Productivity Measurement at the Firm Level

Bitran, Gabriel R.; Li, Chang

doi:10.1287/inte.14.3.29

Cited by 20 publications

(6 citation statements)

References 1 publication

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“…For these industries, direct contact with customers and suppliers might also be more important than it is for other sectors. These results are in line with those of earlier studies on agglomeration elasticities that are based on firm data (10,18). For the manufacturing sector, earlier studies found an elasticity of 0.024, while for construction an elasticity of 0.03 was estimated.…”

Section: Productivity and Agglomerationsupporting

confidence: 92%

“…Companies in the transport sector (transport of goods and persons), hospitality, and business sectors seem to benefit from a higher employment density, and agglomeration benefits seem to accrue. These findings are supported by elasticities from the service sector (18,27). Higher employment density might also mean more potential customers.…”

Section: Productivity and Agglomerationmentioning

confidence: 73%

“…Productivity is the ratio between firm output and firm input (18). Several kinds of productivity ratios have been developed.…”

Section: Productivity: Total Productivity Approachmentioning

confidence: 99%

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Relationship between Agglomeration and Productivity in a Norwegian Context

Nilsen

Babri

Andersen

et al. 2017

Transportation Research Record: Journal of the Transportation R

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Cost-benefit analysis (CBA) is an important tool in many countries in the evaluation of transport investments. The main objective of CBA is to allocate society's shared resources by considering all c o s t s a n d b e n e f i t s t o s o c i e t y a s a w h o l e . W h i l e d i r e c t c o s t s a n d b e n e f i t s a r e o f t e n straightforward to calculate, indirect cost and benefits are more challenging to include in CBA. Among indirect benefits, there has been increased attention to potential productivity benefits for firms, which results from reductions in distance to other firms, workers or costumers. The literature on wider economic impacts and agglomeration economies suggests that such benefits usually exist but varies between sectors and nations. However, there seems to be little consensus on the size of the benefits. This paper analyzes the relationship between agglomeration, productivity and transport investment in a Norwegian context. We use a total factor productivity approach to calculate productivity for 17,000 firms along the western coast of Norway within eight sectors from 2009 to 2013. For each firm an agglomeration index is calculated and a regression model is used to find the relationship between productivity and agglomeration. Our results show that productivity varies between sectors and that there is no clear trend across all sectors that increased agglomeration leads to increased productivity. The implications of these results in a CBA perspective is that one should be careful seeing agglomeration as solely beneficial for all sectors, but be more specific regarding which sectors are affected in each transport investment project.

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Section: Productivity and Agglomerationsupporting

confidence: 92%

Section: Productivity and Agglomerationmentioning

confidence: 73%

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Relationship between Agglomeration and Productivity in a Norwegian Context

Nilsen

Babri

Andersen

et al. 2017

Transportation Research Record: Journal of the Transportation R

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“…A decline in productivity can lead to slow economic growth and high inflation. On the other hand, improved productivity can lead to a higher rate of economic growth and higher living standards for a nation [1]. At an organizational level, productivity measures how well an organization converts input resources (labor, materials, machines, etc.)…”

Section: Introductionmentioning

confidence: 99%

Factor Analysis of Obstacles Restraining Productivity Improvement Programs in Manufacturing Enterprises in Oman

Bashir

Alzebdeh

Riyami

2014

Journal of Industrial Engineering

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Manufacturing enterprises can face obstacles that make productivity improvement efforts ineffective or even prevent improvement operations. This paper reports on an investigation of the internal obstacles restraining productivity improvement programs in the manufacturing enterprises of developing countries, using Oman as a case study. Data required for this study were collected through a questionnaire given to production and operations managers at 51 manufacturing enterprises in Oman. The results of applying factor analysis to a dataset of 15 obstacles showed that these obstacles can be reduced to three major factors. Ranked according to their importance, these factors are poor management practices, employee job dissatisfaction, and poor human resource management. The findings of this research are of value to production and operations managers of manufacturing enterprises who are interested in conducting productivity improvement programs not only in Oman but also in developing countries with environments similar to that of Oman, such as the Gulf Cooperation Council (GCC) countries.

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“…It is used as a measurement for determining the efficiency of a production process. Its calculation varies depending on how the outputs and inputs are defined [ 17 , 18 ]. Tolentino [ 19 ] indicated that productivity should be measured differently, mainly due to the nature of the output, as some measurements are principally dependent on consumer preference and utility, which has an unclear countable output [ 20 ].…”

Section: Relevant Studies: Defining Productivity and Its Determinantsmentioning

confidence: 99%

Identifying Factors Influencing Productivity of Older Workers in Service Sector: A Case Study in Pilot Companies in Thailand

Asavanirandorn

Pechdin

Trang

2022

Behavioral Sciences

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Productivity has posed issues for global countries in terms of promoting older employment due to an emergence of questions regarding production efficiency. Individual characteristics result in varying production efficiencies, which in turn provide different levels of productivity. Taking this concern into account, we are here to examine the characteristics of older workers who provide high productivity in order to seek recommendations for fostering decent work for them. We utilized the dataset collected from the pilot service companies in Thailand who participated in a national initiative program for employing older people. A total of 204 older workers and their characters were then observed. Those characters were analyzed by the Generalized Ordered Logit Regression (gologit) model. Empirical findings indicate that allocating work that is related to communication and coordination to older workers would enhance odd ratio of their productivity by as much as 4.79 times compared to general tasks. Furthermore, employing older individuals on a part-time basis tends to generate higher-level productivity than full-time employment. We also found that gender and age differences have no significant effect on productivity in the service sector as people age, and factors related to types of employment, education, health, and financial status are also a precise determinant for the productivity of older workers. This would suggest that the work design for the older workers must be in line with the aforementioned determinants. In addition, the government initiatives utilizing key findings from this study to boost older workers’ productivity should give priority on tax incentives, promoting and advocating for employment equality, and vocationalization programs.

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Productivity Measurement at the Firm Level

Cited by 20 publications

References 1 publication

Relationship between Agglomeration and Productivity in a Norwegian Context

Relationship between Agglomeration and Productivity in a Norwegian Context

Factor Analysis of Obstacles Restraining Productivity Improvement Programs in Manufacturing Enterprises in Oman

Identifying Factors Influencing Productivity of Older Workers in Service Sector: A Case Study in Pilot Companies in Thailand

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