Incorporating Institutions into Bio-Economic Models of Sustainable Intensification

Source: World Fish Source: ICRAF Incorporating Institutions into Bio-Economic Models of Sustainable Intensification Kimberly Swallow and Brent Swallow Workshop on the BioSight/Sustainable Futures Project IFPRI Headquarters & St. Gregory Hotel, Washington, DC 3-5 December, 2013 Source: ILRI

Outline What?: Institutions Why?: Problem Setting Development Context Sustainable Intensification How?: Integrating Institutions into Bio-Economic Models Conceptual Framework Types of Models 12 Examples Approaches & Tradeoffs Conclusions: Best Practice

Fig. 1 Approaches to Integrating Institutions into Bio-Economic Models Institutions Explicit? Implicit Explicit Fixed Varying Partly Endogenous

Outline What?: Institutions Why?: Problem Setting Development Sustainable Intensification How?: Integrating Institutions into Bio-Economic Models Conceptual Framework Types of Models 12 Examples Approaches & Tradeoffs Conclusions: Best Practice

Institutions Institutions Institutions Definition: formal laws and informal rules that define expectations in transactions Governance Bodies: Choose, monitor & enforce Costs Benefits: Provide incentives and constraints on behavior Provide the context of production and exchange

Context of Production & Exchange Institutions Rights & Distribution of Bargaining Power in Strategic Interactions Define Transactions, esp. t-costs (I.C.E.) Market & Non-Market (Governed) Contexts Imperfections: Who Bears the Costs? Informational Asymmetries Transactions Costs Externalities

Space Coordination Fig. 2 CAPRi: Scale Factors & Institutions (Source: Adapted from Knox, Meinzen-Dick and Hazell, 2002) Institutions Low Tenure Security High Institution Village High Terracing Improved Fallows Plot Micro(Hand)-Dosing Agro-forestry Low Scale Short term Time Long term

Fig. 3 Linkages: Institutions Interactions/Externalities Within and Between Sectors (Source: Adapted from Msangi 2013) Institutions Fisheries Capture Aquaculture species loss Livestock Intensive Extensive feed competition N runoff/ghg grasslands Forestry/ Residential Crops Area Yield land use competition biodiversity/ghg soil (salt, SOM) water N runoff/ghg

Fig. 4 (Biophysical) Techno-Institutional Unit (Sources: Adapted from Ostrom and Cox, 2010; Williamson, 2000) Institutions Embeddedness Biophysical- Technical Community Attributes Institutional Institutional Environment Governance Key: =outputs =feedbacks and user learning Marginal

Problem Institutional Implications Institutions of Stage of Development Embeddedness Biophysical- Technical Community Attributes Institutional Institutional Environment Governance Marginal High degree of embeddedness Large informal exchange sector Governance Structures Highly Varied (Cultural Roots) Horizontally Overlapping & Vertically Nested Linkages are In-Transition (Decentralization & Devolution) Governments: Low Capacity (Responsiveness & Enforcement) & De Jure-De Facto Dichotomy Customary governance structures challenged to keep up with changing transactional needs Regulation and market (dis)incentives challenging

Fig. 5 Institutional Implications Institutions of Sustainable Intensification Problem Intensification Interactions Institutions Resource Scarcity Technique or Enterprise- Technique Market Orientation (Low to High Input/Output) Resource Access Scale Changes Input/Output Exchange Distribution & Externalities Tenure Security & Coordination Coordination/ Conflict Management

Key Elements of a Bio-Economic Model Models Options Resources/Types of Capital Activities & motivations Constraints Operational Units Interactions Outcomes Impact Pathways & Externalities Exogenous & Stochastic Events Feedbacks & Learning Institutions: Property rights Market exchange Intra-household allocation Social networks Collective action Policies Governance Units

Fig. 7 Bio-Econ. Institutional Framework (Sources: Adapted from Ostrom and Cox, 2010; Williamson, 2000) Models Exogenous Influences & Stochastic Events Biophysical- Technical Community Attributes Institutional Embeddedness Action Situations Interactions Outcomes Institutional Environment Governance Marginal Key: =outputs =feedbacks and user learning

Fig. 7 Bio-Econ. Institutional Framework (Sources: Adapted from Ostrom and Cox, 2010; Williamson, 2000) Models Exogenous Influences & Stochastic Events Biophysical- Technical Community Attributes Institutional Embeddedness Institutional Environment Stocks, growth, resilience Actors, objectives, activities, constraints Action Situations Interactions (Markets, prices, information, expectations, aggregation, social networks, bargaining ) Outcomes Governance Marginal (Production, other ecosystem services, income, +/- stocks, latent demand for institutional change )

