Richard HowittSiwa Msangi

28 April – 2 May, 2014ICRAF Campus, Nairobi

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

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-levelMulti-market partial- and

general-equilibrium models

Growth models

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

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

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

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

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

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

Thank you!