Integrating Agri-Environmental Indicators and the OECD Policy Inventory

1. Integrating Agri-Environmental Indicators and the OECD Policy Inventory By Ralph E. Heimlich OECD Workshop March 19-21, 2007 Washington, DC

2. A Vision of Agri-Environmental Policy Development Two contexts for analysis: Inter- and Intra-National Inter-National-analyze relationships between more aggregate agri-environmental indicators (AEIs) and policies across countries • Observations from many countries • Abstracts from or controls for differences in policy implementation and physical, climatic, cultural, economic, and political context across countries • Objective: Which policies work best to improve the AEIs? • Implicit: what works well in one or a set of countries will work well in others.

3. A Vision II Intra-National-analyze relationships between hierarchically disaggregated AEIs and policies within each member country • Disaggregates indicators and policies within a member country • Abstracts policies and programs or controls for context • Objective: Which policies work well in one area that could be applied to others? or What changes could improve efficiency and effectiveness? • Geographic disaggregation for understanding fine distinctions between • policies, • their parameters, • limitations of the resources and agricultural production practices to which they apply.

4. Sectoral disaggregation Geographic disaggregation Industry-steel, agriculture, etc. Sector-crops, livestock Enterprise-corn for grain, hogs, etc. Technology-irrigated, no-till, BT corn Field-Tama silt loam, 2-5% slope,irrigate, no-till, Bt corn Hierarchical Disaggregation

5. Previous OECD Activities Modeling AEI/Policies • Causal graph analysis on data for nutrient balances provided “proof of concept”, but there remain severe data limitations, and problems with the model specification • Applied the OECD Policy Evaluation Model (PEM), specifically for Canada, to a set of alternative policy instruments on nitrogen balance • Three analyses (Swiss dairy production, Finnish arable crop and forestry production, and U.S. land retirement and tillage practices) using the Stylized Agri-Environmental Policy Impact Model (SAPIM) • A great many other analyses using country-specific modeling frameworks presented within the JWP framework. • These uses of ag sector programming models could be modified in a uniform way and used to produce coordinated analyses of uniform policies or examine the responsiveness of AEIs (constructed to be analogous with the OECD set) to policy change

6. The Indicators Won’t quarrel with details of current set, but focus on adapting them for use in policy analysis. Criticisms of AEIs Usefulness for Inter-national Analysis • Designed for international-specified at a high level of generality and aggregation, and a low level of detail and specificity. • Universality-does everyone have these problems? • Inherent and managerial effects-focus on what policy can affect • Scale-neutrality-all indicators should be normalized • Data issues • Do the data that support qualitative classes used in constructing the indicators measure the same things? • Monitoring design and coverage is likely inherently unequal. This probably leads to estimates with differing reliability across countries.

7. The Indicators II Criticisms of AEIs Usefulness for Intra-national Analysis • Hierarchical disaggregation-Can indicators (or analogs) be disaggregated to every geographical/ sectoral level? • Size and scale- Does the meaning of the indicator remain the same when disaggregated? • Methods of quantification- Indicators may need to be calculated differently as the size of the unit of observation decreases

8. The Policy Inventory Environmental Objectives • Agri-environmental policies affect more than one (all) environmental outcomes. • Environmental objectives are not mutually exclusive categories. • Make objectives consistent with/parallel to the AEIs. • Objectives should not mix up outcomes and methods, resources of concern and techniques. • “Generic/Broad Spectrum” is not useful- admission that there is no clear objective of the policy. • A Side Benefit: Direction and magnitude of entire vector of impacts on environmental outcomes is a step toward a cost/benefit framework.

