Download PDF

Policy Tools for Managing
Biological Pollution Risks from Trade
Carson J.
*
Reeling
and Richard D.
†
Horan
* Corresponding
author
Department of Economics and Environmental Science and Sustainability Program
Western Michigan University
[email protected]
† Department
of Agricultural, Food, and Resource Economics
Michigan State University
Selected Poster prepared for presentation at the 2015 Agricultural &
Applied Economics Association and Western Agricultural Economics
Association Joint Annual Meeting, San Francisco, CA, July 26-28
Copyright 2015 by Carson J. Reeling and Richard D. Horan. All rights reserved. Readers may make verbatim copies of this
document for non-commercial purposes by any means, provided that this copyright notice a appears on all such copies.
Policy Tools for Managing Biological Pollution Risks from Trade
Carson J.
Reeling1
and Richard D.
Horan2
|
1Department
of Economics and Environmental and Sustainability Studies Program, Western Michigan University
2Department of Agricultural, Food, and Resource Economics, Michigan State University
INTRODUCTION
RESULTS
The spread of infectious livestock diseases from live animal movement can be
considered a form of “biological pollution.” Prior literature assumes trade-related
biological pollution externalities are unilateral (i.e., importers can spread disease
to neighbors, but cannot be infected by other importers) and arise solely from
trade of infected goods. Their results suggest uniformly-applied trade-based
policies can efficiently manage disease risks. However, trade is only part of the
externality, and importers may be at risk from infection by other importers.
1. Social planner’s first-order conditions (FOCs) for input vi ∊ {xi, zi, ai} and bi:
We find the problem is more analogous to nonpoint source pollution problems:
• Biological pollution is stochastic and largely unobservable at reasonable cost;
• Myriad choices generate external risks – not just trade choices. Some choices:
• Produce only public risk-management benefits (not included in prior work);
• Produce both public and private risk management benefits: trade-related
biological pollution is a “filterable externality” (private risk management
incentives not included in prior trade models), and spillovers may be bilateral;
• Heterogeneous importers generate heterogeneous disease risks.
We reframe trade-related biological pollution as nonpoint source pollution and
explore efficient policy design. The nonpoint literature suggests efficiency may be
attained by directly targeting all choices that contribute to emissions (not just
trade decisions) or by indirectly targeting these choices via a performance-based
proxy, a pollution production function that depends on these choices.
• Private FOCs ignore the biological pollution externality, yielding vi = vi0 in Fig. B.
• Externality arises from ei, which depends only partly on trade decisions (xi, zi).
• Size of externality is smaller when risks are filterable; distance between privately-optimal risk mitigation and social
optimum is smaller when mitigation has private benefits (blue line in Fig. B) than when mitigation has no private
benefits (red line in Fig. B).
2. Efficiency can be attained through
• Individual-specific tax on ei: a “risk-based tax” (analogous to a NPS performance proxy tax) or
• Individual-specific taxes on xi, zi, ai (analogous to NPS input taxes).
3. Strategic complementarities may arise due to presence of e–i in Ωi; may cause multiple Nash equilibria.
• Prices alone may not yield efficient outcome in such settings.
• Command-and-control instruments (e.g., minimum requirements on zi) may be required in addition to incentives.
• These will not be binding, but align importers’ expectations of others’ behavior around the efficient outcome
FIG. A. BIOLOGICAL POLLUTION PROCESS
A MODEL OF DISEASE RISKS
Disease flow
• Importers, indexed by i = 1, … , N, within a region purchase a share xi ∊ [0, 1] of animals from a risk-free region and
the rest from a risky region.
• Importer i can invest in surveillance, zi ∊ [0, 1], and abatement effort, ai ∊ [0, 1]. Surveillance prevents diseased
animals from being introduced into one’s own herd, and infected animals are culled if detected. Abatement
prevents disease from leaving one’s herd to infect neighboring importers and non-importers.
• We consider two cases (see Fig. A):
1. Unilateral spillovers (no bilateral externalities in Fig. A) – importer i can spread disease to neighbors, but cannot
be infected by other importers.
• The social expected net benefits from live animal trade are
Risky
region
1 – x–i
1 – xi
2. Bilateral spillovers – importer i can spread disease to neighbors, and vice versa. We assume here importers can
invest in biosecurity, bi ∊ [0, 1], to protect themselves from spread of infection by neighbors.
• The social expected net benefits from live animal movements are
where now individual importers suffer biological pollution spillovers from their neighbors’ choices, e-i.
Importers can reduce the probability of damage by investing in biosecurity bi.
xi
z–i
zi
FIG. B. THE ABILITY TO FILTER RISKS
SHRINKS EXTERNALITIES UNDER
UNILATERAL SPILLOVERS
Riskfree
region
Disease risk
abatement
x–i
Bilateral Externalities
Importer
i’ s farm
ai
b–i
bi
ai
where ei = ei(xi, zi, ai) is the probability disease spreads from i’s herd: the “biological pollution production
function”.
Optimal taxes are smaller
when risks are filterable
(see Fig. B)
Externality is
from spread, and
therefore occurs
at this point
Neighboring
importers’
(–i ) farms
a–i
Nonimporting
producers
a–i
Magnitude of
externality when
risks are not
filterable
Magnitude of
externality when
risks are filterable
CONCLUSIONS
ACKNOWLEDGEMENTS
Our findings contrast with prior trade literature in three ways: (1) Incentives must
target the externality directly or all choices contributing to the externality – targeting
only trade choices is inefficient; (2) Efficient incentives are importer-specific, and the
magnitude of efficient incentives is smaller for choices (like trade choices) for which
importers have private incentives for risk mitigation; and (3) Bilateral spillovers imply
the possibility of multiple Nash equilibria and the need for additional, commandand-control policy instruments for efficiency.
We gratefully acknowledge funding from the USDA
National Institute of Food and Agriculture, Grant
#2011-67023-30872, grant #1R01GM100471-01
from the National Institute of General Medical
Sciences (NIGMS) at the National Institutes of
Health, and NSF grant #1414374 as part of the
joint NSF-NIH-USDA Ecology and Evolution of
Infectious Diseases program. The contents of the
paper are solely the responsibility of the authors
and do not necessarily represent the official views
of USDA or NIGMS.