Inverse Demand Systems and Welfare Measurement in Quantity Space

1997 ◽  
Vol 63 (3) ◽  
pp. 663 ◽  
Author(s):  
H. Youn Kim
1993 ◽  
Vol 25 (1) ◽  
pp. 217-227 ◽  
Author(s):  
Kuo S. Huang

AbstractA set of ordinary and inverse demand systems for U.S. quarterly meat consumption is estimated for use to measure the effects of U.S. meat trade on consumers' welfare. The approach is useful to incorporate all direct- and cross-commodity effects into price forecasting and the Hicksian compensating variation measurement.


1995 ◽  
Vol 20 (3) ◽  
pp. 519-530 ◽  
Author(s):  
Mark G. Brown ◽  
Jonq-Ying Lee ◽  
James L. Seale

2008 ◽  
Vol 37 (2) ◽  
pp. 243-256 ◽  
Author(s):  
Youngjae Lee ◽  
P. Lynn Kennedy

This study analyzes quantity impacts of imported crawfish tailmeat on Louisiana crawfish tailmeat prices, and demonstrates the statistical validity and proper interpretation of cross substitution within inverse demand systems. Among five inverse demand systems, the Differential Inverse National Bureau of Research (DINBR) model shows no violation of econometric assumptions for the data analyzed. By using Allais coefficients proposed by Barten and Bettendorf (1989), we show substitutability among the five fish species.


2001 ◽  
Vol 83 (1) ◽  
pp. 230-245 ◽  
Author(s):  
Robert H. Beach ◽  
Matthew T. Holt

2010 ◽  
Vol 39 (1) ◽  
pp. 22-36 ◽  
Author(s):  
Roger H. von Haefen

This paper demonstrates how corner solutions raise difficulties for the specification, estimation, and use of incomplete demand systems for welfare measurement with disaggregate consumption data, as is common in the outdoor recreation literature. A simple analytical model of consumer behavior is used to elucidate the potential biases for welfare measurement arising from modeling the demand for M goods as a function of M + N prices (N > 1) and income when individuals do not consume all goods in strictly positive quantities. Results from a Monte Carlo experiment suggest that these biases can be substantial for large-scale policy shocks when prices are highly correlated.


1983 ◽  
Vol 23 (3) ◽  
pp. 329-337 ◽  
Author(s):  
Kuo S. Huang

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