rural recruitment problem.
New York’s rural schools rely more heavily on first-time teachers to fill vacant positions than
non-rural schools necessitated by experienced teacher preferences for suburban over rural schools
(Miller, under review). The recruitment needs of New York’s public schools, measured by the full5
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Miller – Rural Teacher Recruitment
time equivalency status of new hires, are shown in Table 1. The first panel of the table gives the
percent of all full-time teachers who were new hires at schools. The second panel displays the
reliance on beginning teachers to fill open position. Between 1994 and 2002, 16 percent of teaching
positions turned over each year ranging from 14 percent of occupational education teachers to 22
percent of ESL teachers. In general, urban schools had the greatest recruitment needs except for
special education and ESL teachers. For these two subjects, rural schools needed to recruit teachers
to fill a greater percent of their positions than did suburban or urban schools. Twenty-point-six
percent of rural special education teachers were new hires compared to 17.7 percent at suburban
schools and 18.5 percent at urban schools. For ESL, the comparative figures were 22.8 percent for
rural, 21.4 percent for suburban, and 22.1 percent for urban.
(Insert Table 1 about here)
Rural schools are more likely to hire beginning teachers to fill open positions than suburban
schools and, in most subjects, urban schools (Table 1, second panel). Twenty-two-point-two percent
of new hires in rural schools are beginning teachers compared to 20.0 percent in suburban schools
and 23.0 percent in urban schools. However, if you exclude New York City, 17.9 percent of open
urban positions are filled by first-time teachers, lower than in rural schools. Twenty-three percent of
new rural math teachers are beginning teachers whereas the comparable statistic in suburban schools
is 21.7 percent and 20.9 percent in urban schools. Among new special education teachers, 20.2
percent in rural schools are beginning teachers versus 15.3 percent in suburban schools and 16.6
percent in urban schools.
This study attempts to identify characteristics of teachers associated with a higher likelihood
of becoming a rural teacher as well as the characteristics of rural schools and their communities that
assist rural schools recruit teachers. I discuss the data I use in the following section.
3.
Data and Methodology
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My analytic sample consists of teachers who began their teaching career in any New York
State public school between 1993-94 and 2001-02 (1994 to 2002). Data come from a large number
of sources. Information on teachers come from annual administrative datasets maintained by the
New York State Department of Education which allow for teachers to be followed over time as they
progress through their careers within any public school in the State. These data provide
characteristics of the teachers, their teaching assignments, and their schools. I also have data on
where teachers went to high school, where each teacher went to college, the Barron’s
competitiveness rankings of these colleges, and the teachers’ Scholastic Aptitude Test (SAT) scores.
I collected data on community amenities from extant data sources including the New York
State and the National Center for Education Statistics websites, the New York State GIS
Clearinghouse, the Economic Research Service at the U.S. Department of Agriculture, the U.S.
Census Bureau, the U.S. Bureau of Labor Statistics, the U.S. Department of Housing and Urban
Development, and the U.S. Department of Commerce’s annual Zip Code Business Patterns datasets.
These data capture a broad set of the amenities a community offers its residents. A particular
strength of these data is the ability to calculate distances to amenities rather than indicator variables
for the presence of these amenities in a community.
A central construct of this analysis is the distinction between rural and non-rural
communities. There is not one but many commonly used definitions. One definition, rural-urban
continuum codes or Beale codes, separates whole counties into those contained in a Metropolitan
Statistical Area (MSA) and those outside MSA borders. Researchers (such as BLLW) using this
scheme consider the schools in metropolitan counties to be urban and schools in non-metropolitan
counties as rural. While this scheme can be very useful and offer vital insights into differences
between urban and rural schools, it has the drawback of labeling schools in rural communities in
metropolitan counties as urban and schools in urban communities in non-metropolitan counties as
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Miller – Rural Teacher Recruitment
rural. I therefore use the eight-category community-level Johnson Locale Codes released by the
National Center for Education Statistics.1 Generally, urban communities are those labeled as Large
City or Medium-size City; suburban communities are those labeled as Urban Fringe of a Large City, Urban
Fringe of a Medium-Sized City, or Large Town; and rural communities are labeled as Small Town, Rural
outside a Combined Metropolitan Statistical Area, or Rural inside a Combined Metropolitan Statistical Area.
When this classification scheme is applied to New York State public schools, 24 percent of schools
are rural, 40 percent are suburban, and 36 percent are urban.2
Guided by the exploratory work on rural teacher labor markets, I developed measures of the
following seven community amenities:

Geographic isolation: distance to the nearest hub or primary airport3

Professional isolation: distance to the nearest teacher education program4

Access to medical services: distance to the nearest hospital

Availability of shopping: factor of distance-weighted sums of shopping establishments within
the community5

Economic health: distance-weighted average of unemployment rate

Adequate housing: distance-weighted average of fair-market rents for 2-bedroom apartments
within the community
Beginning in 2006, NCES replaced the metric-centric Johnson locale codes with the 12-category urban-centric local
codes.
2 The breakdown for upstate New York (excluding the New York City Metropolitan Statistical Area) is 48 percent rural,
35 percent suburban, and 16 percent urban.
3 Hub airports in New York are: Albany International, Buffalo-Niagara International, Greater Rochester International,
John F. Kennedy International, LaGuardia, and Syracuse Hancock International. Primary airports in New York are:
Binghamton Regional, Chautauqua County/Jamestown, Clinton County, Elmira/Corning Regional, Ithaca/Tompkins
Regional, Long Island MacArthur, Oneida County, Stewart International, and Westchester County.
4 I included the 114 teacher education programs at non-proprietary four-year colleges and universities and graduate only
institutions.
5 Principal component factor analysis was used to calculate the shopping factor. Shopping establishments were measured
at the zip code level and include hardware stores, grocery stores, general merchandise stores, apparel stores, shoe stores,
jewelry stores, drug stores, book stores, sporting goods stores, and restaurants (alpha = 0.9896).
1
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Miller – Rural Teacher Recruitment

Access to familial and social networks: distance from the school to the teacher’s hometown and
most recently attended college or university, respectively
Distances were calculated as straight-line distances in miles using geocoded addresses of the schools
and amenities. Data on the location of shopping establishes only identifies the zip code, meaning I
am unable to identify the distance from the school to nearest shopping establishment. However,
using zip code centroids as a proxy for location, I am able to identify the number of establishments
within a radius around the school. Identifying the number of establishments within a defined area
may partially capture the quality of the shopping amenity to the extent competition increases with
the number of establishments and quality improves with competition. The remaining amenities are
measured at the county-level. These were mapped onto zip codes and then the distant-weighted
averages for school communities were calculated. Based on the research highlighting the localness of
teacher labor markets, I define community as a 20-mile radius around the school or the hometown
(BLLW; Reininger, 2006).6 Amenities were calculated for each school’s community and each
teacher’s hometown community. For communities near New York’s domestic borders, amenities
include those within the 20-mile radius but located in neighboring states.
Table 2 shows that compared to suburban and urban schools, rural schools are farthest from
the nearest airport (30 miles), teacher education program (19 miles), and hospital (9 miles). Rural
school communities also have the fewest shopping venues (1.2 standard deviations below state
mean). Unemployment rates (5.5 percent) are higher than in suburban school communities but
lower than urban school communities. These relatively poor amenities are likely calculated into
monthly rents in rural communities which are the lowest in the state ($627). These patterns hold
with respect to average community amenities in beginning teachers’ hometowns. Elsewhere I find
While the choice of a 20-mile radius was informed by the literature, there is no definitive evidence that this is the
correct distance. However, the use of distance-weighting helps reduce any bias incorrectly specifying the radius may
introduce as amenities located closer to the school receive more weight than amenities located farther away.
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rural teachers are more likely leave schools in rural communities with poorer amenities than school
in communities with richer amenities (Miller, 2011).
(Insert Table 2 about here)
The authors of BLLW graciously provided the geocoded locations of teachers’ hometown
and college. A teacher’s hometown is identified by either the zip code of the teacher’s high school or
of the mailing address they provided when they applied to college. High school information is
known for anyone who took the SAT since 1980. Mailing address information is known for anyone
who applied to any of the 64 institutions of the State University of New York system since 1990.
Salaries and school characteristics are also important determinants of workers labor market
behavior (such as the selection of a first job). Summary statistics of salaries and school
characteristics by community type are provided in Table 3. Rural schools have the lowest starting
salaries. Student poverty rates are higher in rural schools than suburban schools but much lower
than in urban schools. Rural have the smallest student-teacher ratios.
(Insert Table 3 about here)
New York public schools more than doubled the number of beginning teachers hired each
year over this period from 4,240 beginning teachers 1994 to 9,351 in 2002. The analytic sample is
restricted to the 37,997 teachers for whom a hometown is identifiable (approximately 65 percent of
all beginning teachers between 1994 and 2002).7 Nineteen percent of these teachers came from rural
towns, 44 percent from suburban communities, and 37 percent from urban centers. A combination
of teachers’ location preferences and the distribution of open positions across community type
results in more beginning teachers matched to urban schools than suburban schools (38 percent and
44 percent).
7
A hometown is identified for approximately 74 percent of all teachers who began their careers in Upstate New York.
