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Assessing Climate Forecast Impacts . Advancing Ex Post Methodologies. Mark W. Rosegrant Siwa Msangi Liangzhi You. The Growing Importance of Climate Forecast Information. Increasing frequency of extreme weather events and changing global trends in climate characteristics - PowerPoint PPT Presentation
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Mark W. RosegrantSiwa MsangiLiangzhi You
Assessing Climate Forecast Impacts
Advancing Ex Post Methodologies
The Growing Importance of Climate Forecast Information
Increasing frequency of extreme weather events and changing global trends in climate characteristics
The vulnerability of increasingly complex global economic and food systems to environmental factors
In the face of increasing uncertainty, policy makers are demanding better information
How To Measure Forecast Value?
Growing body of literature examining the economic value of forecast information
Theoretical underpinnings are grounded in the theory of decision-making under uncertainty
The majority of this literature employs ex ante methods
Ex Ante or Ex Post ?
A
(prior beliefs)
B(update beliefs)
(model possible response)
C(observe outcome of event) (also observe agent’s actions)
Receive Forecast Signal
Realized Climate Outcome
Ex Ante Ex Post
Modeled behavior
Simulated Benefit
Measured behavior
Realized Benefit
(simulated counter-factual)
Measuring Forecast Value
Within an Ex Ante framework, behavior is modeled and response is simulated to evaluate the net benefits of forecast
Within an Ex Post framework actual behavior is observed and underlying structural relationships driving response must be inferred to estimate the net benefits (comparing to without forecast information)
Overview of the Presentation
Look at Traditional Impact Assessment Discuss the Challenges of Ex Post
Evaluation Look at some promising directions Draw conclusions and
recommendations for advancing research in this area
Ex Post Assessment in Ag Research
Long history of application in empirical literature
Looks mostly at benefits of new technology
Evaluates net benefits with and without innovation
Employs a variety of empirical methods
Two Approaches in Ex Post Assessment
One approach tries to econometrically measure the impact of Ag Research on productivity or production costs with reduced form relationships
Another approach uses consumer welfare theory to relate technology improvements to benefits received by consumers and producers of the agricultural goods within the economy
Econometric Methods
Treating technology as an input, estimate production function, cost function or total factor productivity (TFP)
),,,( ttttt UKZXfQ Conventional Inputs (i.e. land)
Unconventional Inputs (i.e. infrastructure)
Technical knowledge (i.e.R&D investment)
Uncontrollable factors (i.e. weather)
Ag. Output
Econometric Methods
Estimated research coefficients are then used to calculate the value of additional output attributable to the lagged research expenditures ( marginal rate of return to the research investment)
Growth Accounting: contributions by the components in the above equation to the rate of growth of aggregated output
Basic Economic Surplus Model
e
Technology-induced supply shift (S0 to S1)
Total benefits (consumer and producer benefits) are Area of I0I1ab
Basic model can be extended to incorporate multi-markets, to accommodate spillover, to adjust for market distortions etc.
Lessons to Draw from the Literature
Scale (project, program, institution or the whole system)
Attribution (proper accounting for benefits and costs)
Selection bias (random sampling or “cherry-picking”)
Time lags (long lag between R&D investment and final impact)
Lessons to Draw from the Literature
Econometric methods rely on good-quality time series/panel data. More appropriate for entire research system rather than individual projects.
Economic surplus method requires limited data and flexible. It is widely used.
