Geographic Analysis of US Green

Sector Industry Concentration

D. Lane Register, Dayton M. Lambert, Burton C. English, Kimberly L. Jensen, R. Jamey Menard, and Brad Wilson, Michael D. Wilcox

Selected Paper prepared for presentation at the Applied Agricultural Economic Association Annual Meeting, Seattle, WA,

August 12-14, 2012

Portion of this research funded by USDA NIFA and the Southeast Sun Grant Center. Views expressed here are those of the authors.

Overview

• Renewable biofuels (or so-called “green energy” or advanced biofuels) expected to be an important component of the Renewable Fuels Standard portfolio

• What impact would multiple facility sitings have on

the geographic distribution

▫ Jobs, employment, and income ▫ Emissions ▫ Water quality and land use

Modified IMPLAN Model

●Energy conversion costs

●Production coefficients

Rural Jobs

Rural Economic

Output

Rural Value-Added

Labor Income

Task I: Technology-specific Facility Siting (Cellulosic-based Butanol, Green Diesel, Co-/Direct Firing)

Resource s •Local capacity •Industry concentration (LQ’s) •Infrastructure •Feedstock potential

Sequential, Multiple Facility Site

Selection

Impacted Region •Identify Counties •Identify Hydrologic Units

Task 2: Regional Economic Impacts

Disaggregate Total

Employment Multiplier

Effects

Task 3: Environmental Impacts and Indicators

Business Establishment Distribution

Sector-specific

Geographic (County-

level)

Water Quality

Sedimentation

Nutrient Loading

Air Quality

Congestion, Traffic Safety

Emissions

Facility locator model

Suitability Layers Crop Yields Land Use Data Transport Networks

Inputs

Cost of Production of Traditional Crops

Cost of Production of Biofeedstock

Source: Wilson (2009)

Suitability Analysis Candidate facilities to be “filtered” according to industry requirements

Source: Wilson (2009)

Facility locator results Cost-minimizing biorefinery location and feedstock supply

Source: Wilson (2009)

Multiple Facilities

Renewable fuel industries

and supporting sectors

• Biodiesel

• Fast pyrolysis

• Gasification

• Cellulosic

• Direct fire

“Members” identified using expenditure budgets generate by NREL

“Phase I” Objective

• Quantify industry concentration

• Combine measures with facility locator model

• This presentation focuses on global firm concentration indices

Economic Concentration Measures • “Global” - describes the degree to which industries are

concentrated within a region. • Examples include:

▫ Florence (1934) Location Quotient ▫ Hoover’s (1937) Localization Index ▫ Gini Coefficient (Krugman 1991) ▫ Ellison and Glaeser’s (1997) Industry Concentration Index ▫ Guimarães, Figueiredo, and Woodward (2007) Industry

Concentration Index based on plant counts

Plant size tends to be larger where manufacturing firms concentrate Builds on EG index, normalizing by distribution of business

establishments

Framework for Global Firm

Concentration Index

• Expected probability of location: xj is area j’s share of sub-sector employment E(pj) is conditional on “natural advantages” associated with location

• Variance:

• g ; degree of localization above normal economic activity

(difference between pj and xj) • g = 0, no excess concentration; g = 1, all firms located in one

region.

Concentration indices

GFW (2007) – Plant EG (1997) – Employment

Asp

ati

al

Sp

ati

al

Bandwidth selection (GFW, 2011)

Aspatial/spatial

global concentration (gc)

0

0.01

0.02

0.03

0.04

0.05

0.06

0 0.01 0.02 0.03 0.04 0.05 0.06

Sp

ati

al

glo

ba

l in

de

x

Aspatial global index

Results: overview of

concentration estimates

and bootstrapped

95% C.I.’s

(disaggregated sectors)

Concentration index (gc) distributions Combined sectors

0.000

0.002

0.004

0.006

0.008

0.010

gc

2002 Employment Concentration 2002 Establishment Concentration

2006 Employment Concentration 2006 Establishment Concentration

Connecting global index measures to costs

savings from external economies

(Glaeser and Ellison, 1997: J. Political Economy)

Cost elasticity: projected %-change in direct jobs in the economy divided by the projected percent change in labor income in the economy (in dollars).

Concentration, variability of expected costs

savings

0

2

4

6

8

0 0.2 0.4 0.6 0.8 1

Coef

fici

ent

of

Var

iati

on

Share of Employment in All Renewable Energy

Industries (xj)

Biodiesel Residential Solar

Biodiesel: gc = 0.0005 Residential solar: gc = 0.01

Higher CV’corresponds with potential for higher costs savings

Further steps

• Integrate socioeconomic and industry structure data into facility locator model

▫ Supporting industry business establishments

▫ Employment

• “If you build it, they will come”…but will they?

• So far, small, but significant levels of concentration in this analysis…how does this influence site selection parameters?

References

• Wilson, B. Modeling Cellulosic Ethanol Plant Location Using GIS. M.S. Thesis, The University of Tennessee, Knoxville, 2009.

• Ellison, G. and E. L. Glaeser. 1997. Geographic Concentration in US Manufacturing Industries: A Dartboard Approach. Journal of Political Economy 105(5): 889-927.

• Guimarães, P., O. Figueiredo, and D. Woodward. 2007. Measuring the Localization of Economic Activity: A Parametric Approach. Journal of Regional Science 47(4):753-774.

• Guimarães, P., O. Figueiredo, and D. Woodward. 2011. Accounting for Neighboring Effects In Measures of Spatial Concentration. Journal of Regional Science 51(4):678-693.

Data used in this example (Register, 2012)

• 2006:

▫ Employment : IMPLAN

▫ Establishments: US CBP

• Data for all 3078 county divisions in the contiguous US