Production function analysis of small rubber farms in Sri Lanka
Date
1976
Authors
Teo, Choo-Kian
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Abstract
The main purpose of this study has been to estimate the Cobb-
Douglas production function from the cross-section input-output data of
small rubber farms in the Agalawatta district of Sri Lanka. Twelve
factors of production believed to affect output of rubber were identified,
out of which four factors, namely, planting density, farm size,
tapping frequency, and tapping age of the trees were considered in the
estimating equation. The function was estimated for clone PB86 and
clone Tjir 1 separately; and for each clone, the Cobb-Douglas function
was estimated by two different techniques, namely, the Ordinary Least
Squares method which estimates the average function, and the Linear
Programming method which estimates the best or the frontier function.
As a first step in the analysis, simple correlation and simple
regression methods were employed with a view to bringing out relationships
between each of the specific factors and the output per acre.
Next, the relationship between the four factors of production and the
output of rubber was examined within the framework of multiple
regression analysis. The estimated coefficients were used to predict output per acre for
each clone. For both clones, the results strongly suggested that for
every farm or group of farms for every year or group of years, there is
a separate yield curve. Instead of a single production function, there
exists a family of functions depicting various efficiency levels.
For both clones, the estimated coefficients of the frontier function
indicated that the frontier had shifted non-neutrally upward from the
average function. Vectors of technical efficiency relative to the average and relative to the frontier were generated. The results showed that there existed a wide range of technical efficiency at farm level. This big difference in efficiency could be due to soil quality, management, or even be due to errors of measurement in the variables used for the production function analysis. It is clear that further research is needed to clarify the true meaning of these "efficiency" indices as applied to the rubber smallholders. When the efficiency ratings from the average and the frontier functions were compared, it was found that the ranking of the farms were similar issrespective of whether the ratings were calculated relative to the average or to the frontier function. Marginal returns to factors of production for individual farms revealed that was no significant relationship between farm size and the marginal returns to the land. Under the assumption of perfect markets, and assuming that the price of rubber was Rs. 1.00 per pound and the average wage rate was Rs. 5.00 per day, it was found that for PB86 farms, the majority of the smaller farms had overused the tapping labour, and nearly all the larger farms had underutilized this input; for Tjir 1 farms, all the smaller farms and about 50 per cent of the larger farms had overused the tapping labour, and underutilization of tapping labour occurred only in the larger farms. Underutilization of labour in larger farms, which were thought to be held by absentee landlords, could be due to a shortage of labour or an unattrractive nature of the share-arrangement or the wage-payment. Since there were substantial amounts of non-rubber crops such as paddy, coconut and tea in the area, it may have been a mistake to ignore these crops in an economic survey and to obtain information relating only to rubber. Hence, great caution is needed when using the analysis from such a survey to give any crop=specific advice because the information could be quite irrelevant in a mixed enterprise situation.
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