Quantity Augmenting Technical Progress and Two-Factor Production Functions

1966 ◽  
Vol 33 (1) ◽  
pp. 71 ◽  
Author(s):  
Michael D. McCarthy
Keyword(s):  
Technical Progress ◽  
Production Functions ◽  
Factor Production

scholarly journals Evaluation of the Imitation Potential of IT Companies Using the Cobb-Douglas Production Function

Vestnik NSUEM ◽  
2019 ◽  
pp. 130-142
Author(s):  
E. N. Akerman ◽  
A. A. Mikhalchuk ◽  
V. V. Spitsyn ◽  
N. O. Chistyakova
Keyword(s):  
Production Function ◽  
Cluster Model ◽  
Production Functions ◽  
Production Volume ◽  
High Quality ◽  
Production Factors ◽  
Factor Production ◽  
Fixed Assets ◽  
Main Factors ◽  
Douglas Production Function

The relevance of the study has been determined by the acceleration of innovation growth, which encourages companies to use imitation strategies in response to disruptive technological changes.The study used the Cobb-Douglas production function to evaluate the effectiveness of the used production factors of Russian IT companies. A high-quality 3-cluster model of IT companies was built, as well as highly significant two-factor production functions of Cobb-Douglas, which made it possible to identify the contribution of the main factors (wage and fixed assets) to the production volume (revenue) for each cluster.

ENTREPRENEURIAL CAPITAL AND THE VOLUME OF SMALL ENTERPRISES PRODUCTION: RUSSIAN REGIONS DATA

2019 ◽  
Vol 24 (02) ◽  
pp. 1950013
Author(s):  
IULIIA PINKOVETSKAIA ◽  
ANTON LEBEDEV
Keyword(s):  
Spatial Data ◽  
Practical Importance ◽  
Production Functions ◽  
Small Enterprises ◽  
Russian Regions ◽  
Development Programs ◽  
Factor Production ◽  
Entrepreneurial Capital ◽  
Three Factor ◽  
Large Enterprises

The purpose of this study is to assess the three-factor production functions describing the dependence of the small enterprises (SEs) and microenterprises, which include turnover on the wages of their employees, investments in fixed capital and the entrepreneurial capital of a particular region. The authors propose the indicator that characterizes the level of entrepreneurial capital — the number of employees of large enterprises located in the respective regions. This study is based on the empirical spatial data specifying the activities of employees in all SEs and microenterprises, as well as a number of large enterprises. The official statistical information includes observations on business activity of 1,449 SEs and microenterprises in 80 Russian regions in 2015. The research determines the factors influencing the turnover of enterprises, proves the high quality of the source data approximation using the three-factor production functions and confirms the need for an increased number of small and microenterprises for the Russian regions’ economy. The authors discover tools for the evaluation of production functions, which are of academic and practical importance. They can be used in studies of the regions’ entrepreneurial sectors, for monitoring the efficiency of resource use, and the rationale for the entrepreneurship development programs.

Technical progress and energy dependent production functions

1985 ◽  
Vol 45 (3) ◽  
pp. 285-311 ◽  
Author(s):  
Reiner Kümmel ◽  
Wolfgang Strassl ◽  
Alfred Gossner ◽  
Wolfgang Eichhorn
Keyword(s):  
Technical Progress ◽  
Production Functions ◽  
Energy Dependent

Technical Progress and Production Functions

1958 ◽  
Vol 40 (4) ◽  
pp. 407 ◽  
Author(s):  
Warren P. Hogan
Keyword(s):  
Technical Progress ◽  
Production Functions

scholarly journals The Two-level CES Production Function for the Manufacturing Sector of Pakistan

1989 ◽  
Vol 28 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Ashfaque H. Khan
Keyword(s):  
Production Function ◽  
Technical Progress ◽  
Manufacturing Sector ◽  
Relevant Literature ◽  
Returns To Scale ◽  
Appropriate Technology ◽  
Production Functions ◽  
Elasticity Of Substitution ◽  
Two Factors ◽  
Factors Of Production

