Parsimonious Technology Impact Assessment John M. Antle

1. Parsimonious Technology Impact Assessment John M. Antle Professor of Agricultural and Resource Economics Oregon State University Presented at Mich State Univ Sept 13, Purdue UnivSept 17 2010

2. There is a growing demand for assessment of economic, environmental and social impacts of agricultural systems in the US and around the world…

3. Example: NIFA 2010 call for proposals on Global Food Security: “For new CAP proposals, by the end of 5 years (or earlier), the project director and team are to report the estimated overall economic impact of the CAP activities, as well as other significant, relevant outcomes such as, but not limited to, behavioral, social, and, environmental.

4. Example: CGIAR donors Walker et al. (2008): “The desirability of moving … along the impact pathway is unquestioned. As donors want to see ever more comprehensive impact assessments, so ways have to be found to accommodate their wishes… even when resources for carrying out these … studies are not forthcoming.” (p. 14).

5. What do demanders of IA want? • Information needed to support improved decision making… • Timely: when are decisions made? • Relevant: what populations, indicators? • Sufficiently accurate to inform decisions • Credible: transparent & reproducible • Cheap: cost must be small share of research budget

6. What do demanders of IA want? • Most research models are not good tools to provide the information decision makers want and need • Emphasize basic processes to further research objectives & publish for peers • Not designed to improve understanding of outcomes relevant to stakeholders and decision makers • Too complex, data intensive and costly to be both timely and relevant • Not transparent and often difficult or impossible to reproduce, so not credible to stakeholders

7. Most impact assessment follows the “ex post” model Is this a suitable paradigm to respond to the demand for IA? (Walker et al 2008)

8. How to “move along the impact pathway?” • “Multidimensional impact assessment” • Conduct household surveys • Design & execution takes 2-3 years or more • Econometric analysis of adoption • Complex household models, linked to process-based environmental models • “Representative” farm models – heterogeneity? • Spatially explicit models: more time and data… • But is more complexity the answer? Not feasible for most projects! • Too costly in time and money • Increasing cost, diminishing returns to complexity

9. An alternative view: • Technology development, adoption and assessment should be an interconnected, iterative process, not a linear process • IA should be an integral part of project design and implementation, rather than “ex ante” or “ex post” Project design Project implementation Impact assessment

10. Parsimonious Technology Impact Assessment (PTIA) • Meet stakeholder demands for IA by quantifying changes in relevant indicators associated with adoption of new production systems • Timeliness & low cost achieved by • using available data (economic, experimental, model-generated) • targeting limiting data collection to key parameters • integrating data collection into research programs.

11. Production Systems and Technology Adoption Consider a population in a geographic region using system 1. System 2 becomes available for adoption. Define a function (s) which orders all sites s, such that  > a for those using system 1 and  < a for those using system 2. The adoption rate of system 2 is The adoption rate of system 1 is r(p,1,a)  1 – r(p,2,a). E.g., let n(p,s,h) be the present value of expected returns to system h, then(s) n(p,s,1) – n(p,s,2) is the opportunity cost of changing systems. Note, (s) could be farm size or some other ordering.

12. Outcome Distributions and Indicators For a population in a geographic region using system h = 1,2, economic outcomes v and environmental or social outcomes z follow (v,z | p,h). Define the indicator For example, k could be a poverty line, and then (k) = 1 gives the headcount poverty index; letting (k) = (k – k)/ kproduces an indicator similar to the “poverty gap” described by Foster, Greer and Thorbecke (1984).

13. Price-Based Tradeoffs and Adoption-Based Tradeoffs among Econ, Env & Social Indicators • Goals: • construct indicators as functions of adoption rate r and outcome distributions of adopters and non-adopters • parameterize adoption model and outcome distributions Key Implication: impacts cannot be assessed independently of the adoption process unless they are uncorrelated in the population

14. Adoption model: spatial distribution of ω ω > 0 (non-adopters) ω < 0 (adopters)

15. , a Adoption rate varies with adoption threshold a ω > a0 a0 0 < ω < a0 r2 () 100 ω < 0 r (p,2,a0)

16. Variance of opportunity cost and adoption rates  • = 0 r2(%) () 0 50 100

17. Properties of Outcome Distributions • When all farms in a region use system h, then: • the joint and marginal distributions of v and z are defined as (v,z | p,h) and (k | p,h), for k = v,z • there is a joint distribution (ω,k| p,h) • For the sub-population using system h, there is a joint distribution between ω and k = v, z, truncated according to ω > a for system 1 and ω < a for system 2: • (ω,k| p,h,a) (ω,k| p,h)/r(p,h,a)

18. Confidence Ellipsoid for Joint Distribution of the Adoption Variable ω and an Outcome Variable k = v,z k = v,z k(2,0)  k(2,a1)  k d e  0 a1

20. Population-Level Indicators and Adoption-Based Tradeoff Curves

21. Example: headcount poverty indicator Base system income distribution (Poverty = 65%) Improved system income distribution (Poverty = 25%) r2% adopters r1% non-adopters Mixed income distribution (Poverty = 45% )

22. Implementation of PTIA using TOA-MD Design Population (Strata) System characterization Impact indicator design Data Opportunity cost distribution Outcome distributions Simulation Indicators and Tradeoffs Adoption rate

23. Empirical Implementation Using the MD Adoption Model

24. Achieving Parsimony in Model Implementation • Small number of parameters (means, vars, correlations) • can estimate with small # observations vs structural econometric models • Only changes between systems needed • Use model to design data collection as part of project • E.g., on-farm yield trials, cost data • Focus data collection on key parameters • Use sensitivity analysis to assess need for additional data, e.g., correlations between returns and outcome variables • Can use existing system data + experimental and modeled data to construct analysis of new systems • e.g., adaptation to climate change

25. Case study: Machakos, Kenya

26. Heterogeneity in landscape and systems

28. Mean Nutrient Loss and Mean Farm Income Tradeoffs: Full-Data Model (FD) Price Tradeoffs, and Minimum-Data Model (MD) Adoption Tradeoffs, Machakos, Kenya.

29. Risk of Nutrient Loss and Headcount Poverty: Full-Data Model (FD) Price Tradeoffs, and Minimum-Data Model (MD) Adoption Tradeoffs, Machakos, Kenya.

30. For more info… • tradeoffs.oregonstate.edu • TOA-MD V.4 available to download in Excel (ecosystem services model) • TOA-MD V.6 available soon in Excel • Whole farm model (crops, livestock) • Poverty, environmental and social indicators