Professor Douglas' Production Function

1942 ◽  
Vol 10 (1/2) ◽  
pp. 37 ◽  
Author(s):  
J. Tinbergen
Keyword(s):  
Production Function ◽  
Douglas Production Function

Significance of Experience, Farm Size, Quantity of Propagules and Location f or Seaweed Farmers in the Divided Island of Sebatik

2018 ◽  
Vol 9 (9) ◽  
pp. 825-832
Author(s):  
James M. Alin ◽  
◽  
Datu Razali Datu Eranza ◽  
Arsiah Bahron ◽  
◽  
...  
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Production Function ◽  
Weather Conditions ◽  
Education Level ◽  
Farm Size ◽  
Total Production ◽  
Explanatory Factors ◽  
Douglas Production Function

Seaweed-Kappaphycus-Euchema Cottonii and Denticulum species was first cultivated at Sabah side of Sebatik in 2009. By November 2014, sixty one Sabahan seaweed farmers cultivated 122 ha or 3,050 long lines. Thirty Sabahan seaweed farmers in Kampung Pendekar (3.2 m.t dried) and 31 in Burst Point (12.5 m.t dried) produced 16 metric tonnes of dried seaweed contributed 31% to Tawau’s total production (51 m.t). The remaining 69% were from farmers in Cowie Bay that separates Sebatik from municipality of Tawau. Indonesian in Desa Setabu, Sebatik started in 2008. However, the number of Indonesian seaweed farmers, their cultivated areas and production (as well as quality) in Sebatik increased many times higher and faster than the Sabah side of Sebatik. In 2009 more than 1,401 households in Kabupaten Nunukan (including Sebatik) cultivated over 700 ha and have produced 55,098.95 and 116, 73 m.t dried seaweed in 2010 and 2011 respectively. There is a divergence in productions from farming the sea off the same island under similar weather conditions. Which of the eight explanatory factors were affecting production of seaweeds in Sebatik? Using Cobb Douglas production function, Multiple Regression analysis was conducted on 100 samples (50 Sabahan and 50 Indonesian). Results; Variable significant at α = 0.05% are Experience in farming whereas Farm size; Quantity of propagules and Location — Dummy are the variables significant at α 0.01%. Not significant are variables Fuel; Age; Number of family members involved in farming and Education level.

Efficient Cobb-Douglas Production Function

2019 ◽  
Vol 1 (1) ◽  
pp. 16-23
Author(s):  
Farhad Savabi ◽  
Keyword(s):  
Production Function ◽  
Douglas Production Function

PENERAPAN PENDUGAAN EFISIENSI ALOKATIF PADA USAHATANI BUNCIS (Phaseolus vulgaris, L)

2020 ◽  
Vol 21 (1) ◽  
pp. 81-90
Author(s):  
Marthen Robinson Pellokila
Keyword(s):  
Production Function ◽  
Allocative Efficiency ◽  
Maximum Output ◽  
Phaseolus Vulgaris L ◽  
Land Area ◽  
Price Efficiency ◽  
Efficiency Estimation ◽  
Area Use ◽  
A Company ◽  
Douglas Production Function

ABTRACT Efficiency is one of the important indicators to assess the performance of a company or farm. Efficiency guarantees the use of certain inputs to achieve maximum output levels (technical efficiency) and also efficiency ensures the use of certain inputs that maximize profits (price efficiency or allocative efficiency). This article discusses the application of the estimation of price efficiency / allocative efficiency of the use of production inputs in bean farming using the linearized Cobb-Douglas Production function. The results of the analysis shows that the application of price efficiency estimation for production inputs using the Cobb-Douglas production function is satisfactory as long as the classical assumptions required by the multiple regression are fulfilled. Of the five production inputs included in the model, only one production input provides a significant value to production, namely the production input for the land area use. Thus, only the production input for land area use is estimated at the value of its price efficiency. Based on the results of the analysis, it is found that the use of production inputs for land area use has not yet reached its price efficiency.

Is the Ukrainian economy’s absorptive capacity appropriate to attract foreign direct investment and facilitate economic growth?

2018 ◽  
Vol 13 (6) ◽  
pp. 1928-1947
Author(s):  
Svitlana Shevelova ◽  
Svitlana Plaskon
Keyword(s):  
Higher Education ◽  
Economic Growth ◽  
Human Capital ◽  
Foreign Direct Investment ◽  
Production Function ◽  
Transition Economies ◽  
Absorptive Capacity ◽  
Direct Investment ◽  
Content Type ◽  
Douglas Production Function

Purpose Despite an increasing volume of literature focussed on foreign direct investment (FDI) in transition economies, there has been little research into FDI in Ukraine. The relationship between the inflows of FDI (IFDI) and absorptive capacity (AC) has been under-researched in the peripheral transition countries like Ukraine. The purpose of this paper is to analyse the appropriateness of the Ukrainian economy’s AC to attract IFDI and facilitate economic growth with a particular focus on AC factors, such as the potential of human resources to absorb innovation and benefit from research and development (R&D) expenditure. Design/methodology/approach This study presents a thoughtful research design: there is an analysis of the AC framework for justification and selection factors that allows a measurement of the potential of Ukraine’s AC to attract and exploit IFDI. The study uses data from 25 regions in Ukraine for the 1996–2015 period. To estimate the effects of IFDI on Ukrainian economic growth, a Cobb–Douglas production function is used. As an appropriate instrumentation technique for dynamic panel data, the Generalised Method of Moments is used to provide unbiased and efficient estimates of the results. The application of the interactive term in this study allows the authors to indicate the existence of complementarities between IFDI and human capital, in particular with higher education, that afford opportunity to absorb new technologies and benefit from IFDI. Findings The resulting model indicates that R&D expenditure benefited very significantly in evolving country’s innovation system due to economic growth. Physical and human capital has not been used effectively in Ukraine to facilitate economic growth and attract IFDI. The number of patents is not significant in all of the regression models. Moreover, IFDI in Ukraine for the 1996–2015 period did not significantly impact on economic growth. However, the AC of human capital, in particular those with a higher education, is relatively relevant to benefit from IFDI. Practical implications The findings have important implications for governmental policy, which should be based on improving the business climate, a strategy for digital development, innovation, migration, institutional and regional policies aimed at the achievement of country’s sustainable economic growth. The government should increase R&D expenditure as an important factor of gross domestic product growth and introduce grants, loans and other financial supports for encouraging students to continue university education. Originality/value The originality and value of this paper is empirical and methodological. The empirical results of this study enable a conclusion about the appropriate level of the country’s absorptive capability required to benefit from IFDI. The paper also contributes to the existing academic debate and proves that despite the well-established theoretical framework for the IFDI–AC economic impact context, a new theorisation is needed to explore the full complexity of the country’s explicit relationship between AC and IFDI. Future research should be focussed on examining not only groups of countries but also distinctly the country’s explicit relationship between AC and IFDI with the particular attention for the under-researched countries: the peripheral transition economies to discover new research niches for theory building. This study presents an original methodological approach with a careful justification of the theoretical framework for hypothesis development, an appropriate sample and an original application of seminal research methods based on the Cobb–Douglas production function. This study proves that the interactive term, which allows indication of the existence of complementarities between IFDI and other variables, is appropriate for measuring AC in countries with smaller amounts of IFDI.

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