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Record W2301921911 · doi:10.14288/1.0105602

A measurement of the effect of technical change on the productivity of the Canadian agricultural sector : 1926-58

2011· article· en· W2301921911 on OpenAlexaboutno aff
Horace Raymond Carby-Samuels

Bibliographic record

VenuecIRcle (University of British Columbia) · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureProductivityAgricultural economicsNatural resource economicsAgricultural productivityTechnical changeEconomicsEnvironmental scienceBusinessEconomic growthGeography

Abstract

fetched live from OpenAlex

This study is designed to measure the amount, and character of the change in the economic performance of the agricultural sector of the Canadian economy, over the period 1926 to 1958. The major hypothesis which it examines is that changes in technology have had a positive effect on the productivity of factors used in the agricultural sector of the Canadian economy. Technological change is defined herein as any change which produces a shift in the production function of the industry, and the concept of the relative shares going to each factor of production has been used to determine the shape of the aggregate production function. In the agricultural industry natural phenomenon such as weather, which are outside the control of management, are capable of causing significant variations in annual output. These fluctuations in output make it difficult to determine the permanent effects of technological change on input-output ratios. However, the analysis assumes that farmers take into account the possible effects of weather in their decision-making processes, and also assumes that the effects of variations from this source will be randomly distributed over the 33 year period of the study. The production functions were determined by first assuming that Euler’s Theorem holds for the agricultural industry. The relative shares of each factor in total inputs were then used as the estimates of the relative share of each factor in total output. By this method the shape of the aggregate production function was determined with a minimum of distortions which may be due to the effects of exogeneous variables. The determining of the aggregate production functions in the manner used is feasible because of the high degree of competition in the agricultural industry, which contributes to a much lower level of product or process differentiation than that which exists in other industries. The problems of aggregation of products are therefore in this case minimized. The level of aggregate output was determined by expressing as an index the value series which was obtained by deflating the total value of each type of product by an appropriate price index which had a 1935-39 base. The annual deflated values were aggregated; and this output series was then expressed in terms of its value as at 1926 being made equal to 100. These indices of deflated dollar values were used in the production function as estimates of the physical quantity of the output for that particular year. A similar process was used to determine the quantity of each particular factor input which was included in the production function. The quantity of aggregate inputs, however, was determined by a geometric aggregation of the indices of the factor inputs. In this aggregate the quantity of each factor was weighted by its relative share in the aggregate input (hence output under the assumptions of the analysis). This method of geometric aggregation of inputs gives due recognition of the fact that changing marginal rates of substitution of factor inputs are possible in the agricultural industry, and that at different levels of output farmers may effect a change in the marginal rates of substitution of factors without necessarily producing a shift of the production function on which they are operating. The data for the analysis were obtained from D.B.S. material, and the changes in the output-input ratios were examined in a model of comparative statics. When this was done, it was found that when capital was measured inclusive of real estate and other improvements the net change in over-all productivity for the entire period (1926-58), was not substantially different from that which was calculated when capital was measured net of the value of these factors. Between 1926 and 1958, the net change in over-all productivity was 33 per cent when capital was measured inclusive of real estate, and 31 per cent when the value of real estate was removed. Approximately 90 per cent of this observed increase had been recorded between the years 1946 and 1958. Over the period 1950 to 1956 the difference in the calculated measure of overall productivity from each method showed, however, differences of between 3 and 6 percentage points when compared to the more frequently found difference of 2 percentage points in favour of productivity when it was derived from data which included the value of real estate. Technological change between 1926 and 1958 has been associated with an estimated decline of 28 per cent in the quantity of labour in use, a 15 per cent increase in the quantity of total capital as compared with a 43 per cent increase in the quantity of non real estate capital, and a 115 per cent increase in the quantity of annual non-labour expenses. These expenses are chiefly associated with the use of farm machinery and to a lesser extent with that of fertilizer. Feed and seed were omitted from the calculations in order to avoid the problems of double counting. The results of the analysis substantiate the main hypothesis of this study, in that technological change has a positive effect on the productivity of factors used in the agricultural sector of the Canadian economy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.147
Teacher spread0.119 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2011
Admission routes1
Has abstractyes

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