Innovation Capacity, Natural Amenity, Community/Rural/Urban Development, O31, Q51,

Harris, Thomas R.

Kim, Man-Keun

Zhu, Erqian Julia

English

Research Papers in Economics

 
 
Modeling US Counties’ Innovation Capacity with a Focus on Natural 
Amenities 
 
 
 
Erqian Julia Zhu 
Economist 
China Center for International Economic Exchanges (CCIEE) 
Beijing, China 100017 
E-mail: erqian.zhu@gmail.com 
 
 
Man-Keun Kim 
Assistant Professor 
Department of Applied Economics 
Utah State University 
Logan, UT 84322 
E-mail: mk.kim@usu.edu 
 
 
Thomas R. Harris 
Professor 
Department of Resource Economics 
University of Nevada-Reno 
Reno, NV 89557 
Email: tharris@cabnr.unr.edu 
 
 
 
 
 
 
 
Poster prepared for presentation at the Agricultural & Applied Economics Association’s 2011 
AAEA & NAREA Joint Annual Meeting, Pittsburgh, Pennsylvania, July 24-26, 2011 
 
 
 
Copyright 2011 by [E. Julia Zhu, Man-Keun Kim, and Thomas R. Harris]. All rights reserved. 
Readers may make verbatim copies of this document for non-commercial purposes by any means, 
provided that this copyright notice appears on all such copies. MODELING US COUNTIES’ INNOVATION CAPACITY WITH A FOCUS ON NATURAL AMENITIES
E. Julia ZHU, Man-Keun KIM and Thomas R. HARRIS
1. Motivation
 Regional innovation capacity becomes an important 
factor to enhance regional competitive advantages
as the U.S. is building a knowledge-based economy.
 In the past few decades, measuring innovation 
capacity using numerous methods have been 
attempted
 R&D expenditure
 Patent counts
 Employment in high-tech industry
 The key issue is that innovation capacity varies widely 
over regions 
 Previous literature have tried to explain these 
differences but conclusive empirical evidences are 
not provided.
 One of crucial factors to affect the regional 
innovation capacity is (natural) amenity and it 
can explain variation in innovation capacities.
 The association between natural and built-in 
amenities with the overall quality of life and 
economic growth patterns has been well 
established within the development literature (e.g., 
Deller et al., 2001; Deller et al., 2008).
 However, somewhat surprisingly, the relationship 
between innovation capacity and amenity has not 
been quantified and analyzed rigorously
 The intent of this study is to address these 
connections while focusing on the potential value 
of amenities
2. Innovation Capacity
 E. Julia Zhu is an Economist, China Center of International Economic Exchanges (CCIEE), China (erqian.zhu@gmail.com); M-K. Kim is an Assistant Professor, Department of Applied 
Economics, Utah State University, Logan UT (mk.kim@usu.edu); T.R. Harris is a Professor, Dept. of Resource Economics, University of Nevada-Reno, Reno NV (tharris@cabnr.unr.edu)
Authors
 Innovation is a process that begins with an invention 
and results in the introduction of a new product, 
process or service to the market place (Small Business 
Administration, 2009)
 Invention
 Innovation
 Many studies have categorized innovation 
measurements into two groups: innovation inputs, e.g., 
R&D expenditure and employment in high-tech, and 
innovation outputs, e.g., patent counts (Barkley et al., 
2006; Patanawaraha and Polenske, 2007; Slaper and 
Thompson, 2009). 
 The State New Economy Index (Ewing Marion 
Kauffman Foundation and Information Technology and 
Innovation Foundation, 2010) uses various indicators 
to measure the economic competitiveness over U.S. 
states.  
 The innovation capacity category in this index 
consists of five indicators such as high-tech jobs, 
scientists and engineers, patents, R&D expenditure 
and venture capital. 
 In this study, we use two indicators as the innovation 
capacity or measurement
 Number of patents or patents count
 Share of high-tech employment
3. Amenities
 Numerous studies, e.g., Roback (1982), Blanchflower
and Oswald (1994), Gottlieb (1994), Deller et al. (2001), 
and Deller et al. (2008),  have documented amenities 
play an increasingly important role in driving regional 
economic growth and enhancing innovation capacity.  
 This study proposes to categorize amenities and other 
variable that might contribute to innovation capacity 
as following 
 Urban amenities: crime rate, number of 
universities, number of museums, number of golf 
courses
 Natural amenities: temperature, humidity, water 
area, wild land area
 Man-made or built-in natural amenities: 
number of boat units, number of picnic units, 
trailheads, camping grounds, ski resorts
 Local economic conditions: population, wage 
rate
4. Data
 Major data sources are the National Outdoor 
Recreation Supply Information System (NORSIS), U.S. 
Census County Business Patterns, Economic Research 
Service, and Bureau of Economic Analysis.  Number of 
observation is over 3,000 for the year of 1998
 Descriptive statistics are available upon request.
5. Model
 The knowledge production function (KPF) proposed by 
Griliches (1979) is utilized to estimate the existence of 
local characteristics of innovation capacity. 
where y = innovation capacity, x1 = amenities, and x2 
= local economic condition;  and  are parameters 
to be estimated and  is the error term
 The ordinary least squares (OLS) model is used to 
estimate the KPF for the share of high-tech industries. 
 The count model is adapted to estimate the KPF for 
the number of patents following the approach 
proposed in Cameron and Trivedi (1998)

 
2 1 x x y 
6. Results and Discussion
Share of 
High-tech
Employment
Number of 
Patent
OLS Negative Binomial
Pop 0.0018 0.9243*
Unemployment 0.0490* 0.2061*
Wage 0.0085* 0.0170*
Crime rate 0.0307** 0.3247*
# Universities 0.0137 0.1824*
# Museums  0.0225* 0.0922*
# Golf courses  0.0421* 0.1201*
Temp in Jan.  0.0119* 0.0151*
Sunlight hrs in Jan  0.0016* 0.0010
Temp in July  0.0352* 0.0289*
Humidity in July 0.0048 0.0741*
Water area acres  0.0040 0.0779*
Wild land acres  0.0082* 0.0164*
# Boat Units  0.0045 0.0252**
# Picnic Units  0.0045 0.2305*
# Swim Units  0.0086 0.0094
# Trailheads  0.0187* 0.0190**
# Camping  0.0043 0.2129*
# Winter activity  0.0013 0.1147
# Fish and Hunting  0.0406** 0.0280
# Ski resorts  0.0067 0.0418
# Parks  0.0184* 0.0005
Intercept 2.4471* 3.8239*
R2 0.2806
LR Test for  = 0, Prob > 2 0.0000
 Bold numbers with * are statistically significant at 1% 
level.  Numbers with ** are statistically significant at 
5% level.
 In general, followings increase the innovation capacity 
for US counties
 Higher wage rate and lower unemployment rats
 More museums, more golf course
 Warm winters and cool summers
 Urban amenities is crucial to enhance innovation 
capacity
 Natural amenities is also important not man-made 
or built-in natural amenities

Modeling US Counties’ Innovation Capacity with a Focus on Natural Amenities

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