Lecture 5 scenario design for regional demand system
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Lecture 5 Scenario Design for Regional Demand System. Laixiang Sun LUC, IIASA, Austria SOAS, University of London, UK. CHINAGRO 2 nd Training Course 24 Sep. 2003, CAS-CCAP, Beijing. Outline. The basic of demand system in an AGE setup. Why must the design be systematic?

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Lecture 5 scenario design for regional demand system l.jpg

Lecture 5 Scenario Design for Regional Demand System

Laixiang Sun

LUC, IIASA, Austria

SOAS, University of London, UK

CHINAGRO 2nd Training Course

24 Sep. 2003, CAS-CCAP, Beijing


Outline l.jpg
Outline

  • The basic of demand system in an AGE setup.

  • Why must the design be systematic?

  • What can we learn from households surveys?

  • What can we learn from international comparison?

  • Our approaches to have a systematic design.

  • Concluding remarks.


1 basic of demand system in an age setup l.jpg
1. Basic of demand system in an AGE setup

1.1. Linear expenditure system: Most convenient (discrete in time) setup for scenario design

  • Choose Stone-Geary utility function for each individual consumer:

  • Maximising utility s.t. budget constraint yields the linear expenditure system:


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1. Basic of demand system in an AGE setup

1.2. Relationship between elasticities & expenditures

Partially differentiating the LES yields these relationships:

  • In econometric analysis, we use households expenditure pattern to estimate elasticities.

  • In scenario design, we involve in a reverse process: Use acceptable future elasticities to establish future expenditure patterns (various shares).


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2.Why must the design be systematic?

  • Fine tuning income elasticities is not sufficient.

    • It may violate consistent and constraint conditions given before (Section 1), including “adding-up, symmetry, homogeneity, and non-negativity”.

    • It may lead to infeasible marginal shares of expenditures.

    • Troublesome Engel properties.

  • The typical problems of translating cross-section patterns into time-series patterns.

    • The case of consumption vs saving in USA.

  • Is it possible to have a systematic fine-tune?

    • We may need more help from plural perspectives.


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3.What can we learn from surveys?

  • Estimate current patterns of consumption and expenditures across regions, rural and urban divisions, and income groups (an example from CCAP’s tables).

    • Various shares.

    • Matrixes of elasticities (w.r.t. price, expenditure, and income).

  • Understand the limitation of the estimation based on cross-section or pooling data.

    • Same utility function

    • Same probability distribution

  • The estimates are suggestive or illustrative, but not deterministic!


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Source: CHINAGRO Working Package 1.7: Income Growth and Life-style Change, by CCAP-CAS


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3. What can we learn from international comparison?

  • Estimate consumption patterns across the development spectrum (different p.c. GDP levels).

    • Difficulty: Engel curves across development spectrum is non-linear.

    • Marginal and average budget shares are also non-linear across development spectrum.

  • These non-linearity is of fundamental importance for demand scenario design and analysis!


Example 1a average fitted budget shares for food products at mean ppp prices 1985 l.jpg
Example 1a: Average (fitted) budget shares for food products (at mean PPP prices, 1985)

Reference: “Changes in the Structure of Global Food Demand”, by J. Cranfield, T. Hertel, J. Eales, & P. Preckel, Purdue University, 1998.


Example 1b marginal budget shares for food products at mean ppp prices 1985 l.jpg
Example 1b: Marginal budget shares for food products (at mean PPP prices, 1985)

Reference: “Changes in the Structure of Global Food Demand”, by J. Cranfield, T. Hertel, J. Eales, & P. Preckel, Purdue University, 1998.


Example 2 non parametric estimation of meat demand and per capita income 1975 97 l.jpg
Example 2: Non-parametric estimation of meat demand and per-capita income (1975-97)

Reference: “Can We Feed the Animals? The Impact on Cereal Markets of Rising World Meat Demand”, by M. Keyzer, M. Merbis, I. Pavel, C. van Wesenbeeck, SOW-VU, 2003.


4 our approaches to have a systematic design l.jpg
4. per-capita income (1975-97)Our approaches to have a systematic design

4.1. Basic Strategy

  • Run estimations and simulations based on AIDADS or extended LES with switches to establish relationship between consumption patterns (shares and expenditure elasticities) and income growth.

  • Incorporate this externally calibrated relationship into the AGE with Stone-Geary form of utility function.

  • The relationship can also be projected to the time dimension, with the help of an externally calibrated income growth patterns across regions, rural & urban divisions, and income groups.


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4. per-capita income (1975-97)Our approaches to have a systematic design

4.2. Basic on AIDADS

  • AIDADS stands for An Implicit, Directly Additive Demand System.

  • It has been regarded as the “best practice” benchmark model to detect the relationship between consumer demand and income growth.

  • It starts from an implicitly directly additive utility function as follows.


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4. per-capita income (1975-97)Our approaches to have a systematic design

4.2. Basic on AIDADS

  • Solving the 1st order cost minimization conditions yields the budget share form:

  • If αg = βg for all g, AIDADS simplifies to the LES.

Reference: “Estimating consumer demands across the development spectrum: Maximum likelihood estimates of an implicit direct additivity model”, by J. Cranfield, P. Preckel, J. Eales & T. Hertel. Journal of Development Economics, 68 (2002), 289-307.

“Projecting world food demand using alternative demand systems”, by W. Yu, T. Hertel, P. Preckel, J. Eales, Purdue University, 2002.


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4. per-capita income (1975-97)Our approaches to have a systematic design

4.3. Basic on extended LES with switches

  • Demand function is as follow

  • The indirect utility function of this system has close-form expression and meets the requirements.

  • Its marginal and average expenditure shares changes across the switching points.


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5. Concluding remarks per-capita income (1975-97)

  • Fine tuning income elasticites alone may lead to inconsistency and a systematic scenario design of demand system is needed.

  • Systematic design means to integrate plural perspectives and best-available information into a consistent framework. Consistency across income levels (or over time) is essential.

  • Given the fact that improvement in data and estimation models/techniques is evolutionary, improvement in scenario design will follow the same track as well.


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