The Two Kornai Effects

Kornai's theories of a “soft budget constraint” study a firm that receives state assistance when expenses exceed revenues. Such assistance is characteristic of classical socialism. It is well known that Kornai contended that a soft budget constraint increases input demands. He also predicted a second effect that has received less attention: input demands exhibit lower price responsiveness. The Second Kornai Effect may be defined in terms of slopes of input demands as Kornai did, or in terms of elasticities. A simple soft budget constraint model is used to review the First Kornai Effect and to examine the two versions of the Second Kornai Effect.     

Learning from Doing Participatory Rural Research: Lessons from the Peak District National Park

Understanding the socio‐economic and environmental implications of rural change requires the active participation of many research disciplines and stakeholders. However, it remains unclear how to best integrate participatory and biophysical research to provide information useful to land managers and policy makers. This paper presents findings of a RELU scoping study that has formulated and applied a research framework based on stakeholder participation and adaptive learning to model rural change in the Peak District National Park in the north of England. The paper describes a learning process that integrates different types of knowledge to produce future scenarios that describe possible economic and environmental changes due to a national review of burning practices on heather moorland and blanket bogs. We stress the need for using social network analysis to structure stakeholder engagement and outline how a range of participatory approaches can facilitate more inclusive environmental planning and policy development.     

THE MEASUREMENT OF HOUSING OUTPUT: U.S. AND SOVIET CASE STUDIES

This paper examines the measurement of the output of the Housing industry in real GNP accounts of the U.S., the Soviet Union, and selected OECD countries. These real GNP accounts make use of quite different Housing indexes, based on different types of data. This paper's major empirical finding is that the (measured) growth rate of Housing output can be extremely sensitive to the type of index used.
After reviewing the concept of housing quality, the paper presents U.S. and Soviet case studies. The BEA and the CIA do not use identical procedures to measure Housing output for the U.S. and the Soviet Union: the BEA measures many more aspects of housing quality improvements than the CIA does. This difference in the two agencies' procedures increases the growth rate of the US. Real Estate industry relative to the growth rate of the Soviet Housing industry. The idea behind the two case studies is to remeasure Housing output far the Soviet Union (U.S.) using an index that approximates the BEA (CIA) index. The purpose of these studies is the calculation of numerical magnitudes: to what degrees are the levels and growth rates of Housing sensitive to the type of index that is used. The calculations for the U.S. are useful because they show the important role of housing quality growth in the U.S., and because they make the magnitudes reported for the Soviet Union more credible. The Soviet case study provides numerical support for the proposition that the post-WWII growth rate of Soviet housing quality has been considerable and exceeds the growth rate implicit in the CIA output figures.