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In their paper, BLLW report the results of exploratory analyses which lead them to believe
excluding teachers without hometown information does not bias their estimates. The same holds
true for my analysis. Furthermore, a much lower percentage of beginning teachers in rural and
suburban schools are missing this data compared to their urban peers (25 versus 44 percent).
Beginning teachers differ in notable ways across community type (Table 4). Rural teachers
are the most likely to be male and the least likely to be non-white. They are also the least likely to
have graduated from the most competitive colleges and less likely than suburban teachers to have a
graduate degree. Rural beginning teachers have the highest average SAT scores (1044 versus 1040
for suburban teachers and 972 for urban teachers). They are also the most likely to teach multiple
subjects (11.7 percent versus 8.5 and 10.6 percent for suburban and urban, respectively).
(Insert Table 4 about here)
3.1
Job Search Model
Observed job matches result from the alignment of two agent’s preferences. The teacher
must want the job and the school administrator must want to hire the teacher. Modeling such a twosided process is very complicated and relies on game theoretic methodologies (see Boyd et al., 2010).
Previous research has therefore tended to maintain the simplifying assumption that all teachers have
access to the same set of jobs. Thus, if all teachers face the same demand for their labor, then the
observed teacher-school matching results solely from teachers’ decisions as to where to supply their
labor. Although useful insights can and have been garnered from these models, such an assumption
is not wholly satisfying given the desire to model the actual job matching process. If all teachers
have the same jobs available to them, there is no variation in job characteristics across teachers
meaning the main effects of job characteristics on job matches can not be tested. A notable
exception is the job search model developed by BLLW, which I use as the basis for my regional
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choice model. It provides a framework for analyzing teacher-school matches as a function of labor
supply decisions and job characteristics.
The BLLW job search model is a model of the choice of geographic region in which to
focus one’s job search activities. It is unlikely teachers apply to schools all across their state when
searching for their first teaching job. Rather, given the wide availability of teaching jobs, it is more
probable that teachers impose some constraints on where they are willing to teach. While teachers
can not unilaterally select any one job, they are free to narrow their search activities to a geographic
area with a realistically high expectation of receiving some job offer assuming a sufficiently large
number of available positions. Maintaining this assumption permits the modeling of the job (region)
match process as one-sided. (I assess the how likely this assumption is to hold in truth later in this
section.)
The validity of results from the BLLW model depends in great deal on how the geographic
regions in the teachers’ choice set are defined. Regional boundaries must be pertinent to teachers’
job search activities and distinct from one another. There are nine labor markets within New York
each centered on a Census-defined Metropolitan Statistical Area (MSA).8 The Office of Management
and Budget defines an MSA as an area with a densely populated core and the adjacent communities
with a high degree of social and economic integration to that core. Restricting the choice set to these
regions within New York state imposes the assumption that teachers only consider within-state
positions. This is appropriate for the vast majority of beginning teachers given the state-specific
nature of teacher licenses.
These nine labor markets diverge slightly from the ten markets identified by the New York State Department of Labor
(NYSDL). In order to keep New York’s portion of the New York-Northern New Jersey-Long Island MSA intact, I
combined two NYSDL-defined labor markets—New York City and Long Island—with three counties from the Hudson
Valley Region—Putnam, Rockland, and Westchester. Similarly, I moved Schoharie county from the Mohawk Valley
Region to the Capitol Region to keep the Albany-Schenectady-Troy MSA intact.
8
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I divide each labor markets into a maximum of three submarkets, where appropriate: urban,
suburban, and rural using the Johnson Locale Codes discussed above (Map 1). These submarkets are
likely important to teachers. First, many teacher education programs have courses of study to
prepare teachers specifically for urban or rural schools. Second, key job characteristics important to
teachers such as salaries, class sizes, and student demographics differ across these three community
types. Urban submarkets are restricted to city districts that service the city centers of the MSAs (e.g.,
Buffalo and Niagara Falls for the Western labor market, Ithaca in the Southern Tier labor market).
Rural submarkets consist of schools in rural communities. Suburban submarkets include all
remaining schools.9 Seven of the nine labor markets contain an urban, suburban, and rural
submarket. The New York City labor market does not have a rural submarket, and the North
County labor market is entirely rural.
(Insert Map 1 about here)
Observed college attendance and job matches of first-time teachers between 1994 and 2002
suggest teachers are most likely to restrict their search activities to specific labor markets. Sixty-two
percent of teachers attend college within their home labor market with 41 percent remaining in their
home labor submarket. When they first enter the job market, 68 percent are matched to a job in the
same labor market as their college. Forty-four percent remain in their college’s labor submarket. The
revealed geographic preferences are strongest when comparing a teacher’s home labor market to the
labor market of their initial job. Eighty-six percent of teachers begin their careers in their home labor
market; 60 percent in their home submarket.
There are several exceptions to how schools were assigned to labor submarkets. In the New York labor market, less
than 2 percent of the schools are rural and are therefore combined with the suburban labor submarket. In the North
Country labor market, less than 5 percent of schools are located in non-rural communities and are therefore considered
rural in this analysis.
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McFadden’s (1973) discrete choice model, also known as conditional logistic regression, is
well-suited for this analysis because the teacher’s complete choice set is known and a teacher’s
choice of region can be modeled as a function of characteristics of each alternative. The data is
organized such that each teacher is linked to each alternative choice (here, the labor submarkets).
Hence, in this application, each beginning teacher has 24 regional submarket-specific observations in
the dataset. This focus on the choice alternatives is the model’s primary distinction from the
multinomial logit model which takes the individual as the unit of analysis and uses individual
characteristics as key explanatory variables (Hoffman & Duncan, 1988). I estimate a mixed logit
discrete choice model which combines the multinomial with the conditional logistic to model
regional choice as a function of both the characteristics of the alternatives and the teacher.
Teachers are assumed to teach in the region that maximizes their utility (or satisfaction).
Each teacher therefore calculates an expected utility from each of the labor submarket options when
choosing a region in which to focus their job search activities. The analytic mission of this job search
model is to allocate each teacher’s probability of accepting a teaching position (i.e., 1) among the
regions.10 Technically, a teacher has some probability of selecting any one of the 24 regions within
New York State. Yet, I am only interested in predicting the probability a teacher selects into one of
the state’s eight rural regions. The only reason I must include the non-rural regions in my analysis is
to estimate the probability of teaching in a non-rural region, Pr(Non-Rural), so I can examine how
their probability of becoming a rural teacher, 1 – Pr(Non-Rural), is distributed across the eight rural
regions. For the purpose of this research, my estimates of the influence of community amenities on
the probability of teaching in one of the eight rural regions does not depend on how the probability
My analytic sample consists only of those beginning teachers who accepted an employment offer. Consequently, all
teachers have a probability equal to 1 of accepting a job offer. I do not have information on beginning teachers who
conducted job search activities yet were not matched to an open position (e.g., they received no offers or they received
no offers that provided more utility than being unemployed or employed in a non-teaching position).
10
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of becoming a non-rural teacher is distributed across 16 non-rural regions. Therefore, I collapse the
24 labor submarkets to nine regions—the eight rural submarkets and one omnibus region that
encompasses the all urban and suburban submarkets. Each teacher appears in the dataset nine times,
once for each of the nine regional alternatives. Within each teacher, the predicted probabilities
across the nine regions sum to 1 as required by the BLLW job search model (equation 1).
(1)
8
Pr( Non  Rural )   Pr(Rural r )  1 , where
r 1
16
Pr( Non  Rural )   Pr( Non  Rural n )
n 1
The goal of this analysis is to estimate and explain influences on Pr(Rural) and that requires
estimating Pr(Non-Rural).
A teacher is assumed to pick the region that maximizes her utility, U mr . When selecting
among regions, job seekers assign value to the benefits they expect to receive from living and
working in that region. Let U mr indicate the utility teacher m receives from teaching in region r
(equation 2). Teacher m will select region r if U mr  U mk for all k ≠r.
(2)
max U mr  X m r  Z mr    mr
r
X m is the case-specific matrix and describes the beginning teachers (or cases) searching for their
first teaching job (i.e., the choices). However since teacher characteristics are constant across the
regions, teacher characteristics are interacted with the eight indicator variables for the regional
choice alternatives (the omnibus non-rural region is the reference region). The alternative-specific
matrix, Z mr , contains measured characteristics of each alternative (i.e., the regions). Region
characteristics include the distance from the region to the teacher’s hometown and undergraduate
institution and comparison of community amenities and student characteristics between the
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teacher’s hometown and the region. All variables in the alternative-specific matrix vary across the
choice alternatives within each teacher.
Assuming the unobserved error term,  mr , from the utility function detailed in equation 2 is
Gumbel-distributed, the probability that teacher m will be initially matched to a school located in
region r is:
(3)
Pr(Regionm = r) =
exp( X m r  Z mr  )
9
 exp( X
r 1
m
 r  Z mr  )
where regions are indexed by r = 1 to 9. The  r and β coefficients can be interpreted as teachers’
revealed preferences over regions and their characteristics in deciding whether or not to teach in a
specific region. Robust standard errors account for the clustering of beginning teacher observations
into subject areas by first-year teacher cohort.
As for the specific variables included in these models, the case-specific matrix, X m , contains
interactions between teacher characteristics and the region alternative indicator variables to allow
teacher preferences over the regions to vary with their characteristics. These preferences are
measured by the  r vector of coefficients. I include gender, age, combined (mean-centered) SAT
scores, hometown community type, ethnicity, competitiveness of the undergraduate institution
attended, and whether or not the teacher has a graduate degree.
