The rate of return to public sector agricultural R&amp;D in the UK, 1965–80

1996

This study is the first UK application of the integrated, single‐stage approach to estimating the returns to R&D. Fitting a residual profit function, which incorporates the technology variables, to new data for UK agriculture for 1954–1990 produces short‐ and long‐run estimates of the output supply and input‐demand price elasticities, elasticities of the effects of relaxing the non‐variable input constraints and shadow prices for these non‐variable factors. All of the supply and demand relationships are found to be inelastic, but the price‐responsiveness of the system has increased over time. The long‐run model, in which all of the inputs except land are treated as variable and private‐sector research is included, gives a marginal rate of return to public agricultural R&D of only 18 per cent. Estimates of the factor‐saving biases of the technology variables suggest that public R&D has had its greatest effect in the animal sector.

Section: Shadow Prices and The Returns To Randdmentioning

confidence: 55%

Section: Datamentioning

confidence: 99%

SUPPLY AND DEMAND FUNCTIONS FOR UK AGRICULTURE: BIASES OF TECHNICAL CHANGE AND THE RETURNS TO PUBLIC R&D

Khatri¹,

1996

“…The lack of data limited the tests that could be performed, so the results should be treated with caution, but it does seem that short and long lags are just as important as those of four to seven years, which is when the peak effect should be found, according to conventional wisdom. The inverted U-shaped polynomial lags and the alternative lag structures used in the returns to R&D literature (see Thirtle and Bottomley, 1989) seem to fit these data poorly. We return to this problem after testing the pooled sample, in the next section.…”

Section: Granger Causalitymentioning

confidence: 99%

Cointegration, and Causality: Exploring the Relationship Between Agricultural and Productivity

Schimmelpfennig¹,

1994

Self Cite

This study investigates the causal relationships between total factor productivity (TFP) and explanatory variables, such as public sector agricultural extension, farmer education, private sector patents and the weather. Cointegration and Granger causality tests are applied to the UK data which were used by Hallam (1990). Unlike Hallam, we find that there is a relationship between research spending and productivity. The same methodology is applied to new data for ten EC countries and the USA. In all cases there is evidence of a long run relationship between TFP and. Pooling the data for the ten EC countries and the USA, and then testing for causality shows that expenditures are Granger prior to TFP and that TFP is also Granger prior to expenditures. This result agrees with Pardey and Craig's (1989) US study.

“…The fixed factors, such as land, are treated as having a rental price for the service flow they produce. In the second stage of this approach, TFF' changes are explained by the determining variables in the E vector (see Thirtle and Bottomley, 1989).…”

Section: Theorymentioning

confidence: 99%

Total Factor Productivity in Uk Agriculture, 1967‐90

Bottomley

1992

Self Cite

Indices of total factor productivity (TFP) measure aggregate output per unit of aggregate input, providing a guide to the efficiency of agricultural production. This paper outlines the relationship between production functions and TFP indices. An index for the period 1967‐90, constructed from the UK aggregate agricultural accounts, shows that TFP grew at an average rate of 1.9% per annum and that TFP growth has increased since the UK joined the European Community. At the aggregate level, this change seems to be explained by increased aggregate output and decreased aggregate input, in about equal proportions. Disaggregation shows the intensification effect of the CAP price regime. There has been rapid growth in the output of farm crops, relative to other enterprises, and in the use of agricultural chemicals.