Estimation of time-varying price elasticity in 1970–1997 Japanese raw milk supply by structural time-series model

The price elasticity of raw milk supply in Japan is estimated over time by a structural time-series model in state space form, and the changes in elasticity are tested by the diagnosing test of de John and Penzer in 1998. The supply function is modeled with a local linear trend and a strictly exogenous autoregressive distributed lag price. The seasonality in the data is adjusted by a structural time-series model. In the 1970–1997 period, there were major dairy policy changes in Japan: a production adjustment, related penalties, and two-tier pricing. Hokkaido is the region that mainly provides the supply of raw milk for milk products, while Tofuken is the region that mainly produces drinking milk. The estimated price elasticity in Hokkaido became stable after imposition of the production adjustment. On the other hand, price elasticity in Tofuken became stable after the inflation of the mid-1970s. Price elasticity is estimated to be more inelastic as well as more likely to converge in inelasticity in Hokkaido than in Tofuken. We discuss the relationship between changes in dairy policy and changes in price elasticity.     

Consistent poverty comparisons and inference

Building upon the cost of basic needs (CBN) approach, an integrated approach to making consumption-based poverty comparisons is presented. This approach contains two principal modifications to the standard CBN approach. The first permits the development of multiple poverty bundles that are utility consistent. The second recognizes that the poverty line itself is a random variable whose variation influences the degree of confidence in poverty measures. We illustrate the empirical importance of these two methodological changes for the case of Mozambique. With utility consistency imposed, estimated poverty rates tend to be systematically higher in rural areas and lower in urban areas. We also find that the true confidence intervals on the poverty estimates—those incorporating poverty line variance—tend to be considerably larger than those that ignore poverty line variance. Finally, we show that these two methodological changes interact. Specifically, we find that imposing utility consistency on poverty bundles tends to tighten confidence intervals, sometimes dramatically, on provincial poverty estimates. We conclude that this revised approach represents an important advance in poverty analysis. The revised approach is straightforward and directly applicable in empirical work.