The alternative-specific matrix, Z mr , describes the characteristics of each alternative, and the
β coefficients can be interpreted as the premiums teachers place on these characteristics. Again, the
purpose of this study is to examine the probability individual teachers are recruited to rural schools,
Pr(Rural). I therefore interact all the variables in the alternative-specific matrix with an indicator
variable equal to one if the region is rural. This allows me to make statistical inference specifically
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about rural teacher recruitment rather than a weighted average of the rural and non-rural teacher
recruitment.
Similar to BLLW, I also include the following variables in the alternative-specific matrix
detailing how the rural region relates to the teachers hometown and the higher education institution
where the teacher most recently earned a degree (for the vast majority this will be their
undergraduate institution). Distance from the teacher’s hometown to the rural region enters as log
distance and is interacted with two indicator variables equal to one if the teacher’s hometown region
is located in a suburban or urban submarket. Two additional variables indicate (1) whether or not
the teacher’s hometown is located in the rural region or (2) whether or not the teacher’s hometown
is located in the suburban or urban submarket within the rural region’s labor market (also interacted
with an urban hometown indicator variable). Distance between the teacher’s most recently attended
institution of higher education and the rural region also enters as log distance and is interacted with
two indicator variables equal to one if the teacher’s hometown is located in a suburban or urban
submarket. Another variable indicates if this most recent institution is located within the rural
region.
I expand this matrix to include a set of variables describing the rural region’s community
amenities and student characteristics. Teacher valuations of community amenities likely vary
according to the amenities with which they grew up. For example, take two teachers considering a
rural community located 35 miles from the nearest airport. A teacher from Buffalo, a city with an
airport, will likely place a lower value to this amenity than a teacher who grew up in a rural
community 55 miles from the nearest airport. Therefore, community amenities (as well as student
characteristics) enter the analysis as region-to-hometown ratios in order to capture the comparisons
beginning teachers likely make between a potential job’s community and their hometown
community.
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Regional community amenities and student characteristics are based on the set of open
positions within the teacher’s subject area (i.e., any position held by a teacher at a given school who
was not at that same school the previous year). State-issued licenses and certificates qualify teachers
to teach in certain subjects and grade levels. It is reasonable to assume therefore that teachers base
their job offer expectations in a given region on the number of open jobs in their certificated subject
and grade level. I do not observe the subject(s) and grade level(s) for which each teacher is certified
to teach. Therefore, I assign teachers to subject areas based on the subject assignment of their first
job.11 Subject assignments are in the following areas: elementary, humanities, mathematics, science,
special education, fine arts, ESL, occupational education, and other subjects.
My model specification detailed above improves upon that of BLLW in several ways. First,
whereas they delineate 17 regions, three of which are rural, I map 24 mutually-exclusive regions
primarily by separating out the rural communities BLLW include in suburban regions. This then
allows me to estimate a discrete-choice model designed to make statistical inferences about teacher
recruitment in New York’s eight rural regions versus BLLW’s three rural regions. Details of how we
each assign specific counties to labor markets are detailed in Table A-1 in the appendix. Relatedly, I
use community-level rather than county-level data to group schools into urban, suburban, and rural
regions. I expand the observation period by five years to include the period from 1993-1994 to
2001-2002 (1994 to 2002). They estimate the conditional logit form of the McFadden discrete-choice
model rather than a mixed logit form (i.e., they do not estimate the vector  r ). And given current
statistical software, I am able cluster observations within-subject within-year.
Another key difference is how the distances are calculated. BLLW calculated distance from
the teacher’s hometown and college to the nearest district zip code centroid in each region. I
Some teachers have positive full-time equivalency (FTE) status in more than one subject area (elementary is one
subject in the data). I assign them to the subject with FTE>.5.
11
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calculate average distances from a teacher’s hometown and college to all schools in a region with
openings in the teacher’s subject. BLLW’s job search model assumes teachers select a region in
which to focus their job search activities with the reasonably high expectation of receiving a job in
that region. Average distances better reflect their expected distance from home or college. If
teachers have preferences for closer proximity to their hometown and college as BLLW show, we
would predict they would be less likely to choose a region if all the open jobs were on the region’s
far side rather than if they were on the side nearest the teacher’s hometown or college.
3.2
Support for Interpreting Results as Supply-Side Response
Assuming each teacher faces sufficient demand for their labor within each region for
teachers to restrict their job search activities to a given region generates the power of the regional
job search model to measure supply-side response to community amenities. But does the reality of
New York’s teacher labor market support this assumption? A direct measure of a full teacher
demand schedule for each region is not possible with the available data. However, job availability
across subjects and regions and it’s correlation with community amenities in rural regions lend
justification for imposing the sufficient demand assumption.
Teachers should feel more comfortable restricting their job search activities to a region in
times when the number of teachers is expanding and less comfortable when it is shrinking. New
York’s public schools generally increased the number of teachers employed across subjects and
regions between 1994 and 2002. Regressing the number of teachers employed within a given subject
(9 areas) and region (8 rural, 8 suburban, and 8 urban; 24 total) on a time trend shows significant
increases in 65 percent of the subject-region labor forces in rural regions, 69 percent in suburban
regions, and 50 percent in urban areas with almost all the others experiencing no change in size over
the period. Schools significantly decreased their teaching staff in occupational education (8 regions)
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and ESL (2 regions). These labor force trends lend support to the imposition of the sufficient
demand assumption.
Job search activities are likely more influence by the percent of these positions that are open
and for which schools are hiring. Although the data do not indicate which jobs were publicly
advertised to solicit applications from beginning teachers, likely upper and lower bounds on the
number of open positions within subject areas and regions can be calculated from observed teacher
turnover. School-level turnover is a likely upper bound estimate: those positions held by teachers
observed at a given school in the current year who were not at that same school the previous year.
These positions were either newly created or positions vacated by a teacher the previous year. This is
likely to be an overestimate as some of this school-level turnover may be within-district transfers
that do not result in job openings (two 4th grade teachers switching schools, for example).
Depending on district policies, these positions may not have been publicly advertised. Therefore, a
lower bound estimate is district-level turnover: positions held by teachers not employed by the
district the previous year. At an upper bound, job opening averages almost 16 percent of all
positions within a subject area and region and is very rarely below 10 percent. At the lower bound,
job openings average over 11 percent of positions within a subject and region and are very rarely
below 7 percent. The rates are slightly higher in urban regions than in suburban and rural regions
which themselves are very similar. These rates likely send a signal of sufficient demand to teachers.
However, I do conduct sensitivity checks to assess the influence of varying demand across subjects
and regions.
A related threat to the measurement of supply-side response to rural community amenities is
that they vary with the demand for teachers. Evidence shows this is not the case however.
Correlations between amenities and subject-specific job availability rates within each of the 8 rural
regions are low in absolute value (below .2 on average) indicating that the hiring needs of schools in
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regions with poor amenities are not substantively different than the needs in richer amenity regions.
The same is true for student characteristics. Estimated coefficients relating community amenities and
student characteristics to the probability of becoming a rural teacher can be safely assumed to reveal
teacher preferences.
Starting salaries, on the other hand, are highly correlated with a rural region’s hiring needs
and thus are not included in the job search model. The correlation with the share of open positions
in mathematics, the humanities, and other subjects exceeds .5 and is near .4 for elementary, science,
and occupational education teachers. Furthermore, salaries, especially in comparison to community
amenities and student characteristics, can be manipulated by school districts to influence the
quantity of teacher labor supplied by the market. While teacher supply is surely responsive to
salaries, their direct link with demand makes any measured relationship between relative salaries and
regional choice hard to interpret from a teacher labor supply perspective within the current onesided job search model.
With this support for interpreting the coefficients as teacher supply decisions, the results
from this discrete-choice model, discussed in the following section, provide insights into the two key
questions driving this analysis. Who chooses to teach in rural schools? And do schools with
relatively poorer community amenities have a harder time recruiting beginning teachers who grew up
in different types of communities—urban, suburban, or rural?
4.
Results
Rural teacher labor markets are considerably larger than those in non-rural communities.
Beginning teachers from rural regions exhibit weaker attachment to their hometown (and family
networks) when searching for their initial job compared to teachers from urban communities (Table
5). On average, teachers from rural communities are matched to a first job 47 miles from their
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hometown compared to 17 miles for those from urban hometowns.12 Among those from rural areas,
45 percent teach within 15 miles and 70 percent teach within 40 miles. The comparable figures for
teachers from urban areas are 85 and 92 percent. It is not surprising then that 75 percent of teachers
from urban areas remain in their hometown region compared to only 43 percent of teachers from
rural areas (Table 6). Furthermore, 42 percent of teachers from rural regions move to non-rural
regions while only 22 percent of teachers from urban regions move to non-urban regions.
Compared to teachers from urban areas, teachers from rural areas are more likely to move away
from the communities and lifestyle in which they grew up.