Production functions have been widely studied in the relevant literature. In this paper, apart from labour and capital, we have used energy as a factor input and calculated the elasticity of substitution between these inputs, measured technical progress, and determined the returns to scale in the manufacturing sector of Pakistan. Since we have more than two factors of production, the standard Cobb· Douglas and CES production functions do not provide satisfactory results. Hence, two·level (nested) CES production function becomes the natural choice for the appropriate technology. Using this technology, we have found low elasticity of substitution between the three factors of production. Furthermore, the manufacturing sector is found to exhibit decreasing returns to scale, having experienced disembodied technical progress at the rate of 3.7 percent per annum.

scholarly journals TWO-FACTOR PRODUCTION FUNCTIONS WITH GIVEN MARGINAL RATE OF SUBSTITUTION

2019 ◽  
pp. 169-181
Author(s):  
G. Khatskevich ◽  
A. Pranevich ◽  
M. Chajkovskij
Keyword(s):  
Inverse Problems ◽  
Production Functions ◽  
Marginal Rate ◽  
Marginal Rate Of Substitution ◽  
Production Processes ◽  
Factor Production ◽  
Rate Of Substitution

The article is devoted to the study of inverse problems of identifying two-factor production functions from given marginal rate of technical substitution. The analytical forms of twofactor production functions with given linear-fractionalmarginal rate of technical substitution of labor by capital. Classes of two-factor production functions that correspond to given (constant, linear, linear-fractional, exponential, etc.) marginal rate of technical substitution areindicated. The obtained results can be applied in modeling of production processes.

scholarly journals Compositum mixtum of one-factor production function

2020 ◽  
Vol 4 (92) ◽  
pp. 118-128
Author(s):  
Daniil Cherevatskyi ◽  
Keyword(s):  
Production Function ◽  
Power Plants ◽  
Production Functions ◽  
Coal Production ◽  
Food Ingredients ◽  
Long Period ◽  
Factor Production ◽  
The One ◽  
Composite Goods ◽  
Permanent Increase

The paper is devoted to finding a convenient argument for the one-factor production function of a mine. For construction of one-factor production functions it is offered to use composite goods (compositum mixtum), the value of which corresponds to the amount of costs, incurred by an enterprise during a year-long period of time. Taking into account peculiarities of coal mines, the study verifies an expedience of converting the composite good to the coal equivalent (conventional fuel), consumed by an enterprise in a technological process, Big Mac burgers, and the stuff number. An attempt to use coal to build the production function of a mine as a composite good was not entirely successful. An experimental study of the dependence of coal production from actual costs of coal resources, carried out on a mathematical model of a mine, proved the inexpediency of such formalization, particularly due to the strong influence of non-mine (external) factors. Statistically significant, for example, were the efficiency of fuel use in power plants and the norm of coal for household needs. The relevance and popularity of the ‘sandwich index’ is due to the fact that McDonald's is in most countries, and Big Mac itself contains so many food ingredients (bread, cheese, meat and vegetables) that can act as a kind of mold of the national economy. But the conducted research demonstrated that such a way of expressing the composite good for Ukrainian mines is not rational due to macroeconomic interventions (permanent increase of the minimum wage) in the microeconomics of an enterprise. The most appropriate argument for the production function of the mine is the stuff number of an enterprise. The rationality of Big Mac as the basis of the production function is demonstrated in the construction of the production function of world export coal markets.

scholarly journals Sectoral Technology and Structural Transformation

2015 ◽  
Vol 7 (4) ◽  
pp. 104-133 ◽  
Author(s):  
Berthold Herrendorf ◽  
Christopher Herrington ◽  
Ákos Valentinyi
Keyword(s):  
Structural Transformation ◽  
Technical Progress ◽  
Second Order ◽  
Production Functions ◽  
Elasticity Of Substitution ◽  
Constant Elasticity ◽  
Constant Elasticity Of Substitution ◽  
Production Factors

We assess how the properties of technology affect structural transformation, i.e., the reallocation of production factors across the broad sectors of agriculture, manufacturing, and services. To this end, we estimate sectoral constant elasticity of substitution (CES) and Cobb-Douglas production functions on postwar US data. We find that differences in technical progress across the three sectors are the dominant force behind structural transformation whereas other differences across sectoral technology are of second-order importance. Our findings imply that Cobb-Douglas sectoral production functions that differ only in technical progress capture the main technological forces behind the postwar US structural transformation. (JEL E16, E25, O33, O47)

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