scholarly journals Technical Efficiency and Technical Change in Canadian Manufacturing Industries

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Samuel Gamtessa
Keyword(s):  
Technical Efficiency ◽  
Technical Change ◽  
Fixed Effects ◽  
Manufacturing Sector ◽  
Stochastic Frontier ◽  
Manufacturing Industries ◽  
Omitted Variables ◽  
Dynamic Panel ◽  
Data Set ◽  
Canadian Manufacturing

This study applies the “true fixed effects” panel stochastic frontier methodology to the Canadian KLEMS data set to estimate technical change and technical efficiency in the Canadian manufacturing sector. To account for the endogeneity of capital inputs as well as the possible problems related to omitted variables, a two-stage residual inclusion method is pursued. The first stage is estimated using the dynamic panel GMM method. The results show that Canadian manufacturing industries experienced significant declines in technical efficiencies during the last ten years. This suggests that the observed slowdown in TFP growth during the recent past is partly due to declining technical efficiency.

scholarly journals COMPOSITION OF THE JOB-SEEKER STOCK IN LABOUR MARKET MATCHING :A STOCHASTIC FRONTIER APPROACH

2016 ◽  
Vol 3 (1) ◽  
pp. 63
Author(s):  
Sanna Mari Hynninen
Keyword(s):  
Labour Market ◽  
Technical Efficiency ◽  
Fixed Effects ◽  
Labour Force ◽  
Stochastic Frontier ◽  
Matching Function ◽  
Fixed Effects Model ◽  
Job Seekers ◽  
Data Set ◽  
Local Labour

This paper investigates the technical efficiency of labour market matching taking a stochastic frontierapproach. The data set consists of monthly data from 145 Local Labour Offices (LLOs) in Finland over theperiod 1995/01-2004/09. The true fixed-effects model is utilised in order to separate cross-sectionalheterogeneity from inefficiency. According to the results, there are notable differences in matching efficiencybetween regions, and these differences contribute significantly to the number of filled vacancies. If all regionswere as efficient as the most efficient one, the number of total matches per month would increase by over 10%. If inefficiency had no role in the matching function, the number of matches would increase by almost 24 %.The weight of the composition of the job-seeker stock and other environmental variables in the determinationof matching inefficiency is on average 61 %. In particular, job seekers out of the labour force and highlyeducated job seekers improve technical efficiency in the matching function

Impact of Determinants of Industrial Development on Employment and Wages in Indian Manufacturing

2020 ◽  
pp. 18-30
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Positive Impact ◽  
Manufacturing Sector ◽  
Factor Productivity ◽  
Firm Level ◽  
Employment Level ◽  
The Impact ◽  
Level Performance

ABSTRACT The present study was undertaken to explore the evolution of the impact of firm-level performance on employment level and wages in the Indian organized manufacturing sector over the period 1989-90 to 2013-14. One of the major components of the economic reform package was the deregulation and de-licensing in the Indian organized manufacturing sector. The impact of firm-level performance on employment and wages were estimated for Indian organized manufacturing sector in major sub-sectors in India during the period from 1989-90 to 2013-14 of the various variables namely profitability ratio, total factor productivity change, technical change, technical efficiency, openness (export-import), investment intensity, raw material intensity and FECI in total factor productivity index, technical efficiency, and technical change. The study exhibited that all explanatory variables except profitability ratio and technical change cost had a positive impact on the employment level. Out of eight variables, four variables such as net of foreign equity capital, investment intensity, TFPCH, and technical efficiency change showed a positive impact on wages and salary ratio and rest of the four variables such as openness intensity, technology acquisition index, profitability ratio, and technical change had negative impact on wages and salary ratio. In this context, the profit ratio should be distributed as per the marginal rule of economics such as the marginal productivity of labour and capital.

scholarly journals Are the determinants of markup size industry-specific? The case of Slovenian manufacturing firms

2011 ◽  
Vol 58 (2) ◽  
pp. 229-244 ◽  
Author(s):  
Nina Ponikvar ◽  
Maks Tajnikar
Keyword(s):  
Manufacturing Sector ◽  
Manufacturing Firms ◽  
Manufacturing Industries ◽  
Dynamic Panel ◽  
Pricing Policy ◽  
Large Variability ◽  
Oligopoly Theory ◽  
Determination Process ◽  
Precise Role

The aim of this paper is to identify factors that affect the pricing policy in Slovenian manufacturing firms in terms of the markup size and, most of all, to explicitly account for the possibility of differences in pricing procedures among manufacturing industries. Accordingly, the analysis of the dynamic panel is carried out on an industry-by-industry basis, allowing the coefficients on the markup determinants to vary across industries. We find that the oligopoly theory of markup determination for the most part holds for the manufacturing sector as a whole, although large variability in markup determinants exists across industries within the Slovenian manufacturing. Our main conclusion is that each industry should be investigated separately in detail in order to assess the precise role of markup factors in the markup-determination process.