(Insert Table 5 about here)
(Insert Table 6 about here)
In commenting on the “draw of home”, BLLW focus on the potentially negative
consequences for urban education. “If, historically, the graduates of urban high schools have not
received adequate education, then the cities face a less-qualified pool of potential teachers” (BLLW,
p. 127). These statistics also raise a potential concern for rural education. If more than 50 percent of
teachers leave their home rural regions and 40 percent leave rural communities altogether, it is
important to understand who is leaving versus staying. If higher quality rural hometown teachers are
more likely to choose to teach in non-rural than rural communities, rural students are placed at a
disadvantage. Additionally, teachers are more likely to leave jobs located farther from their
hometown contributing to lower retention rates in rural schools than non-rural schools (Boyd et al.,
2005b; Miller, 2011).
A cursory analysis finds teachers with stronger academic records are more likely to leave
than those with weaker academic qualifications. A higher percentage of teachers who attended the
This difference is only partially driven by the greater spatial dispersion of rural schools. Rural schools are about 3 miles
from the nearest school whereas urban schools are about half a mile from the nearest school.
12
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most competitive colleges, as identified by Barrons, leave their home rural regions than those who
attended the least competitive colleges (69 versus 60 percent). Teachers with a graduate degree are
also more likely to leave (63 versus 56 percent) as are teachers with SAT scores in the top 25th
percentile than the bottom 25th percentile (62 versus 54 percent).
Turning to the results of the rural regional choice model, I discuss the rural teacher
recruitment problem in terms of the probability teachers with specific observable will select into
rural regions relative to non-rural communities. Next, I reinterpret BLLW draw of home findings as
they pertain to rural teacher labor markets. Finally, I present early findings on the influence of
community amenities and job characteristics on first-time teachers’ decisions to become rural
educators. The findings generally contradict the community amenities hypothesis; however, the
sensitivity of some results to model specification renders these findings, for the moment,
speculative. Table 7 contains the estimated coefficients from the rural regional choice model that
excludes community amenities and job characteristics. I use these results to predict probabilities
teachers with varying characteristics and distances to familial and social networks select into rural
regions.13 Table 8 presents similar coefficients from models that include community amenities and
student characteristics.
(Insert Table 7 about here)
4.1
Teacher Characteristics
Results indicate rural schools have more difficulty recruiting beginning teachers who
graduated of the most or least competitive colleges, hold a graduate degree, and are a member of a
racial or ethnic minority group. As shown in Figure 1, graduates of the most competitive colleges are
31 times less likely than graduates of competitive colleges to become a rural teacher than a non-rural
To test the sensitivity to the identifying assumption teachers have a reasonable assumption of being able to obtain
some job offer in each region, I estimated a model on the reduced sample of teachers with greater job availability. The
pattern of results between the two samples is very similar.
13
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teacher. Challenges recruiting these teachers are particularly pronounced in the two most heavily
rural labor markets (the North Country along the border with Canada and the Southern Tier along
the border with Pennsylvania). On the other hand, graduates of the least competitive colleges are
also less likely to select into rural regions (52 times less likely) than competitive college graduates.
Beginning teachers with a Master’s degree are 18 less likely than teachers without an advanced
degree to select into rural rather than non-rural communities. Minority teachers are 67 times less
likely than white teachers to teach in rural than non-rural schools.
(Insert Figure 1 about here)
Rural schools in four regions—Mid-Hudson (immediately north of New York City), Capitol
(surrounding Albany), Finger Lakes (surrounding Rochester), and Western (surrounding Buffalo)—
have more success recruiting teachers with higher composite SAT scores. The proximity of these
regions to large urban centers may be playing a role. For example, these teacher’s friends and/or
spouses may be attracted to these regions for the higher non-teaching salaries they provide relative
to regions such as the North Country or the Mohawk Valley in the Adirondack Mountains. The
estimated coefficients in these other regions are insignificant. The state-wide relationship between
combined SAT scores and a beginning teacher’s odds of becoming a rural educator is shown in
Figure 2. Compared to a teacher with average scores, a teacher two standard deviations above the
mean is 11 times more likely to becoming a rural teacher.
(Insert Figure 2 about here)
4.2
Geographic Profile
Before turning to the results of the McFadden model of rural regional choice, the reader
should be reminded of a key difference between this model that that specified by BLLW. Here, all
the alternative-specific covariates are either interacted with an indicator variable equal to one if the
region is rural. Therefore, the results presented below pertain specifically to teacher preferences over
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the New York’s eight rural regions. The results from BLLW’s model reflect teacher preferences
when selecting among all regions within New York (rural, suburban, and urban) and show the
preferences of rural hometown teachers for proximity to either their hometown or their most
recently attended university does not differ from that of suburban hometown teachers.
When selecting where to focus their job search activities, teachers who grew up farther away
from a rural region are, unsurprisingly, less likely to choose become a teacher in that rural region
than another region. Rural hometown teachers however reveal a weaker preference for close
proximity to their hometowns and family networks than beginning teachers from suburban or urban
hometowns even accounting for mean differences between hometown community type and whether
or not the teacher’s hometown is located within the same labor market. A ten percent increase in the
average distance of the open jobs to a their hometown is associated with a 8.5 percent decrease in
the odds ratio a rural hometown teacher will choose to teach in that rural region. A similar increase
in the average distance is associated with a 15.9 percent decrease in the odds ratio for suburban
hometown teachers and a 15.4 percent decrease for urban hometown teachers.
The relative likelihood an average teacher (i.e., a teacher with characteristics equal to the full
sample averages provided in Table 3) will become a rural versus non-rural teacher are plotted in
Figure 3. As the distance to the rural region increases, the odds of becoming a rural teacher decrease.
Up until a distance of roughly 35 miles, the results predict an average first-time teacher from a rural
hometown teacher is more likely to become a rural than non-rural teacher. She has a greater
likelihood of selecting in a non-rural community when the rural region is more than 35 miles from
her hometown. The tipping point for the same teacher but from a suburban hometown is about 19
miles and 13 miles for an urban hometown teacher. Rural hometown teachers are more tolerant of
distance when selecting between rural and non-rural regions for their first job.
(Insert Figure 3 about here)
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(Insert Figure 4 about here)
Figure 4 uses the same estimated coefficients to plot the average teacher’s likelihood of
selecting a rural region 5 miles away from her hometown over another rural region farther away.
Non-rural hometown teachers have a much stronger preference for closer rural regions than more
distant rural regions than rural hometown teachers. A suburban hometown teacher is predicted to be
1.9 times as likely to select a rural region 5 miles away as one 20 miles away; 6.5 times as likely as one
60 miles away. However, rural hometown teacher the odds ratio only increases from 1.4 to 2.2 as the
distance of the second rural region increases from 15 to 60 miles.
Teachers also prefer to be closer to the colleges and universities they attended; although the
preference is substantially weaker than preference for proximity to hometown. Here again, the
preference is weaker among rural hometown teachers than among non-rural hometown teachers. A
10 percent increase in the average distance of the open jobs to their most recently attended college
or university is associated with a 4.4 percent decrease in the likelihood a rural hometown teacher will
become a rural teacher, a 6.5 percent decrease for suburban hometown and 6.4 percent decrease for
urban hometown teachers. Figure 5 plots the odds ratio of becoming a rural versus non-rural
teacher, and Figure 6 plots the odds ratio of selecting into a rural region 5 miles away relative to
another rural region further away. They reveal the same patterns as the analogous figures for
distance to hometown (Figures 3 and 4 above).
(Insert Figure 5 about here)
(Insert Figure 6 about here)
Rural schools are more successful recruiting teachers, especially teachers from non-rural
hometowns, when they’ve had prior exposure to or immersion in rural communities. Homegrown
teachers (i.e., those who grew up in the rural region) are almost 5 times more likely to choose their
hometown rural region than another region. Suburban and urban hometown teachers are 1.3 times
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more likely to select the rural region in the home labor market than another region. And beginning
teachers who attended college in the rural region are 50 percent more likely to teach there versus
another region.
4.3
Community Amenities and Student Characteristics
Rural hometown teachers are more likely than take a first job farther from their hometown
and more willing to relocate to a rural region farther away than non-rural hometown teachers. Why
might this be? Do they have greater wanderlust? Or might there be something about rural
communities and schools that lure them away while dissuading job candidates who grew up in nonrural communities? Below, I summarize what my findings regarding the influence of community
amenities and student characteristics on beginning teachers’ rural regional choice.
According to the community amenity hypothesis, rural schools have particular difficulty
recruiting teachers because of the set of amenities their community has to offer is viewed less
favorably that those offered by other communities. Combined with substantially lower salaries and
high poverty rates, rural schools are at a substantial disadvantage in the marketplace for teachers,
particularly high quality teachers. I test the community amenities hypothesis by adding a set of
variables to the alternative-specific matrix that compare the amenities in the rural region to those in
the teachers’ hometown (rural region/hometown ratio). These ratios allow for teachers who grew up
in communities with different amounts of amenities to place different values on those offered by a
rural region.14
(Insert Table 8 about here)
Another option would have been to include a teacher’s hometown amenities in the case-specific matrix to test their
influence on a teacher’s choice of an individual rural region versus another rural region. The results of these models
indicate this influence varies across the eight rural regions presumably due to the fact the amenity set available offered
by the rural region varies across regions. The region-to-hometown ratios test this directly and more parsimoniously.
