Lecture 5 Scenario Design for Regional Demand System

1. 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

2. 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.

3. 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:

4. 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).

5. 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.

6. 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!

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

8. 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!

9. 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.

10. 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.

11. 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.

12. 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.

13. 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.

14. 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: • 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.

15. 4. 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.

16. 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.