Biosight: Quantitative Methods for Policy Analysis using GAMS

1. Richard Howitt
Siwa Msangi
28 April – 2 May, 2014
ICRAF Campus, Nairobi

2.  Provide a survey of useful quantitative methods that can
be applied to address important problems in agricultural
and resource economics
 Provide a “tool kit” of various models that can be adapted
to address the issues of interest to researchers and other
partners
 Convey an understanding of the strengths and weaknesses
of various quantitative approaches, and the empirical
challenges that are entailed in applying them to real-world
problems
 Help strengthen the quantitative skills of the participants
and underline the economic foundations of the methods
being applied

3.  We will not be able to cover all 4 of these quadrants at the
same level of detail – but we will provide a roadmap for
how they are connected
Static Dynamic
Micro-level
Farm production
models
Resource extraction
models
Macro-level
Multi-market
partial- and
general-equilibrium
models
Growth models

4.  We will begin with the agricultural production
problem at the farm level, and understand how to
capture some key aspects of behavior --
especially the decisions of the producer to adjust
on either the intensive and extensive margins
 We will explore the duality that underlies the
production problem, and use it to derive the
demand or key resources such as land and water
 We will use these derived demands to define the
behavioral equations that can be used in larger
market models or to construct the benefit
functions that drive resource extraction behavior

5.  In all of the models that we will consider, we will assume
that agents (producers or consumers) are optimizing with
respect to preferences, defined objectives and under
constraints of limited resources
 In micro-level models, we can make the optimization
explicit in the structure of the model
 When moving to macro-level models, we have to make the
optimization of agents implicit in reduced-form equations
that represent the first-order conditions of optimizing
behavior
 Macro-models can sometimes contain the objective of the
social planner who is hypothesized to maximize combined
producer & consumer surplus -- although most do not
make this explicit

6.  Econometrics and programming approaches
◦ Historically these approaches have been at odds,
but recent advances have started to close this gap
 Where do we apply programming models?
◦ Explain observed outcomes
◦ Predict economic phenomena
◦ Influence economic outcomes
 Why a programming approach?
Day 1 NotesHowitt and Msangi 6

7.  Econometric Models
◦ Often more flexible and theoretically consistent, however
not often used with disaggregated empirical microeconomic
policy models of agricultural production
 Constrained Structural Optimization (Linear
Programming)
◦ Ability to reproduce detailed constrained output decisions
with minimal data requirements, at the cost of restrictive
(and often unrealistic) constraints
 Positive Mathematical Programming (PMP)
◦ Uses the observed allocations of crops (or livestock and
other activities) to derive nonlinear cost functions that
calibrate the model without adding unrealistic constraints
Day 1 NotesHowitt and Msangi 7

8.  Computable General Equilibrium (CGE)
◦ Used in macro-economic and sectoral applications,
represents markets across the entire economy
 Calibration using Entropy
◦ Suitable for ill-posed problems. Enables consistent
reconstruction of detailed flexible form models or
production functions on a disaggregate basis
 So, what is the best economic model to use?
Day 1 NotesHowitt and Msangi 8

9.  A better understanding of how various economic models
are related to each other – and where to apply them
 A strengthened understanding of the economic principles
underlying these models
 An appreciation for the empirical challenges in applying
these models, and how they can be addressed
 Some ideas for how your own research problems can
addressed with the use of some of these models
 An operational understanding of GAMS

10. Thank you!