14
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Community amenities are significant predictors of a beginning teacher’s likelihood of
becoming a rural versus non-rural teacher (see Table 8 for complete results). Furthermore, beginning
teacher preferences over community amenities explain away group differences across hometown
community type in their preferences for proximity to their hometown. Holding community
amenities and student characteristics constant, a 10 percent increase in the distance decreases rural
and non-rural hometown teachers’ odds of becoming a rural teacher by 13 percent. Non-rural
teachers are more willing than previously to relocate greater distances if they can access the same set
of amenities and rural teachers are less willing to move farther from their hometowns. Other
findings pertaining to preferences over proximity to the most recently attended college or university
and preference of specific types of teachers for becoming rural teachers remain unchanged. And
while teacher preferences over amenities and student characteristics generally confirm the
community amenity hypothesis, there are some notable contradictions.
The community amenity hypothesis asserts teachers prefer jobs in communities with
relatively richer amenities to those in poorer amenity communities. Thus, it predicts negative
relationships between the probably of becoming a rural teacher and the following ratios: (1) distance
to the nearest hub airport—a measure of the region’s isolation, (2) distance to the nearest teacher
education program—a proxy for the region’s professional isolation, (3) distance to the nearest
hospital—reflecting access to medical care, (4) unemployment rate, (5) percent of students eligible
for free or reduced-price lunch, and (6) student-teacher ratios. The predictions assume teachers
prefer lower values for each of these amenities. For example, assuming teachers prefer to live in
communities with low unemployment rates (and the benefits with which they are correlated),
teachers are less likely to select a rural region as it’s unemployment rate increases relative to that in
the teacher’s hometown. Similarly, teachers are less likely to narrow their search activities to a rural
region as the available jobs in that region are located farther from an airport relative to their
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hometown’s proximity to the nearest airport. Conversely, the hypothesis predicts a positive
relationship for availability of shopping assuming teachers prefer greater to fewer shopping
opportunities. The prediction for percent minority student is less clear given the model includes
student poverty measures of community economic health which may be correlated with
concentrations of non-white populations.
Predicting the relationship for median rents for two-bedroom apartments (a measure of
affordable housing) is tricky. Assuming teachers prefer lower and more affordable rents, the
hypothesis predicts a negative relationship (teachers less likely to prefer regions with relatively higher
rents). However, rents are a function of the demand for and supply of rental units. Demand for
rental units in turn is partly a function of the market value of the amenities available in the local
community. This positive correlation predicts a positive relationship with the probability of
becoming a rural teacher. To the extent that the variation in the amenities I measure capture the
variation in the amenity set factored into rents, higher region-to-hometown rents will negatively
predict becoming a rural teacher.
The community amenities hypothesis is generally confirmed by the results, particularly for
teachers from rural and suburban hometowns. Confirming the community amenity hypothesis, the
average beginning teacher is revealed to prefer rural communities located closer to a teacher
education program, with more shopping venues, and smaller class sizes. Beginning teachers also
prefer rural regions with higher concentrations of minority students than their hometown.
Beginning teachers are revealed to have a preference for rural regions with relatively more
expensive monthly rents. This is most likely picking up teacher preferences for unmeasured
positively correlated amenities as the preference grows substantially when all other amenities are
removed from the model. And the other amenity results are robust to whether or not rents are
included in the model.
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It may be that preferences over the community amenities and job characteristics available in
rural regions vary across hometown community type. For example, it could be that suburban
hometown teachers need more shopping opportunities to entice them to teach in a rural school than
is needed by a rural hometown teacher. I therefore estimated the model in presented in Table 8
separately for rural, suburban, and urban hometown teachers.
Parameterizing the model by hometown community type serves another purpose. For some
of the community amenity ratios, the region of common support for the distributions across
hometown community type is rather smaller resulting in collinearity of specific value ranges with
hometown community type. Given that hometown amenities are highly associated with hometown
community type (as shown in Tables 2), the coefficients for the amenity ratios could be estimated
off the fact urban and suburban hometown teachers are less likely to teach in rural schools rather
than estimated off the variation in the amenity itself.15
The influence of community amenities and students characteristics in rural regions on a
beginning teacher’s probability of becoming a rural teacher varies significantly across hometown
community type (Table 8 and Figure 7). While all teachers prefer greater over fewer shopping
amenities, the preference among rural hometown teachers is only half as pronounced as that among
non-rural hometown teachers. All teachers also prefer rural communities that are relatively less
professionally isolated; however, the coefficients for distance to nearest teacher education program
are only significant for teachers from non-rural hometowns. Candidates from rural towns prefer
rural regions closer to airports relative to their hometowns (confirming the hypothesis) whereas
teachers from urban hometowns prefer relatively more isolated rural regions (contradicting the
hypothesis). Similarly with respect to the unemployment rate, rural teachers reveal a preference for
Interacting the ratios with indicator variables for suburban and urban hometowns is another technique to address this
situation. Models by hometown community type provide a more flexible correction for collinearity between amenity
values and community type.
15
30
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Miller – Rural Teacher Recruitment
relatively lower unemployment rates (confirming) while urban teachers are more likely to teach in
rural regions with relatively higher unemployment rates (contradicting). Finally, the preference for
relatively higher rents is only present among non-rural hometown teachers. There is no preference
detected for rural hometown teachers.
(Insert Figure 7 about here)
4.4
Raising the Bar for Sufficient Demand
Again, this job search model is predicated on a teacher facing a sufficiently large number of
job openings in order to form a reasonably high expectation of being offered at least one job in the
region chosen. It is not possible to determine whether or not the reality of the New York teacher
labor market satisfies this assumption. However, sensitivity to this assumption can be assessed by
restricting the sample to beginning teachers in subjects with relatively more job openings. Model
results estimated on a sample excluding ESL, fine art, occupational education, and other subject
teachers are presented in Table 9. These are the subject areas with the smallest number of job
openings. Revealed teacher preferences over community amenities and student characteristics are
very robust for rural and suburban hometown teachers. However, preferences for teaching in rural
regions among urban hometown teachers of subjects with more job openings are not related either
shopping venues or monthly rents.
(Insert Table 9 about here.)
5.
Discussion and Policy Implications
Relatively poorer community amenities contribute to rural schools’ recruitment challenges.
However, enriching the amenities a community offers is not easily accomplished and often
undesirable. New York does not need more hub airports. And rural population density cannot
support the number of shopping venues present in more populous regions. Effective policies will
31
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Miller – Rural Teacher Recruitment
therefore be those which seek to diminish the negative influence and enhance the positive influence
of rural community amenities.
Professional isolation, measured by the distance to the nearest teacher education program, is
associated with a lower likelihood of becoming a rural teacher. Enhanced connections between
teacher education programs and rural schools may help lessen teachers’ sense of professional
isolation. These connections could be strengthened through the development of rural teacher
education programs similar to those common for preparing teachers for working in urban schools.
Facilitating and increasing the number of student teaching placements in rural schools may also be
beneficial. Psychological barriers, created by misconceptions of working and living in rural
communities, are likely the source of some reluctance to becoming a rural teacher. As the results
show, increased exposure to rural communities increases the likelihood of becoming a rural teacher.
Teacher education programs can help provide that exposure and diminish those psychological
barriers.
To the extent barriers remain despite increased exposure and knowledge of rural schools and
communities, assignment incentives can provide the additional enticement needed to recruit
teachers. These programs provide incentives to teachers to accept positions in hard-to-staff schools
and subjects. Salary bonuses and loan forgiveness are common incentive programs at both the
federal and state level with some states providing housing benefits or tuition coverage. Assuming
these programs succeed in increasing the supply of teachers to rural schools, they should target
teachers with stronger academic backgrounds who agree to teach in rural schools. The incentives
should be restricted to or be larger for teachers who agree to teach in rural communities with the set
of amenities correlated with recruitment challenges.
Efforts to expand broadband internet access to rural communities hold the promise of
lessening the negative influence of the relatively poor amenities available in rural communities. The
32
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Miller – Rural Teacher Recruitment
internet has dramatically altered how we conduct business, shop for and purchase goods and
services, acquire knowledge of local and world events, and connect to friends and family. Broadband
internet can increase access to shopping venues and decrease the teachers’ sense of geographic
isolation (as measured by distance to the nearest airport). It can also provide teachers with more
instructional resources and enhance job satisfaction. Connections to teacher education program and
teachers in other communities can be strengthened, combating the sense of professional isolation.
Rural communities and residents can also harness the potential of the enhanced connections to
other communities to create new employment opportunities leading to improved economic wellbeing. In doing so, broadband internet would lower the unemployment rate and student poverty
rates; higher rates of both discourage candidates from becoming rural teachers.
Rural communities can benefit from a public relations campaign emphasizing the benefits of
the rural lifestyle, the opportunities for success and happiness it provides individuals, and its
importance to the health of the country. Such a campaign could help reverse population loses and
breathe new life into struggling communities. This public awareness campaign would emphasize the
amenities, not measured here, which are more abundant in rural communities. The amenities
included in this analysis tend to favor non-rural communities.
