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# Lecture 5 Scenario Design for Regional Demand System - PowerPoint PPT Presentation

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

Laixiang Sun

LUC, IIASA, Austria

SOAS, University of London, UK

CHINAGRO 2nd Training Course

24 Sep. 2003, CAS-CCAP, Beijing

• 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.

1.1. 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:

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).

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.

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!

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

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)

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)

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)

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. 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.

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.

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.

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.

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.