Challenges of Ex Post Assessment
Harder to take a ‘descriptive’ rather than ‘prescriptive’ approach
Non-excludable nature of climate information makes valuation harder
Must infer relevance of forecast from observed behavior which could be driven by a variety of factors
Dis-entangling the underlying structural relationships is non-trivial
Undertaking a Descriptive Analysis
1. Define decision alternatives and determine that the decision is weather-information-sensitive
2. Identify user’s goals
3. Identify all decision-relevant information available to user
4. Develop a model describing the relationship between available information and the decision
5. Evaluate the model. Does it adequately describe the user’s behavior?
6. Use the model to determine the impact of forecast on criteria
Economic Valuation Methods
Observed Behavior(ex post)
Hypothetical Behavior(ex ante)
DirectValuation
Experiments that measure response to better
information in a laboratory
Observed behavior under improved information from
collected data
Questions on willingness-to-pay for better forecasts
Simulated behavior under improved information,
assuming a behavioral model
IndirectValuation
Hedonic values generated by actual behavior within a
related market that can be tied to forecast information
Contingent ranking of attributes that can be indirectly related
to forecast
Direct Valuation Methods
An Ex Ante approach would rely on questioning the information ‘consumer’ directly
An Ex Post approach relies on the revelation of this value through behavior directly tied to climate shocks
Making the structural link between the climate shock and observed action is the challenge
Ex Post Approaches to Direct Valuation
Direct Valuation can be done using reduced-form relationships that derive statistical relationships between forecast information to behavior
Surplus values can also be computed An alternative is to look more closely at
the underlying structural determinants of behavior and estimate models that can link those to climate information
Reduced-Form Methods An econometric Ex Post analysis relies
on the statistical inference of forecast value from observables in both the physical and economic environment (e.g. land value and climate characteristics)
Controlling for non-forecast related factors that affect observed reaction to climate shocks (and information) is the principal challenge
Possible Reduced-Form Model
Relationship tying Farm profits (P) to climate information (K) and other on-farm characteristics
( , , , )t t t t tP f X Z K UConventional Inputs (i.e. land)
Unconventional Inputs (i.e. infrastructure)
Climate Information (knowledge sources)
Uncontrollable factors (i.e. weather)
Controlling for Behavioral Factors
Since the on-farm input levels are endogenous, they are instrumented
The ability of the farmer to adjust also should be accounted for
( , )t t t tX f Y G v
On-farm characteristics
(i.e. credit/labor constraints, farmer experience)
Exogenous factors (environmental, etc. )
Error term
Structural Estimation Approach
Is better able to connect the underlying drivers of response to climate information and environmental shocks than a ‘reduced-form’ approach
Better able to represent the constraints to agent behavior
Comes at a great computational cost as behavioral as well as environmental relationships must be estimated
Promising Trends
Despite relatively thin literature covering ex post methods, recent examples of innovative applications have surfaced
Cover a variety of settings, from pastoral management to crop production
Some methods empirical while others are experimental
A Few Case Studies
Solow et al. (1998) estimate net welfare from the use of ENSO-based climate information to range between $240 and $320 million annually for the U.S. agriculture sector alone
Bradford and Kelajian (1978) looked at the benefit-to-cost ratio of reducing sampling error in government-collected crop and livestock statistics
Livestock Management
Luseno et al. (2003) look at pastoralists in Northern Kenya/Southern Ethiopia
Compare pastoralists own perceptions with climate forecasts and observe how it influences their beliefs
They note the importance of taking flexibility into account, when measuring forecast value, to avoid underestimation
Experimental Approach
Sonka et al. (1988) use agribusiness example to show how decision experiments can be used to test response to varying levels of climate information
By using a controlled setting they are able to measure the impact of information more accurately and directly observe agent behavior
Comments and Critique
Despite the novelty of experimental methods, one may not be entirely sure that ‘real world’ behavior is being observed
Much depends on the design of the experiment and the ‘framing’ of the problem
But much insight can still be gained, and methods are apt to keep improving
Conclusions
Surplus-based methods might be applicable if designed properly
Structural estimation approaches give the most detail of the underlying relationships, but are the most challenging to apply, hard to generalize
Experimental techniques have increasing appeal and potential utility
Conclusions
Econometric methods would be valuable if reliable time series/panel data are available
Need to design panel or cross-sectional data collection efforts for evaluating climate forecast information (i.e. ASTI in agricultural R&D). Needs collaborative effort and long-term commitment.