There is clearly something about rural communities that teachers find attractive, something I
likely have not measured here. Almost 40 percent of all rural beginning teachers grew up in nonrural communities. Rural teaching jobs are not jobs of last resort for all these teachers. Rural regions
offer less of the amenities included in this analysis, putting them at a competitive disadvantage for
teachers’ labor. Yet, there are amenities on which rural communities have an edge. Interviews
conducted for the W.K. Kellogg Foundation (2001) found Americans view rural communities as
more tight-knit with a greater commitment to community and a stronger sense of family than in
suburban and urban areas. Physical amenities such as outdoor recreation, landscape, and scenery as
33
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Miller – Rural Teacher Recruitment
well as social amenities such as a slower pace of life and lower crime rates are cited by migrants as
reasons why they move to rural communities (Rudzitis, 1999; Leistritz et al., 2000). Teachers, when
determining the value they would receive from teaching in a rural school, seek to maximize exposure
to these positive amenities and minimize the costs imposed by poorer amenities such as those
examined in this analysis. Additional research, which incorporates these positive attributes of rural
schools and their communities, can further unpack how amenities influence teacher’s decisions to
teach in rural schools.
6.
CONCLUSION
Borrowing the approach of Boyd, Lankford, Loeb, and Wyckoff (2005a), this study takes the
complex two-sided job-matching process where the teacher must want the job and the school must
want to hire the teacher and simplifies it by imposing several assumptions in order to analyze it from
a one-sided (supply) prospective. Those two assumptions are (1) teachers confine their job search
activities in specific geographic regions within the state and (2) teachers are free to unilaterally select
a geographic region given sufficient job availability in that region. It adapts their approach in order
to highlight teacher preferences for teaching in rural versus non-rural schools in order to quantify
the rural recruitment problem.
Echoing their findings, my results demonstrate that greater distance from their hometown
and the familial networks likely based there is a strong deterrent for teachers choosing to teach in a
rural community. Distance is a greater barrier to recruiting to rural schools teachers who grew up in
non-rural communities. Previous exposure to and immersion in a rural region either by attending
college there or by growing up in the same labor market increases a beginning teacher’s likelihood of
choosing a rural region over a non-rural region for their first job.
My results confirm that rural schools have difficulty recruiting beginning teachers who have
stronger academic backgrounds (graduated from the most competitive colleges and earned an
34
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Miller – Rural Teacher Recruitment
advanced degree). Not only are these teachers less likely to become rural teachers, but these teachers
who grew up in rural communities are also the most likely to relocate to non-rural regions. Rural
schools are losing many of their community’s best educated young adults who choose to become
teachers.
Community amenities do influence a beginning teacher’s probability of selecting into a rural
region as the community amenities hypothesis claims. Rural communities’ efforts to recruit teachers
are hamstrung the more professional isolated they are and the fewer shopping venues they support.
They also struggle to attract teachers from rural hometowns, who are most inclined to become rural
teachers, the more geographically isolated they are and the more they struggling to maintain healthy
local economies.
Together these results provide educators and policy makers with much needed information
particular to rural schools and the unique difficulties they face in recruiting teachers. The insights
contained herein can be useful in the design of policies and programs to assist rural schools. As is
true with any policy, success requires correctly identifying both where the problem is and the
solution to solve that problem. My analysis speaks to the first requirement. New York with its
sizeable population of rural schools, many in close proximity to non-rural schools with whom they
compete for teachers, provides a powerful case study. Insights gained from New York are likely
applicable to other states with similar concentration and juxtaposition of rural and non-rural schools.
However, additional research is needed in states generally considered rural such as Alaska, Maine,
and Nebraska to begin to assess the generalizability of New York’s rural teacher recruitment
problem.
35
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Miller – Rural Teacher Recruitment
Humanities
Mathematics
Science
Special Education
Fine Arts
English as Second
Language (ESL)
Occupational
Education
Other Subject
Areas
Overall
Elementary
TABLE 1. Teacher Recruitment Needs Measured by Full-Time Equivalents by Subject and Job
Community Type, 1994 to 2002
13.5
14.3
22.3
17.2
17.0
13.8
13.9
20.6
16.4
16.4
13.5
14.5
22.4
16.1
17.1
20.6
17.7
18.5
19.0
18.5
12.7
13.2
19.6
15.0
15.1
22.8
21.4
22.1
22.3
22.0
12.8
13.3
16.6
13.7
14.2
15.5
15.9
19.5
17.8
17.4
13.2
14.3
19.0
16.5
16.1
23.0
21.7
20.9
17.7
21.5
25.9
25.5
29.8
23.1
27.6
20.2
15.3
16.6
16.8
16.7
17.4
16.1
20.2
15.4
18.0
18.9
11.1
18.3
16.5
17.4
20.9
18.0
15.8
14.7
17.8
17.6
14.4
16.9
12.3
16.1
22.2
20.0
23.0
17.9
21.9
New Hires as % of All Teachers
Rural
Suburban
Urban
Urban Minus NYC
OVERALL
10.4
12.7
17.5
15.3
14.3
Beginning Teachers as % of New Hires
Rural
Suburban
Urban
Urban Minus NYC
OVERALL
23.3
22.3
27.4
20.2
25.1
25.2
22.2
24.4
18.4
23.8
NOTE: All statistics are based on the summation of the full-time equivalency status of teachers Teachers working part-time are not included in these
calculations
TABLE 2. Teachers’ First Job and Hometown Community Amenities by Community Type, 1994 to
2002
Rural
Distance (in miles) to nearest…
30.0
Airport
Teacher Education Program
Hospital
Shopping (z-score)
Unemployment Rate (%)
2-Bedroom Rent ($)
(18.5)
18.9
(11.8)
8.9
(5.7)
-1.23
(0.38)
5.5
(1.4)
627
(106)
First Job Community Type
Suburban
Urban
Overall
12.7
(8.7)
6.5
(6.0)
3.5
(3.1)
-0.10
(0.62)
4.8
(0.9)
957
(271)
7.0
(4.8)
2.2
(2.9)
1.1
(0.9)
0.89
(0.67)
6.2
(1.3)
964
(168)
14.7
(14.1)
7.9
(9.5)
3.9
(4.5)
0.00
(1.00)
5.5
(1.4)
882
(250)
Hometown Community Type
Rural
Suburban
Urban
Overall
27.5
(18.8)
16.6
(11.5)
8.0
(5.1)
-0.70
(0.45)
5.2
(1.3)
639
(111)
12.8
(7.7)
6.1
(5.6)
3.4
(3.0)
0.39
(0.61)
4.6
(1.0)
1,033
(251)
7.5
(4.9)
2.9
(3.1)
1.5
(1.1)
1.16
(0.71)
5.8
(1.3)
932
(179)
13.7
(12.4)
7.0
(8.2)
3.6
(3.8)
0.46
(0.91)
5.2
(1.3)
921
(250)
NOTE: Standard deviations in parentheses
36
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Miller – Rural Teacher Recruitment
TABLE 3. Average Job Characteristics by Job Community Type, 1994 to 2002
Salaries
Starting Salaries—BA (in 2004$s)
Starting Salaries—MA (in 2004$s)
School Characteristics
Percent Minority
Percent Eligible for Free/Reduced-Price
Lunch
Student-Teacher Ratio
Rural
Suburban
Urban
Overall
33,202
(3,100)
36,162
(3,757)
37,236
(4,350)
42,449
(5,996)
34,656
(1,914)
38,156
(3,399)
35,097
(3,280)
39,438
(5,209)
5.0
(8.2)
30.3
(16.1)
14.8
(3.1)
20.6
(25.0)
19.9
(21.7)
15.5
(5.0)
69.8
(27.2)
66.3
(26.9)
15.8
(7.4)
34.5
(35.7)
39.3
(30.5)
15.4
(5.6)
NOTE: Standard deviations in parentheses
TABLE 4. Descriptive Statistics on Beginning Teachers by Community Type, 1994 to 2002
Hometown
First Job
Full
Sample
Rural
Suburban
Urban
Rural
Suburban
Urban
N
37,997
7,249
16,811
13,937
6,973
14,270
16,754
%
100.0
19.1
44.2
36.7
18.4
37.6
44.1
Female (%)
74.0
70.5
73.2
76.8
69.5
72.5
77.1
Age (years)
26.5
(3.7)
26.4
(3.7)
26.5
(3.7)
26.5
(3.7)
26.6
(3.8)
26.5
(3.7)
26.5
(3.6)
Age < 30 (%)
83.0
83.7
83.1
82.7
82.6
83.5
82.9
Racial/Ethnic Minority (%)
17.9
3.0
8.1
37.4
2.4
6.6
33.9
Composite SAT score
1010
(166)
1044
(142)
1033
(156)
966
(177)
1044
(142)
1040
(150)
972
(178)
9.0
13.0
8.6
7.5
12.7
8.8
7.7
12.1
3.9
10.9
17.8
3.4
9.7
17.8
74.3
82.6
74.4
69.8
84.3
75.8
68.8
13.6
13.5
14.7
12.4
12.3
14.5
13.5
5.3
4.6
5.8
5.2
4.5
5.4
5.6
26.4
24.9
31.3
21.4
24.8
32.5
22.0
Missing SAT Scores (%)
Most Competitive College
Graduate (%)
Competitive College
Graduate (%)
Least Competitive College
Graduate (%)
College Unknown (%)
Graduate Degree (%)
NOTE: Standard deviations appear in parentheses All average teacher characteristics (except the two age variables) differ significantly (p < 05) across
hometown community type and across first job community type
37
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Miller – Rural Teacher Recruitment
TABLE 5. Distribution of Distance from Hometown and Undergraduate Institution to Job by
Community Type, 1994 to 2002
Average
% within
% within
% within
% within
(miles)
5 miles
15 miles
25 miles
40 miles
Distance between Hometown and First Teaching Job
By Hometown Community Type
Rural
46.8
18.6
45.1
59.8
70.1
Suburban
26.3
24.6
56.3
75.3
86.2
Urban
17.3
53.5
84.6
89.3
91.8
OVERALL
26.9
34.0
64.5
77.5
85.2
By Job Community Type
Rural
43.8
17.0
43.2
59.0
70.1
Suburban
26.1
32.8
65.9
79.0
86.5
Urban
20.5
42.2
72.3
83.9
90.3
OVERALL
26.9
34.0
64.5
77.5
85.2
Distance between Undergraduate Institution and First Teaching Job
By Hometown Community Type
Rural
74.2
5.9
20.3
33.0
48.6
Suburban
76.9
10.7
35.3
48.4
59.1
Urban
55.0
28.3
60.4
68.2
72.7
OVERALL
68.3
16.2
41.6
52.7
62.1
By Job Community Type
Rural
70.9
2.4
15.4
30.1
47.6
Suburban
73.7
8.5
34.6
47.1
59.6
Urban
62.8
28.7
58.8
67.1
70.2
OVERALL
68.5
16.3
41.8
52.8
62.1
% within
60 miles
77.0
91.1
93.4
89.3
77.7
89.8
93.6
89.3
61.5
66.5
76.9
69.4
62.0
67.4
74.2
69.5
TABLE 6. Hometown to First Job Mobility, 1994 to 2002
Teachers’ Hometown
Community Type
Rural
Suburban
Urban
OVERALL
Remain in Hometown Labor Market
% Stay in Hometown
% Move Other
Region
Region
42.6
24.7
54.9
33.3
75.2
16.9
60.0
25.6
Move to Other Labor Market
% Move to Region of
% Move to Region of
Same Type
Other Type
15.4
17.3
4.6
7.2
2.9
5.1
6.0
8.3
38
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Miller – Rural Teacher Recruitment
TABLE 7. Estimated Coefficients from Discrete-Choice Model of Regional Choice, 1994 to 2002
MidHudson
North
Mohawk
Southern
Central
Country
Valley
Tier
Alternative-Specific Variables
All variables interacted with an indicator variable equal to 1 if region is rural
Distance from Familial and Social Networks
ln(distance to
-0.846***
hometown)
(0.089)
* Suburban
-0.745***
hometown
(0.140)
* Urban
-0.670***
hometown
(0.197)
Region is home
1.753***
(0.109)
Hometown part of
0.820***
region’s labor market
(0.128)
* Urban
0.004
hometown
(0.240)
ln(distance to recent
-0.442***
college or univ.)
