1 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

2 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?

3 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

4 Modeling Collective Delegation Which type of principal? Single Principal Collective Principal Multiple Principals Most IOs X Agent XYZ Agent Y Z X

5 Two Stages of Delegation at MDBs: Asian Development Bank

6 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

7 Collective vs. Multiple Principals, Stage 2 0% 50% 100% 25% 75% X Y Z Collective Principal with Majority Vote Proportion of Environmental Loans X Policy Outcome Divergent Expectations 0%50%100%25%75% X Y Z Proportion of Environmental Loans Multiple Principals, Independent Action Policy Outcome?

8 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

9 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

10 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)

11 MDB Environmental Lending

12 Environmental Policy Preferences - Policy Index

13 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

14 Ordered Logit Regression Results Dep. Var.: Environmental Impact

15 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

16 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

17 Extra Slide Pivotal-Weighted Preferences

Pivotal-Weighted Preferences Actor Vote ShareIdealPivot. Ideal* Pivot. A B C D E Sum5.2 Actors’ Ideal Points: A B CD E PotentialPivotal CoalitionsPlayers ABCA or C ABCDNone ABCDENone BCDB or D BCDEB