Lecture 5 Scenario Design for Regional

Demand System

Laixiang SunLUC, IIASA, Austria

SOAS, University of London, UK

CHINAGRO 2nd Training Course

24 Sep. 2003, CAS-CCAP, Beijing

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

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:

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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!

Table 91. Demand elasticities in North urban China by income group, 1997-2001

Expenditure elasticities Income elasticities

Mean Low Middle High Mean Low Middle High

Rice 0.250 0.320 0.196 0.144 0.191 0.249 0.150 0.106

Wheat 0.295 0.310 0.195 0.405 0.226 0.241 0.149 0.299

Coarse 0.340 0.419 0.258 0.270 0.260 0.326 0.197 0.199

Processed 0.424 0.534 0.385 0.241 0.324 0.415 0.294 0.178

grain

Oil 0.244 0.348 0.175 0.079 0.187 0.271 0.133 0.058

Meat 0.553 0.702 0.561 0.354 0.423 0.546 0.428 0.262

Fish 0.514 0.639 0.574 0.311 0.393 0.497 0.438 0.230

Vegetable 0.339 0.409 0.315 0.250 0.259 0.318 0.240 0.184

Sugar 0.657 0.761 0.654 0.534 0.502 0.592 0.499 0.394

Fruit 0.575 0.706 0.599 0.405 0.440 0.549 0.457 0.299

Othfood 0.931 1.026 0.927 0.861 0.712 0.799 0.707 0.636

Clothes 0.868 0.940 0.859 0.793 0.664 0.731 0.655 0.585

Othnofood 1.386 1.399 1.391 1.372 1.060 1.088 1.062 1.012

Per capita: Expenditure 3088 5526 9347 Income 3649 6967 13041

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. 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. 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. Our approaches to have a systematic design

4.2. Basic on AIDADS Solving the 1st order cost minimization conditions yields

the budget share form:

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• 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. Our approaches to have a systematic design

4.3. Basic on extended LES with switches Demand function is as follow

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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

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.