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The Effects of Technological Change
Published in Keith Norris, John Vaizey, The Economics of Research and Technology, 2018
A study in the U.S.A. has, however, produced more positive results.2 Minasian tested the hypothesis that ‘productivity increases are associated with investment in the improvement of technology and the greater the expenditures for research and development the greater the rate of growth of productivity’. He tested this hypothesis by using data on firms—eighteen firms in the chemicals industry and five drugs firms—over the period 1947–57. Obviously, this is more meaningful than our broad industrial categories. The productivity measure used was total factor productivity derived from a Cobb–Douglas production function. Surprisingly, high correlation coefficients of around 0·7 were obtained, and, interestingly enough, various other hypotheses (notably that productivity would be explained by gross investment) were apparently disproved. As the author points out, however, this was a strictly non-random sample and the results should not be generalized without further thought. There are good grounds for arguing that the chemical industry is, in this respect, not typical of industry as a whole.
Flow shop scheduling with human–robot collaboration: a joint chance-constrained programming approach
Published in International Journal of Production Research, 2023
For , let and be the number of human workers and cobots allocated to processor i, respectively. We introduce a stochastic Cobb–Douglas production function to evaluate the production efficiency of HRC in processor i. The Cobb–Douglas production function (Cobb and Douglas 1928) is widely used to express the relationship between the amounts of inputs and the amount of output that can be produced with the inputs (Kleyn et al. 2017). In the case of two inputs (labour and capital), the Cobb–Douglas production function is given as where ζ is the amount of output, and and are the amounts of labor and capital inputs, respectively. Coefficient in Equation (1) is the total factor productivity, and coefficients and can be interpreted as output elasticities and the sum of the two represents returns to scale. For properties and estimation of the Cobb–Douglas production function as well as other functional forms, the readers are referred to the book by Coelli et al. (2005).
Optimal unlabeled set partitioning with application to risk-based quarantine policies
Published in IISE Transactions, 2023
Jiayi Lin, Hrayer Aprahamian, Hadi El-Amine
Proposition 1 is not surprising, as the best way to mitigate the spread of a disease is to isolate each subject from all other subjects. However, such an approach is not only difficult to enforce, but it can also have severe and costly economic consequences. In fact, an important consideration when identifying effective quarantine policies, which Problem IQ does not take into account, is the potential economic impact. Given its relevance, some effort has been put towards incorporating economic impact into the modeling framework (e.g., Alvarez et al. (2020); Barro et al. (2020)); Eichenbaum et al. (2021); Favero et al. (2020); Toda (2020). For example, Alvarez et al. (2020) propose to estimate the economic impact of a pandemic by using fatality rate data to measure GDP loss. Alternatively, a number of papers propose to measure the economic impact of a quarantine policy through economic measures (e.g., Favero et al. (2020); Toda (2020)). One of the most commonly utilized is the Cobb–Douglas production function, which aims to estimate the maximum amount of output (e.g., GDP) that can be obtained from a given number of inputs (e.g., labor and capital). More formally, letting Y denote the total economic production, L the labor force, and C the capital input (a measure of all industries), the Cobb–Douglas production function is given by: where A is a total factor productivity parameter, and α, are output elasticities of C and L, respectively (Douglas, 1976). The Cobb–Douglas production function is widely adopted across different fields (e.g, agriculture (Li and Li (2019); Zhang et al. (2020)), manufacturing (Husain and Islam (2016); Wulan et al. (2018)), and economy (Dritsaki and Stamatiou (2018); Onalan and Basegmez (2018); Wang (2020))) to estimate to production output. For example, Wang (2020) adopt the Cobb–Douglas production function to analyze the effect of different variables (e.g., labor and technology) on the local economic growth. Their choice is based on the fact that the function comprehensively models all the necessary factors of economic growth.