(0.051)
* Suburban
-0.209***
hometown
(0.061)
* Urban
-0.195*
hometown
(0.088)
0.406***
Graduated from
college in region
(0.064)
Case-Specific Variables
Female
-0.113
-0.257**
-0.003
-0.340**
-0.142
-0.059
(0.103)
(0.098)
(0.084)
(0.129)
(0.103)
(0.083)
Racial/Ethnic
-1.120*** -0.840*** -0.697**
-1.660*** -1.849*** -1.136***
Minority
(0.179)
(0.215)
(0.230)
(0.418)
(0.469)
(0.220)
Age < 30
-0.315**
-0.087
0.013
-0.120
-0.190
-0.085
(0.117)
(0.128)
(0.103)
(0.103)
(0.109)
(0.110)
Comp. SAT Score
0.001**
0.001*
0.000
0.000
0.000
0.000
(mean-centered)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
(0.000)
Most Competitive
-0.437**
-0.546*
-0.801*** -0.735*** -0.500*
-1.233***
College Graduate
(0.142)
(0.242)
(0.200)
(0.211)
(0.226)
(0.284)
Least Competitive
-0.381*
-0.212
-0.692*** -0.246
-0.399**
-0.562***
College Graduate
(0.153)
(0.131)
(0.171)
(0.170)
(0.148)
(0.115)
Graduate Degree
-0.389*** -0.015
0.141*
-0.170
-0.192
-0.320***
Holder
(0.103)
(0.103)
(0.071)
(0.104)
(0.109)
(0.095)
Suburban Hometown 2.634*** 2.745*** 2.749*** 2.899*** 3.192*** 2.854***
(0.557)
(0.638)
(0.700)
(0.630)
(0.590)
(0.634)
Urban Hometown
2.020**
1.738*
1.976*
2.132**
2.294**
2.042*
(0.763)
(0.843)
(0.966)
(0.827)
(0.763)
(0.868)
Constant
3.230*** 3.280*** 4.030*** 3.513*** 2.786*** 4.055***
(0.422)
(0.451)
(0.481)
(0.419)
(0.404)
(0.422)
Log Likelihood
-19,088.7
Observations
Capitol
Finger
Lakes
-0.163
(0.097)
-1.674***
(0.261)
0.002
(0.108)
0.001**
(0.000)
-0.792***
(0.211)
-0.220
(0.116)
-0.422***
(0.104)
3.417***
(0.612)
2.476**
(0.804)
2.996***
(0.394)
341,144
Western
-0.096
(0.089)
-1.009***
(0.284)
-0.026
(0.103)
0.001**
(0.000)
-0.881**
(0.272)
-0.071
(0.148)
-0.255*
(0.104)
3.242***
(0.635)
2.358**
(0.839)
2.933***
(0.439)
*** p<0 001, ** p<0 01, * p<0 05
Robust standard errors in parentheses
39
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Miller – Rural Teacher Recruitment
TABLE 8. Estimated Coefficients on Alternative-Specific Variables from Discrete-Choice Model of
Regional Choice Accounting for Community Amenities and Student Characteristics by Hometown
Community Type, 1994 to 2002
Pooled
Distance to Familial and Social Networks
ln(distance from hometown)
-1.274***
(0.072)
* Suburban hometown
0.018
(0.125)
* Urban hometown
0.042
(0.169)
Region is home
1.438***
(0.084)
Hometown part of region’s labor
0.777***
market
(0.100)
* Urban hometown
0.037
(0.197)
ln(distance from most recently
-0.389***
attended college or university)
(0.049)
* Suburban hometown
-0.189**
(0.058)
* Urban hometown
-0.156
(0.082)
Graduated from college in region
0.465***
(0.059)
Community Amenities (Region-to-hometown ratios)
ln(distance to nearest airport)
-0.055
(0.031)
ln(distance to nearest hospital)
0.029
(0.016)
ln(distance to nearest teacher
-0.081***
education program)
(0.019)
Shopping
1.234***
(0.179)
Unemployment Rate
0.197
(0.176)
2-Bedroom Rents
1.210***
(0.161)
Job Characteristics (Region-to-hometown ratios)
Percent Minority
0.441***
(0.048)
-0.202
Percent Eligible for Free/ReducedPrice Lunch
(0.125)
Student-Teacher Ratio
-1.233**
(0.423)
Log Likelihood
-18,175.3
Observations
340,109
Hometown Community Type
Rural
Suburban
Urban
-1.286***
(0.076)
-1.267***
(0.092)
-1.042***
(0.162)
0.755***
(0.101)
0.957***
(0.196)
-0.418***
(0.060)
-0.551***
(0.069)
-0.472***
(0.112)
0.379***
(0.082)
0.547***
(0.104)
0.598***
(0.156)
-0.228***
(0.049)
0.059
(0.031)
-0.023
(0.029)
0.882***
(0.187)
-0.568**
(0.192)
0.239
(0.278)
-0.089
(0.053)
0.034
(0.034)
-0.090*
(0.039)
1.812***
(0.388)
0.331
(0.297)
1.539***
(0.220)
0.102**
(0.036)
-0.076
(0.055)
-0.079*
(0.034)
1.752*
(0.866)
1.219***
(0.321)
0.914*
(0.409)
0.450***
(0.053)
-0.560***
(0.141)
-0.582
(0.377)
-8,303.4
64,655
0.255***
(0.073)
0.034
(0.169)
-3.307***
(0.676)
-6,245.3
151,041
0.566***
(0.169)
-0.643**
(0.249)
-0.109
(1.120)
-3,457.2
124,620
1.390***
(0.086)
*** p<0 001, ** p<0 01, * p<0 05 Robust standard errors in parentheses
40
CEPWC Working Paper Series No. 2. October 2012.
Available at http://curry.virginia.edu/research/centers/cepwc/publications.
