The Theory of Marginal Productivity Tested by Data for Manufacturing in Victoria, I

Handsaker, Marjorie; Douglas, Paul H.

doi:10.2307/1884498

Cited by 13 publications

(8 citation statements)

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“…Technical change is addressed by following the lead of Handsaker and Douglas (1937) and Williams (1945) who identified the need to include technological advances in the estimation process. Fraser (2002) assumes a non-constant value, A(t), to capture technical change using a time trend (t).…”

Section: Functional Formmentioning

confidence: 99%

An Evaluation of the Sensitivity of U.S. Economic Sectors to Weather

Larsen

2006

SSRN Journal

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This study quantifies the impact of weather on eleven two-digit SIC sectors of the U.S. economy ranging from agriculture and construction to retail trade and utilities.Although it is obvious that weather affects the output of most sectors in some way, the magnitudes of these effects are not well known. This research effort estimates the historical sensitivity of production to annual weather variability. In addition to defining what it means for a sector to be sensitive to weather relative to another sector, industry productivity from 1977-2000 was modeled against measures of temperature and precipitation along with more traditional inputs to production like capital, labor and energy consumption. Specifically, sector output is modeled using a transcendental logarithmic production function (TRANSLOG) with measures of regional weather included. In order to estimate the aggregate sensitivity of the U.S. economy to weather, Monte-Carlo simulation of the four measures of weather is employed for each region by randomly drawing from historically observed weather combinations (i.e. temperature and precipitation) to produce distributions of sector-region output variability holding the conventional economic inputs constant.As expected, the results show that the impact of weather varies from region to region and sector to sector. It is also evident that traditional methods used to model economic production such as the Cobb-Douglas and TRANSLOG specifications are improved statistically with the inclusion of measures of weather as factors of production. Given the available data at the annual level, the econometric results show that in general and across regions, the manufacturing sector is more sensitive to weather (e.g. cooling degree-days) than previously thought. The results also indicate that economic sectors in the Western U.S. are more sensitive to weather than they are in the Midwest and East Coast. It is also reported that as the standard deviation of precipitation changes by 1%, average U.S. transportation and utilities sector output drops between .02% and .09% (respectively). For context, actual year 2000 output for the utilities sector was over $200 billion and actual transportation output exceeded

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Section: Functional Formmentioning

confidence: 99%

An Evaluation of the Sensitivity of U.S. Economic Sectors to Weather

Larsen

2006

SSRN Journal

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“…A more important problem with the original specification of the functional relationship is the omission of technical change. The need to take account of technical change in estimation was noted by Handsaker and Douglas (1937) and Williams (1945). Although Williams noted a method to proxy technical change, no effort was made in either of these studies to address this issue.…”

Section: The Cobb-douglas Production Functionmentioning

confidence: 99%

“…The data used in this paper are taken from Cobb and Douglas (1928) for the USA, Douglas (1934) for Massachusetts (MASS) and New South Wales (NSW), Handsaker and Douglas (1937) for Victoria (VIC), and Williams (1945) for New Zealand (NZ). The salient features of the data are described in Appendix A.…”

Section: Datamentioning

confidence: 99%

“…For the USA, MASS, and VIC the contribution of the labour input is more significant than capital which is a reflection of the rapid growth in the capital series not matched by the increases in output shown in Figure 1. Handsaker and Douglas (1937) suggest that the difference in factor shares for NSW happen because the capital index was lowered by the inclusion of depreciated capital at higher price levels (p. 4, fn.4). They also note that the capital index for VIC overstates the actual growth in capital during the data period (p.23).…”

Section: No Time Trendmentioning

confidence: 99%

“…However, it is an interesting question to ask whether or not the original work and claims made by Douglas can be maintained when the original data sets are re-examined using current statistical techniques. In this paper we focus specifically on the five time-series studies: Cobb and Douglas (1928), Douglas (1934), Handsaker and Douglas (1937) and Williams (1945). The reason for focusing on the time-series data sets is that they are the most frequently cited in relation to the results derived by Douglas.…”

Section: Introductionmentioning

confidence: 99%

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Cobb-douglas production function

Gass¹,

Harris

2001

Encyclopedia of Operations Research and Management Science

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It summarised approximately 30 econometric studies that inductively investigated marginal productivity theory. It was this body of work that yielded the Cobb-Douglas production function that is used extensively in theoretical and applied research. Douglas undertook this research because he had reached the conclusion that economic theorists had become lazy in how they illustrated marginal productivity curves. Douglas (1948) argued that economic theorists showed little interest in determining the actual position and slope of marginal productivity curves when drawing them. He observed that they were happy to draw them downward sloping left to right and that this was a symptom of, 'intellectual slovenliness, unfortunately set in' (p.5).The central finding of his research was that the estimated exponents of the CobbDouglas production function were very similar to the expected factor shares. This allowed Douglas to argue that the findings provided strong support for marginal productivity theoryThe Laws of Production. Douglas (1948) makes this point as follows:

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Climate impacts on geographical changes in the energy industry

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Owing to global warming, ice in the Arctic Ocean has shrunk by 2.7% every 10 years since 1978, resulting in tremendous damage. This study uses the Cobb–Douglas production function to analyze the impact of abnormal weather on energy industry production using temperature and precipitation indicators. In particular, the government identified the impact of abnormal weather conditions on manufacturing and public works related to the Korean energy industry. The results showed that the numbers of freezing days and heavy rain days adversely affect public works, whereas hot and cold days and heavy rain days all positively affect the production of coal, petroleum, and chemical products. These results also led to regional risk assessments of the energy industry. This study can help in developing the 2030 industry climate change adaptation plan.

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The Theory of Marginal Productivity Tested by Data for Manufacturing in Victoria, I

Cited by 13 publications

References 0 publications

An Evaluation of the Sensitivity of U.S. Economic Sectors to Weather

An Evaluation of the Sensitivity of U.S. Economic Sectors to Weather

Cobb-douglas production function

Climate impacts on geographical changes in the energy industry

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