The Introduction of the Cobb-Douglas Regression and Its Adoption by Agricultural Economists

Biddle, Jeff E.

doi:10.1215/00182702-1158745

Cited by 10 publications

(8 citation statements)

References 47 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results are in line with the manner in which the Cobb-Douglas model detects production efficiency in economic production theory. The model does this by identifying firms that are producing more output than the sum of their inputs suggest that they should be able to produce, when compared to an industry average as established by a regression equation (Biddle, 2011). This suggests that the excess production is attributable to a technological efficiency that the highly productive firm is employing and that the average and lower producing firms are not.…”

Section: Discussionmentioning

confidence: 99%

“…Known as the Cobb-Douglas model, this approach is used to study the relationship between a set of inputs and the quantity of output produced, which in turn can be used to measure production efficiency, including the impact of technological improvements (Biddle, 2011). The two studies examined in this paper take a similar approach by identifying the institutional inputs that go into producing scholarly research and regressing those measures against actual scholarly outputproducing an industry production function of sorts for academic scholarship.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Looking for Links: How Faculty Research Productivity Correlates with Library Investment and Why Electronic Library Materials Matter Most

Rawls¹

2015

EBLIP

View full text Add to dashboard Cite

the terms of the Creative Commons-AttributionNoncommercial-Share Alike License 4.0 International (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly attributed, not used for commercial purposes, and, if transformed, the resulting work is redistributed under the same or similar license to this one. AbstractObjective -This paper summarizes two studies that share the same research question: do universities produce more scholarly research when they invest more in their libraries? Research libraries spend a great deal of effort reporting their expenditures, collections statistics, and other measures that serve as a basis for interlibrary comparison and even rankings. The straightforward assumption implied by this activity is that libraries better serve their student and research communities when they are well-funded and well-resourced. The studies examined here both ask if that notion can be validated empirically, not because research libraries require some sort of justification, but because in an environment of tough budget decisions and shifting opinions about the changing role of libraries, it may be useful to demonstrate that sustained investment in libraries offers tangible returns or that the failure to do so can result in tangible costs.Methods -A cross-sectional design featuring ordinary least squares regression analysis was used in both studies to estimate the relationship between scholarly research productivity at U.S. doctoral institutions and an array of institutional characteristics presumed to influence that productivity. The concept of research productivity is operationalized as the total number of scholarly journal articles produced by each institution over a five year period -as journal articles represent the most common form of scholarly expression across the greatest number of academic fields. Serving as the dependent variable, this data was regressed against a variety of institutional Evidence Based Library and Information Practice 2015, 10.2 35 characteristics including faculty size, research expenditures, and grant awards, and several library variables centered mostly on expenditures. The concept behind this design is that to realistically explore the relationship between levels of library investment and research productivity, all other institutional drivers of research productivity must also be represented in the dataset. While the design was similar for both studies, they each drew on different data sources and marginally different populations.Results -Both studies found that an institution's research productivity is positively and significantly correlated with the level of investment it makes in its libraries. Furthermore, both studies found electronic library material expenditures to be particularly associated with increased productivity. This relationship was so strong that an institution's level of research productivity appears to be sensitive to how its library's collec...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Looking for Links: How Faculty Research Productivity Correlates with Library Investment and Why Electronic Library Materials Matter Most

Rawls¹

2015

EBLIP

View full text Add to dashboard Cite

show abstract

“…The Cobb–Douglas production function offers clear advantages when handling multiple inputs and also captures the key assumptions of diminishing returns (Coelli, ). When the explanatory variables are regressed, the coefficients produced can be directly and easily interpreted as elasticities of output in terms of farm inputs (Biddle, ). The stochastic production frontier was estimated for tilapia farming in China using the Cobb–Douglas functional form as specified below:

L n Y_{i} = β_{0} + \sum_{x = 1}^{9} β_{i} L n X_{ij} + V_{i} - U_{i},

where subscript i means the i th farm in the survey; Ln refers to the natural logarithm; Y is the farm production; and the X ij s are input variables accounting for all farm production costs.…”

Section: Methodsmentioning

confidence: 99%

Technical efficiency of different farm sizes for tilapia farming in China

et al. 2019

View full text Add to dashboard Cite

China is the world's largest tilapia producer. This study uses the stochastic frontier function in the Cobb–Douglas model to evaluate a production frontier for tilapia aquaculture and analyses the relationship between farm size and production efficiency in China. A random sample of 300 tilapia farmers was surveyed in the main tilapia‐producing areas to study their economic efficiencies. The percentage distribution of tilapia farm technical efficiency was determined, with an average efficiency of 79%, which showed that the sample tilapia farmers operate at 21% below the production frontier and hence that they still have a chance to achieve targeted yields. The technical efficiencies of two categories (≤1 ha and >1 ha) were 78.82% and 79.27% respectively. A second‐stage analysis investigated the relationship between farm size and technical efficiency and showed a positive relationship between farm size and production efficiency based on the ordinary least‐squares model. The optimal farm size was found to be 7.50 ha. This study proposes strategies such as enlarging the farming scale moderately, thereby achieving the advantage of economies of scale, and enhancing the competitiveness of the tilapia industry in China.

show abstract

“…The Cobb-Douglas model has been applied to measure the effect of access to tractor service on the technical efficiency of small-scale maize farmers. The model was established in 1927 by Charles Cobb and Paul Douglas (hence Cobb-Douglas), with the objective of understanding the relationship between output level and quantities of inputs used in production (Biddle, 2010). In its general form, i.e.…”

Section: Analytical Techniquementioning

confidence: 99%

Analysing the effects of access to tractor service on the technical efficiency of small-scale maize farmers in Mpumalanga Province: a case study of the Masibuyele Emasimini Programme

Sechube

Belete

Hlongwane

2020

J. Agribus. Rural Dev.

View full text Add to dashboard Cite

Abstract. The Purpose of the study was to analyse the effects of access to tractor service on technical efficiency among small-scale maize farmers following the implementation of Masibuyele Emasimini programme in Mpumalanga Province. Data collection was carried out in the districts of Mpumalanga province and these are: Ehlanzeni, Nkangala and Gert Sibande. Purposive sampling technique was used in identifying the three districts. A total of 101 small-scale maize farmers were purposively selected from the districts and used for the study. The study concluded that small-scale maize farmers do have access to tractor service rendered by the Masibuyele Emasimini programme, with 73% of farmers in Ehlanzeni and 60% in both Nkangala and Gert Sibande district that have access to the service, compared to 27% and 40% without access. Results indicate that there are socio-economic factors influencing small-scale maize farmers’ access to tractor service, for example, irrigation, inter alia. The Cobb-Douglas model found access to tractor service to be negative and insignificant in both Ehlanzeni and Nkangala district. The variable was however positive (although insignificant) in Gert Sibande. The study recommends that modern irrigation systems (preferably, sprinkler) should be provided as part of the programmes’ input list to contribute towards efficient maize production.

show abstract

The Introduction of the Cobb-Douglas Regression and Its Adoption by Agricultural Economists

Cited by 10 publications

References 47 publications

Looking for Links: How Faculty Research Productivity Correlates with Library Investment and Why Electronic Library Materials Matter Most

Looking for Links: How Faculty Research Productivity Correlates with Library Investment and Why Electronic Library Materials Matter Most

Technical efficiency of different farm sizes for tilapia farming in China

Analysing the effects of access to tractor service on the technical efficiency of small-scale maize farmers in Mpumalanga Province: a case study of the Masibuyele Emasimini Programme

Contact Info

Product

Resources

About