Fig. 7 Bio-Econ. Institutional Framework (Sources: Adapted from Ostrom and Cox, 2010; Williamson, 2000) Models Exogenous Influences & Stochastic Events {Policy & external shocks} Biophysical- Technical Community Attributes Institutional Embeddedness Institutional Environment Governance Marginal {Biophysical-technical feedbacks depend on cycle lengths and buffers, and can be modelled as recursive, inter-temporal, dynamic recursive, or stochastic processes} Action Situations Interactions {Socio-economic feedbacks depend on transaction costs, political power, and information asymmetry, and can be modelled as different types of institutional change processes } Outcomes

Table 2 Types of Bio-Economic Models &Institutions Models Model Type Pros Cons Applicability Institutions Optimization Optimization Equilibrium No Feedback (but, can discount) Parameters Constraints Bayesian Network Models risk No Feedback Limited heterogeneity Discrete process Uncertainty X-sector linkages Nodes Probability Systems Dynamics Inter-connections between sectors No autonomy No adaptablty/learning No evolution X-sector linkages Parameters Evolutionary Can adapt/learn Can evolve/feedback No autonomy Long time scale Parameters Agent- Based Interact/Autonomy Can adapt/learn Can evolve/feedback Non-equilibrial Maybe inconsistent theoretical underpinnings Scaling-up X-sector linkages Parameters Dependent variable

Table 3 Ex. of Explicit Integration of Institutions Models G R O U P Example Model Type Depicting Institutions HHs micro-credit, Asia (Ngo & Wahhaj, 2012) optimization Intra-hh bargaining Collect ives Networks Markets Property Rights X-Sector Linkages Policy thresholds rangelands, Africa (McCarthy et al., 2003) empirical strategic interaction Ind. / group PES on CA (Narloch et al., 2012) experimental Motivational crowding tech. diffusion, disease, endogenous networks & macro growth (Fogli&Veldkamp,2013) targeted transfers, tech. adoption & poverty (Chantarat & Barrett, 2012) evolutionary optimization Benefits & costs of networks Endogenous social networks irrigation, USA (Carey & Zilberman, 2002) optimization market interaction fertilizer pollution permits, Australia (Heckbert, 2011) agent-based fixed rule/marg. cond./hetr. inter. rangelands, Africa (Swallow & Bromley, 1994) optimization strategic interaction crop-forest, Asia (Fernandez, 2006) optimization strategic interaction Irrigation, drinking water, shellfish, France (Mongruel et al., 2011) systems dynamics fertilizer, France (SEAMLESS-IF, van Ittersum, 2009) IntFramework n/a conservation, Australia (Whitten & Bennett, post 2004) optimization fixed rules social choice

institutions into bio-economic models 1. Hypothesis: institutions play important roles in defining existing and alternative contexts for interaction among agents 2. Use theory and / or evidence to depict how interactions are defined by institutions (Game theory, social network theory, ethnographic research, experimental economics, expert opinion) 3. Develop conceptual model to generate hypotheses about effects of institutions or institutional change 4a. Collect data & test hypotheses empirically (survey or experiment) 5a. Policy analysis with validated statistical model 3b. Develop & specify empirical model, with some institutions fixed, others varying discretely (eg. x = 0, 1), others varying as parameters (0 x /1) 4b. Solve and / or simulate model and validate by comparison with reality and / or expert opinion 5b. Policy analysis with validated simulation or optimization model

Table 4 Modeling Approaches & Tradeoffs Models Resource/Capital Approach Tradeoffs Examples Low complexity on I side frees up capacity for complexity on biophysicaltechnical side. Simplification may decrease accuracy for policy guidance. Chantarat & Barrett (2012) Fogli & Veldkamp (2013) Constraint (Fixed-Rule) Heckbert (2011) Mongruel et al. (2011) Marginal Incentives Heckbert (2011) Structuring Interactions High fit Proswith reality increases accuracy for policy guidance. High Cons complexity on Institutional side may reduce capacity for complexity on biophysicaltechnical side. Market Interactions Carey & Zilberman (2002) Heckbert (2011) Strategic Behavior Fernandez (2006) McCarthy et al. (2003) Narloch et al. (2012) Ngo & Wahhaj (2012) Swallow & Bromley (1994) Interactions of Hetero. Heckbert (2011) Whitten & Bennett (post2004)

Conclusions: Best Practice Conclusions 1. Clarify Theoretical Foundations and Links between Theory and Practice 2. Simplify Institutions into Constraints or Parameters 3. Characterize Unobservable Behavior with New Tools (e.g., field experiments) 4. Sustain and Integrate Modeling Efforts Source: World Fish Source: ILRI Source: ICRAF

Conclusions: Best Practice Conclusions 1. Clarify Theoretical Foundations 2. Simplify into Constraints or Parameters Implicit Institutions Explicit? Explicit 3. Characterize Unobservable Behavior with New Tools Fixed Varying Partly Endogenous 4. Sustain and Integrate Modeling Efforts