1. Social and Economic Impact of Nearby Schools on Rural Communities: Exploring the Gradient
John W. Sipple, PhD
Joe D. Francis, PhD
Development Sociology
Xiaoling Li, PhD Candidate, Regional Science
Paper presented at the 2014 Annual Meeting of the Rural Sociological Society
New Orleans, LA August 1, 2014

2. Introduction
View that schools in rural communities are central to community vitality (e.g., the hub of community life).
School district consolidation and school closure are often proposed to relieve fiscal stress (reduce tax burden & enhancing educational opportunity).
It is unclear to what extent schools play in the social and economic vitality of rural communities.

3. Introduction
Lyson (2002) showed that the presence of schools provide significant social and economic benefits to rural communities.
His findings, when compared to rural communities without schools…
In smallest rural communities, the presence of a school is associated with higher housing values
Higher per capita income from self-employment is found in communities with schools
Higher proportion of workers in communities with school are employed within their villages

4. This Study
This paper revisits Lyson’s hypothesis and our Lyson Redux by employing 2000 & 2010 Census and geoinformatic analysis of school locations in New York State.
Like Lyson, we focus on villages (i.e. incorporated villages with population 2,500 or less)
These rural villages are first categorized by presence or absence of public schools; We then examine the impact of Nearby schools in the Gradient surrounding villages.
Presence/absence of schools: Bivariate Mean Difference
Presence/absence of schools: Multiple Regression
School Indicator (in & nearby schools): Multiple Regression
Panel Analysis

5. Data & Methodology Data are primarily drawn from two databases:
Census data
2000 Census (short form & long form)
2010 Census (decennial & ACS)
Census geography TIGER/Line® shapefile: rural incorporated villages in New York State (NYS) of both 2000 & 2010 Census
Public school directory from NYS Department of Education (2010) and Cornell Program on Applied Demographics (2000)
National Center for Educational Statistics School Search
Key variables include:
Population characteristics
Housing characteristics
Income and welfare
Occupational and employment characteristics
Distance from village centroid to school (as bus drives)

6. Where are the villages? 2010 Places Total: 1,189 2000 2010**
Population 500 or less
73 66
Population 501 to 2,500
Population 2,500+
*exclude 32 outliers (based on village’s median house value)

7. Changes in Places (Villages)
Rural villages
345 in 2000
343 in 2010

8. Identifying presence of schools
Schools in 2000
Total: 4,281
To identify schools’ impact on rural communities, we needed to locate public schools in relation to rural communities
This involved geocoding physical addresses of schools—addresses spatially referenced to pin down the coordinates of their locations
Schools in 2010
Total: 4,513

9. Changes of Villages: Presence/Absence of Schools
Change in presence of schools in villages :
2010
Absence
Presence ∑
2000
36%
21%
57% 6%
37%
43%
42%
58%

10. Nearby Schools?
Previous work examined only: school presence or absence within a community’s boundaries.
But what of Nearby schools? What if we measured the gradient around the villages?
0.5 mi
1 mi
2 mi

11. Additional Work –Nearby Schools

12. Changes of Villages: in & within 5 mi schools
Change in Number of schools located in & nearby village: 2000 – 2010
Decrease 1-4 schools: 22.1%
No Change: 50.5%
Increase 1-22 schools:27.4%
Decrease 3-4 schools: 2%
No big Change [-2,2]: 89.4%
Increase 3-22 schools:8.6%

13. Results—Bivariate:comparison of villages with/without schools
1990 (Lyson's)
2000
2010
small
large
Number of Village (with school vs. without)
36/28
192/41
13/60
126/146
12/54
106/139
Housing and Municipal Infrastructure Characteristics
Average house value ($) +* + - -*
Median house value ($)
Houses built within the past 20 years (%)
Income and Welfare
Household income ($)
Per capita income ($)
Households receiving public assistance (%)
Population in poverty (%)
Children in poverty (%)
Occupational and Employment Characteristics
Professional, managerial, executive workers (%)
Households with wage income (%)
Per capita income from wage ($)
HHs with income from self-employment (%)
Per capita income from self-employment ($)
Residents who work in village (%)
Workers who commute < 15 min. to jobs (%)
* p< 0.05 within community size categories

14. Villages with Nearby Schools
Finding: a large number of villages, though do not have any school in their boundaries, do have schools nearby.
Example villages:
Villages without school :
15.5%
8.2%
6.1%
10.8%
43.4%
56.6%
2.8%
97.2%
Villages with school :
0.5mi
1mi
2mi
5mi

15. School Proximity Indicator for Each Village
Measure of the Proximity of School(s) to Village: (# schools, Distance to school(s))
∑ j = sum of number of schools within 5 miles
dIj = distance from school j to village i

16. Multiple Regression Results – School Proximity
HH Inc
2000
2010
PerCap Inc
H Value
School Prox
+**
<39 -*
-**
W Child +*
% White
White collar
Self Employ
Historical bd R2
.82
.66
.78
.59
.77
.54
Significance level: * for 0.05; ** for 0.01

17. Multiple Regression Results Comparison
Using presence / absence of schools
–– not significant
Using School Proximity Indicator for villages
–– a strong factor
HH Inc
2000
2010
PC Inc
H Value
Large +*
School
SchXSize -* …… R2
.79
.60
.75
.57
.73
.51
HH Inc
2000
2010
PC Inc
H Value
SchIndic
+** …… R2
.82
.66
.78
.59
.77
.54
Significance level: * for 0.05; ** for 0.01

18. Panel Analysis Presence/absence of schools is not significant
HH Inc
PerCap Inc
H Value
Large
School
School X Size
White collar +*
+**
HHWageInc
Self Employ
Presence/absence of schools is not significant
Schools in and near villages matters
HH Inc
PerCap Inc
H Value
School Indic
+** +*
White collar
HHWageInc
Self Employ
Significance level: * for 0.05; ** for 0.01

19. Discussions & Conclusions
More nuanced story around the Lyson Hypotheses and the stabilizing presence of schools on communities.
Fertile ground for studying the proximal gradient around places to assess impact of infrastructure decisions on community vitality.
Next step is to layer other entities
Libraries
Health care facilities
Parks
What else?