Supply And Use Tables Research Articles

PROJECTION OF SUPPLY AND USE TABLES: METHODS AND THEIR EMPIRICAL ASSESSMENT

We present eight existing projection methods and test their relative performance in estimating Supply and Use tables (SUTs) of the Netherlands and Spain. Some of the methods presented have received little attention in the literature, and some have been slightly revised to better deal with negative elements and preserve the signs of original matrix entries. We find that (G)RAS and the methods proposed by Harthoorn and van Dalen (1987) and Kuroda (1988) produce the best estimates for the data in question. Their relative success also suggests the stability of ratios of larger transactions.

Joint Estimation of Supply and Use Tables

We propose a new biproportional method specifically designed for joint projection of Supply and Use tables (SUTs). In contrast to standard input-output techniques, this method does not require the availability of total outputs by product for the projection year(s), a condition which is not often met in practice. The algorithm, called the SUT-RAS method, jointly estimates SUTs that are immediately consistent. It is applicable to different settings of SUTs, such as the frameworks with basic prices and purchasers’ prices, and a setting in which Use tables are separated into domestic and imported uses. Our empirical evaluations show that the SUT-RAS method performs quite well compared to widely used short-cut methods.

A Shortcut Method for Generating Time Series of Input Data for Productivity Analysis

This paper outlines a simple method for generating long time-series of intermediate inputs by industries and products from supply and use tables (SUTs) that can serve as building blocks in productivity growth analysis. This backward extrapolation method is tested on a set of Dutch SUTs for the period 1987–2001. We find that our methodology delivers estimates of values, volumes and real growth rates of intermediate inputs that compare well with the ones from officially published tables. It generates a stable long series of tables based on a limited set of information. This method will hence be a useful tool for generating time-series of intermediate inputs, which are a crucial element in productivity analysis.