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

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.

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

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

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)

Where are the villages? 2010 Places Total: 1,189 2000 2010* 2000 2010* Population 500 or less 73 66 Population 501 to 2,500 272 245 Population 2,500+ 705 846 *exclude 32 outliers (based on village’s median house value)

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

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

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

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

Additional Work –Nearby Schools

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%

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

Villages with Nearby Schools Finding: a large number of villages, though do not have any school in their boundaries, do have schools nearby. Example -- 2010 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

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

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

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

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

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?