daniel l. nielson brigham young university michael j. tierney college of william and mary
DESCRIPTION
Principals and Interests: Collective Principals and Environmental Lending at Multilateral Development Banks. Daniel L. Nielson Brigham Young University Michael J. Tierney College of William and Mary. Empirical Puzzle. Trends Member governments grow more environmentalist after early-1970s - PowerPoint PPT PresentationTRANSCRIPT
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Principals and Interests: Collective Principals and Environmental Lending at
Multilateral Development Banks
Daniel L. NielsonBrigham Young University
Michael J. TierneyCollege of William and Mary
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Empirical Puzzle
Trends Member governments grow more environmentalist after
early-1970s MDBs largely ignore environment until late-1980s Late-1980s through 1990s – Big increase in MDB
environmental lending
Gaps How can we explain delay and eventual adoption of an
environmental agenda? How can we explain timing of adoption across different
MDBs?
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IR Theory and IOs
Neorealism and Neoliberalism deny IO agency
Constructivism suggests abdication Agency theory resolves gaps
IOs are independent actors Member states conceived as principals of IOs Our distinction: collective principal
Principals’ converging preferences guide agents Principals’ coordination problems enable agency slack
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Modeling Collective Delegation
Which type of principal?
Single Principal
Collective Principal
Multiple Principals
Most IOs
X Agent XYZ Agent Y Agent
Z
X
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Two Stages of Delegation at MDBs: Asian Development Bank
US
Japan
Laos
Afghanistan
Bhutan
A
B
C
ASDB
States Executive
Board MDB
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Collective vs. Multiple Principals, Stage 1
0% 50% 100% 25% 75%
X Y Z
X
Collective Principal with Majority Vote
Proportion of Environmental Loans
Policy Outcome
0% 50% 100%25% 75%
X
Y
Z
X
Proportion of Environmental Loans
Multiple Principals, Independent Action
Observational Equivalence
Policy Outcome
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Collective vs. Multiple Principals, Stage 2
0% 50% 100% 25% 75%
XYZ Collective Principal
with Majority Vote
Proportion of Environmental Loans
X
Policy Outcome
Divergent Expectations
0% 50% 100%25% 75%
X
YZ
Proportion of Environmental Loans
Multiple Principals, Independent Action
Policy Outcome?
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What Causes IO Agents to Change Behavior? Hypothesis: As the policy preferences of collective
principals shift toward environmental concerns MDB Environmental loans will increase MDB Neutral loans will increase MDB Dirty loans will decrease
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Imputing & Aggregating Preferences (IVs) Imputing Preferences
Infer preferences from behavior – revealed preferences
Use policy outcomes as proxies for preferences Environmental Policy Index Environmental Foreign Aid
Aggregating Preferences Examine states’ preference distribution Predict voting coalitions Weight state’s influence by degree it proves
“pivotal” to potential coalitions
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Data on Dependent Variable More than 7,500 individual development loans
World Bank (IBRD & IDA) African Development Bank & Fund Asian Development Bank Inter-American Development Bank Islamic Development
Coded all loans on five-point environmental scale Dirty Strictly Defined: direct negative impact (i.e., logging) Dirty Broadly Defined: moderate but negative (agriculture) Neutral: no immediate impact (education, telecomm.) Environmental Broadly Defined: preventative (nuclear safety) Environmental Strictly Defined: direct (pollution control,
biodiversity protection)
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MDB Environmental Lending
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
Year
Per
cen
t E
nvi
ron
men
tal
& N
eutr
al P
roje
cts
AFDFIADBIBRDIDAISDB
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Environmental Policy Preferences - Policy Index
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
11
98
0
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
Year
En
viro
nm
enta
l P
refe
ren
ces
(Po
licy
In
dex
)
AFDBAFDFASDBIADBIBRDIDAISDB
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Variables of Interest Dependent Variable: Environmental Impact
1=DSD, 2=DBD, 3=N, 4=EBD, 5=ESD
Key Indep. Variable: Environmental Preferences Measured 3 ways:
Environmental Policy Index Environmental Foreign Aid / Total Aid Environmental + Neutral Aid / Total Aid
Controls Organic Water Pollution, (De)forestation, Threatened Birds,
Sanitation, Infant Mortality, Fertility Rate, Agricultural Value Added, CITES Commitments, GDP Per Capita, ln(GDP), ln(Population), Domestic Savings, Exports, Vehicles, Protected Land
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Ordered Logit Regression ResultsDep. Var.: Environmental Impact
Environmental Environmental Environmental Independent Variables Preferences: Preferences: Preferences: Policy Index Foreign Aid Foreign Aid
(w/Neutral) Environmental Policy Preferences
1.137***
(0.21) Environmental For. Aid Prefs. 2.771*** (0.20) Enviro. Aid Prefs. (incl. Neutral) 2.097*** (0.16) Fertility Rate -0.0787*** -0.0255 -0.0122 (0.029) (0.031) (0.030) Savings -0.00974*** -0.00596** -0.00544** (0.0030) (0.0024) (0.0026) GDP Per Capita (1k US $) 0.0602*** 0.0241 0.0397* (0.023) (0.020) (0.023) Agriculture Value Added -0.00726** -0.00538* -0.00411 (0.0037) (0.0032) (0.0031) Threatened Birds 0.268 1.028* 0.982* (0.64) (0.59) (0.57) Forestation (Deforestation) -1.042*** -0.886*** -0.823*** (0.19) (0.19) (0.19) Sanitation 0.00308** 0.00284** 0.00198 (0.0014) (0.0013) (0.0013) Protected Land 0.524 0.913** 0.819* (0.47) (0.44) (0.43) Number of Observations 7538 7538 7538 Variables tested but insignificant statistically: Log of GDP, Log of Population, CITES Commitments Kept, Protected Land, Organic Water Pollution, Vehicles, Exports / GDP, Infant Mortality Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
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Results Summary Environmental Preferences:
Positive and Significant (beyond .001 level) Substantively Important
+1 stdev (.12 to .16) for Environmental Preferences 4.6% to 8.3% in probability of Dirty project 2.5% to 4.9% in probability of Neutral project 1.6% to 3.4% in probability of Environmental project
Controls: mixed to weak Only Savings and (De)Forestation performed
consistently Others significant in 2 specifications:
GDP per capita, Agriculture Value Added, Threatened Birds, Sanitation, and Protected Land
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Conclusion
Collective delegation can work In (Most) Difficult Context
International Anarchy Extreme Preference Heterogeneity Many Actors (up to 180)
More work to Other specifications of preferences Further robustness checks