9. RESOURCE: SOIL RESOURCE CONCERN: SOIL EROSION SHEET AND RILL WIND EPHEMERAL GULLY CLASSIC GULLY STREAMBANK IRRIGATION INDUCED SOIL MASS MOVEMENT ROADBANK/CONSTRUCTION      RESOURCE CONCERN: SOIL CONDITION SOIL TILTH SOIL COMPACTION SOIL CONTAMINATION   SALTS ORGANICS FERTILIZERS PESTICIDES DEPOSITION/DAMAGE DEPOSITION/SAFETY     RESOURCE: WATER RESOURCE CONCERN: WATER QUANTITY SEEPS RUNOFF/FLOODING EXCESS WATER INADEQUATE OUTLETS WATER MGT. IRRIGATION       SURFACE SPRINKLER WATER MGT. NON-IRRIGATED RESTRICTED FLOW CAPACITY (H20 Convey.)      RESTRICTED STORAGE RESOURCE: WATER RESOURCE CONCERN: WATER QUALITY GROUNDWATER CONTAMINANTS PESTICIDES NUTRIENTS ORGANICS SALINITY HEAVY METALS PATHOGENS SURFACE WATER CONTAMINANTS PESTICIDES NUTRIENTS ORGANICS SEDIMENTS DISSOLVED OXYGEN SALINITY HEAVY METALS TEMPERATURE PATHOGENS RESOURCE: AIR RESOURCE CONCERN: AIR QUALITY AIRBORNE SEDIMENT AND SMOKE PARTICLES       AIRBORNE SEDIMENT CAUSING CONVEYANCE PROBLEMS AIRBORNE CHEMICAL DRIFT AIRBORNE ODORS FUNGI, MOLDS, AND POLLEN       RESOURCE CONCERN: AIR CONDITION AIR TEMPERATURE AIR MOVEMENT (Windbreak Effect) HUMIDITY RESOURCE: PLANT RESOURCE CONCERN: SUITABILITY SITE ADAPTATION PLANT USE       RESOURCE CONCERN: CONDITION PRODUCTIVITY HEALTH, VIGOR, SURVIVAL RESOURCE CONCERN: MANAGEMENT ESTABLISHMENT/ GROWTH HARVEST NUTRIENT MANAGEMENT PESTS THREAT/ENDANGERED PLANTS RESOURCE: WILDLIFE RESOURCE CONCERN: HABITAT FOOD COVER/SHELTER WATER (QUANTITY & QUALITY)       RESOURCE CONCERN: MANAGEMENT POPULATION BALANCE THREAT/ENDANGERED HEALTH NRCS CONSERVATION PRACTICE PHYSICAL EFFECT WORKSHEET

10. The Policy Inventory II Types of Measures • Make explicit the spectrum of measures from least coercive through voluntary methods, quasi-regulatory measures, and on to the most coercive. (see graph) • Further disaggregate the taxonomy of payment types • Differentiate payments based on farming practices between cost-share and incentive. • Accommodate policies using a variety of measures by separating their component parts and assigning the level of resources committed to each.

11. Continuum of Policy Measures High Level of Coerciveness Low Range of Environmental Policy Measures

12. Incorporating AEIs and Policies Into Quantitative Models • Positive and Normative approaches • Econometric models; • Single equation • Multi-equation simultaneous systems • Inter-industry (Leontiev) models; • I/O models • CGE models • Ag sector programming models.

13. Representative Farm Models (SAPIM) A special case of programming models Principal advantage as a communications tool Because of diversity in agriculture, it would take a large number of representative farms to accurately portray even one sector in one region or country Useful for understanding, but not for estimating overall impacts

14. Coordinated Ag Sector Modelling • Activity level is the unit of production (acre, hectare, animal unit) • Activities embody dissaggregation of • Resources (soils, climate, etc.) • Sectors (crops, livestock enterprises, etc.) • Technology (tillage, fertilization, pesticides, irrigation, conservation practices, etc.) • Vector of AEIs is differentiated by activity, implied by dissaggregation • Develop and require: • A coordinated set of policy questions • Guidance on how to adapt the set of AEI’s

15. Conclusions The AEIs • Scale-or Size-neutral • Universally relevant • Sectorally and geographically dissaggregable • Measures of data quality for comparability The Policy Inventory • Focus on entire vector of environmental impacts • Don’t mix outcomes and methods • Eliminate the “catch all” • Make more parallel with the AEIs • Make continuum of coercivness more explicit as an organizing principle • Subdivide policies/programs based on their objectives and allocation of resources

16. Conclusions II Policy Analytic Approaches • Fit the analytic approach to the policy being analyzed:What policies does the JWP most want to analyze? • Let those who know best do the work • Develop a coordinated set of policy questions • Develop guidance on how to adapt the set of AEI’s to the questions • Let modelers in each member country (or group of countries) adapt existing disaggregated models for the analyses, • Conduct hybrid analyses that “cascade” results from one level of modeling to more and more dissaggregated levels. • A more “black box” approach that deemphasizes causality may be useful to develop reliable econometric estimates of coefficients between existing policies and the levels of the AEI’s

17. Reflections Policy development is highly articulated (many roles and many players) • Policy formulation (developing good questions)- NGOs, agricultural interests, political figures • Policy research (what are the relationships?) Universities, research agencies, consultants • Policy analysis (refining proposals, estimating effects on key outcomes) The Secretariat, upper agency officials, consultants • Policy making (cutting deals) politicians compromising on the results for competing objectives • Policy implementation (putting programs in place) agencies in member countries, international institutions

18. Reflections II The limits of policy analysis • Illuminating tradeoffs between agricultural production and environmental consequences. • Timely and to the point • Process allows for iteration and successive approximations • Inform at all points of policy development • Don’t defer input for the “perfect” analysis