Curry School of Education | Frank Batten School of Leadership and Public Policy | University of Virginia
Miller – Rural Teacher Recruitment
TABLE 9. Estimated Coefficients on Alternative-Specific Variables from Discrete-Choice Model of
Regional Choice for Teachers of Subjects with Greater Job Availability Accounting for Community
Amenities and Student Characteristics by Hometown Community Type, 1994 to 2002
Pooled
Hometown Community Type
Rural
Suburban
Urban
Distance to Familial and Social Networks
ln(distance from hometown)
-1.262***
-1.268***
(0.085)
(0.089)
* Suburban hometown
0.029
(0.148)
* Urban hometown
0.245
(0.180)
Region is home
1.458***
1.412***
(0.095)
(0.098)
Hometown part of region’s labor
0.849***
market
(0.111)
* Urban hometown
0.136
(0.215)
ln(distance from most recently
-0.427***
-0.459***
attended college or university)
(0.055)
(0.070)
* Suburban hometown
-0.191**
(0.064)
* Urban hometown
-0.245**
(0.091)
Graduated from college in region
0.467***
0.381***
(0.065)
(0.094)
Community Amenities (Region-to-hometown ratios)
ln(distance to nearest airport)
-0.075*
-0.268***
(0.034)
(0.051)
ln(distance to nearest hospital)
0.015
0.028
(0.017)
(0.034)
ln(distance to nearest teacher
-0.087***
-0.015
education program)
(0.020)
(0.031)
Shopping
1.125***
0.701***
(0.202)
(0.204)
Unemployment Rate
0.286
-0.541*
(0.198)
(0.222)
2-Bedroom Rents
0.884***
-0.194
(0.160)
(0.310)
Job Characteristics (Region-to-hometown ratios)
Percent Minority
0.518***
0.529***
(0.051)
(0.055)
Percent Eligible for Free/Reduced-0.325*
-0.731***
Price Lunch
(0.134)
(0.159)
Student-Teacher Ratio
-1.110*
-0.499
(0.487)
(0.446)
Log Likelihood
-15,125.5
-6,851.1
Observations
294,552
54,297
*** p<0.001, ** p<0.01, * p<0.05 Robust standard errors in parentheses.
-1.243***
(0.106)
-0.855***
(0.177)
0.835***
(0.111)
1.082***
(0.218)
-0.645***
(0.073)
-0.475***
(0.128)
0.442***
(0.109)
0.822***
(0.173)
-0.091
(0.057)
0.039
(0.036)
-0.113**
(0.042)
1.991***
(0.431)
0.323
(0.336)
1.211***
(0.229)
0.089*
(0.041)
-0.091
(0.059)
-0.093*
(0.037)
1.881
(0.989)
1.417***
(0.343)
0.573
(0.445)
0.284***
(0.081)
-0.006
(0.188)
-3.410***
(0.744)
-5,193.1
131,130
0.707***
(0.192)
-1.061***
(0.254)
0.331
(0.882)
-2,917.4
109,129
41
CEPWC Working Paper Series No. 2. October 2012.
Available at http://curry.virginia.edu/research/centers/cepwc/publications.
Curry School of Education | Frank Batten School of Leadership and Public Policy | University of Virginia
Miller – Rural Teacher Recruitment
FIGURE 1. Likelihood of Becoming a Rural Teacher Relative to a Non-Rural Teacher by Teacher
Characteristic
1 - Odds Ratio (P1/P2)
0.0
-0.2
-0.10
-0.12
-0.18
-0.31
-0.4
-0.52
-0.6
-0.67
-0.8
Female
relative to
Male
Minority
relative to
White
Age < 30
relative to
Age >= 30
Most
Competitive
relative to
Competitive
Least
Competitive
relative to
Competitive
Graduate
relative to
Bachelor
Degree
SOURCE: Estimated Coefficients in Table 7
FIGURE 2. Likelihood of Becoming a Rural Teacher Relative to a Non-Rural Teacher by Composite
SAT Scores Relative to the Average Composite SAT Score
1 - Odds Ratio (P1/P2)
0.4
0.28
0.3
0.18
0.2
0.08
0.1
0.0
-0.1
-0.14
-0.2
-0.07
-0.20
-0.3
3 SD
Below
Mean
2 SD
Below
Mean
1 SD
Below
Mean
1 SD
Above
Mean
2 SD
Above
Mean
3 SD
Above
Mean
SOURCE: Estimated Coefficients in Table 7
42
CEPWC Working Paper Series No. 2. October 2012.
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Curry School of Education | Frank Batten School of Leadership and Public Policy | University of Virginia
Miller – Rural Teacher Recruitment
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Curry School of Education | Frank Batten School of Leadership and Public Policy | University of Virginia
Miller – Rural Teacher Recruitment
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CEPWC Working Paper Series No. 2. October 2012.
Available at http://curry.virginia.edu/research/centers/cepwc/publications.
Curry School of Education | Frank Batten School of Leadership and Public Policy | University of Virginia
Miller – Rural Teacher Recruitment
APPENDIX
TABLE A-1. Labor Markets and Submarkets of New York State
Labor
Market
Submarkets
Urban School
Districts
Counties
MSA Included
New
York City
 Urban
 Suburban
New York
City
 New York’s
share of the
NY-Northern
NJ-Long Island
MSA
Hudson
Valley
 Urban
 Suburban
 Rural
Capitol
 Urban
 Suburban
 Rural
Newburgh,
Kingston,
Middletown,
and
Poughkeepsie
Albany, Glens
Falls,
Schenectady,
and Troy
Bronx, Kings,
Nassau, New York,
Putnam, Queens,
Richmond,
Rockland, Suffolk,
Westchester
Dutchess, Orange,
Sullivan, Ulster
 Glens Falls
 AlbanySchenectadyTroy
North
Country
 Rural
None
Mohawk
Valley









Urban
Suburban
Rural
Urban
Suburban
Rural
Urban
Suburban
Rural
Rome and
Utica
Albany, Columbia,
Greene, Rensselaer,
Saratoga,
Schenectady,
Schoharie, Warren,
Washington
Clinton, Essex,
Franklin, Hamilton,
Jefferson, Lewis, St.
Lawrence
Fulton, Herkimer,
Montgomery,
Oneida, Otsego
Cayuga, Cortland,
Madison,
Onondaga, Oswego
Broome, Chemung,
Chenango,
Delaware, Schuyler,
Steuben, Tioga,
Tompkins
Finger
Lakes
 Urban
 Suburban
 Rural
Rochester
 Rochester
Western
 Urban
 Suburban
 Rural
Buffalo and
Niagara Falls
Genesee,
Livingston,
Monroe, Ontario,
Orleans, Seneca,
Wayne, Wyoming,
Yates
Allegany,
Cattaraugus,
Chautauqua, Erie,
Niagara
Central
Southern
Tier
Syracuse
Binghamton,
Elmira, and
Ithaca
 Kingston
 NewburghMiddletownPoughkeepsie
None
 Utica-Rome
 Syracuse
 Binghamton
 Elmira
 Ithaca
 Buffalo-Niagara
Falls
Comparison to regions in
Boyd, Lankford, Loeb,
Wyckoff (2005a)
Same
Separated into urban,
suburban, and rural areas.
Removed Columbia,
Delaware, Greene, and
Otsego counties.
Added Columbia and
Greene from Hudson
Valley. Added Warren and
Washington counties from
North Country. Removed
Montgomery county.
Removed Warren and
Washington counties.
Added Montgomery from
Capitol. Added Otsego
county from Hudson Valley.
Added Cortland county
from Southern Tier rural
region.
Combined the IthacaElmira-Binghamton MSAs
with the Southern Tier rural
region. Added Delaware
county from Hudson Valley.
Removed Cortland, Seneca,
Wyoming and Yates
counties.
Added Seneca, Wyoming,
and Yates counties from
Southern Tier rural region.
Added Allegany,
Cattaraugus and Chautauqua
counties from Southern Tier
rural region.
50
CEPWC Working Paper Series No. 2. October 2012.
Available at http://curry.virginia.edu/research/centers/cepwc/publications.
Curry School of Education | Frank Batten School of Leadership and Public Policy | University of Virginia
Miller – Rural Teacher Recruitment
TABLE A-2. Revealed Geographic Preferences of Beginning Teachers, 1994 to 2002
Labor Market
New York City
Hudson Valley
Capitol
North Country
Mohawk Valley
Central
Southern Tier
Finger Lakes
Western
State-wide
Labor Market
New York City
Hudson Valley
Capitol
North Country
Mohawk Valley
Central
Southern Tier
Finger Lakes
Western
State-wide
Labor Market
New York City
Hudson Valley
Capitol
North Country
Mohawk Valley
Central
Southern Tier
Finger Lakes
Western
State-wide
Job Labor Market by Home Labor Market
Remain in Submarket (%)
Remain in Labor Market (%)
Leave Labor Market (%)
75.1
35.3
35.7
66.3
35.3
32.9
41.6
34.6
34.2
59.8
96.3
68.2
70.9
66.3
56.1
62.3
66.6
78.4
76.9
85.7
3.7
31.8
29.1
33.7
43.9
37.7
33.4
21.6
23.1
14.3
Job Labor Market by College Labor Market
Remain in Submarket (%)
Remain in Labor Market (%)
Leave Labor Market (%)
74.4
24.2
10.8
38.2
13.2
12.0
10.8
25.0
21.2
44.0
98.3
60.0
51.0
38.2
23.4
30.6
31.8
63.9
63.0
67.5
1.7
40.0
49.0
61.8
76.6
69.4
68.2
36.1
37.0
32.5
College Labor Market by Home Labor Market
Remain in Submarket (%)
Remain in Labor Market (%)
Leave Labor Market (%)
53.2
17.1
15.7
61.3
11.3
23.8
4.7
20.5
34.9
41.0
71.3
40.9
45.1
61.3
22.8
55.0
13.3
54.9
73.6
62.4
28.7
59.1
54.9
38.7
77.2
45.0
86.7
45.1
26.4
37.6
NOTE: Statistics reflect only those teachers hometown and/or college location information, as required All teachers whose hometowns are out-ofstate or attended college out-of-state are considered to have left their home/college labor market and are included in the state-wide statistics
51
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