scholarly journals Technical Efficiency of the Malaysian Wooden Furniture Industry: A Stochastic Frontier Production Approach

2010 ◽  
Vol 3 (3) ◽  
pp. 10 ◽  
Author(s):  
Alias Radam ◽  
Mohd Rusli Yacob ◽  
Huda Farhana Mohd Muslim
Keyword(s):  
Technical Efficiency ◽  
Manufacturing Sector ◽  
Stochastic Frontier ◽  
Production Model ◽  
Furniture Industry ◽  
Conventional View ◽  
Maximal Output ◽  
Wood Furniture ◽  
Wooden Furniture ◽  
Medium Firms

Wood furniture industry is an important component in our manufacturing sector for it significantly contributes to the industrialization of Malaysia’s economy. Evaluating wood furniture industry’s level of efficiency is important to assist and provide a relative direction to small and medium firms on their business. The objective of this research is to examine the efficiency of wooden furniture industry by determining the technical efficiency using stochastic frontier production model. Results show that firm output is 54 per cent less than the maximal output which can be achieved from the existing inputs. The technical inefficiency on individual firm varies from 1.63 to 94.69 per cent and so does the potential to increase firm output from the existing inputs. This evidence suggests that many firms still operate below the efficiency level, confirming the conventional view that labor-intensive firms are most likely inefficient.

scholarly journals The Dynamics and Sources of Technical Efficiency of the Manufacturing Industries in Bangladesh

2020 ◽  
Author(s):  
Sheikh Jafar Emran ◽  
Md. Moniruzzaman
Keyword(s):  
Technical Efficiency ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Returns To Scale ◽  
Manufacturing Industries ◽  
Efficiency Gain ◽  
Firm Characteristics ◽  
Frontier Analysis ◽  
Textile And Apparel ◽  
Wooden Furniture

Abstract This paper aims to analyze the dynamics of technical production efficiency of the manufacturing sector in Bangladesh using the cross-sectional data collected under the Survey of Manufacturing Industries (SMI) conducted in 2006 and 2012. Based on the dynamics of mean efficiency scores among the industries derived using Stochastic Frontier Analysis(SFA) techniquewith Cobb-Douglas technology with half-normal distribution during the considered period three most efficiency gainer industries are ((i) Jute textile,(ii) Dying and bleaching of textiles, and(iii) Bidies respectively. On the other hand, under SFA specification with Translog production function top three efficiency gainers are (i) Jute textile,(ii) Bidies, and(iii) Fish, Crustaceans and Molluses respectively. Under constant returns to scale in Data Envelopment Analysis(DEA), based on the mean efficiency score top three efficiency gainers are(i) Fibre textile,(ii) Embroidery of textile and apparel, and(iii) Wooden furniture and fixture respectively while undervariable returns to scale top three gainers are(i) Fibre textile,(ii) Embroidery of textile and apparel, and(iii) Wooden furniture and fixture respectively. Whatever technique we employ, we find that most cases garments or garments related industries remain among top performers in terms of efficiency gain. This indicates that garments industries have improved significantly in terms of efficiency to survive in world competition. Moreover, our results suggest that firm characteristics, location factors as well as ownership features are more important jointly rather than individually to enhance efficiency. Locational and ownership characteristics jointly, in most cases, are also not so influential in pulling the efficiency measures up. However, the firm characteristics are very important in raising the technical efficiency of the firms, especially in case of stochastic frontier analysis. And firm characteristics shows stronger impacts in interaction with other locational and/or ownership characteristics.

The Influences of Revenue Diversification and Incoming Tourists on the Performance of Star-Rated Hotels in China

2019 ◽  
Vol 24 (4) ◽  
pp. 483-495
Author(s):  
Chun Kwok (Henry) Lei
Keyword(s):  
Technical Efficiency ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Hotel Management ◽  
Revenue Diversification ◽  
Data Set ◽  
Frontier Analysis ◽  
Capital Productivity ◽  
Primary Determinant ◽  
Different Origins

Human capital and a transformed data set are combined into a stochastic frontier analysis to estimate the technical efficiency of star-rated hotels in 30 provinces and municipalities of China for the period of 2011–2015. The influences of revenue diversification across room, food & beverage, and other services and incoming tourists on technical efficiency, labor, and capital productivities are addressed. Hotels in Zhejiang, Shanghai and Tianjin are found to be the most efficient. For the efficiency and productivities determinants, per capita GDP of the hosting province is the primary determinant, hotel ranking and diversification in revenue sources contribute to efficiency and partial productivities, while tourists from different origins bring about diversified impacts. Hotels located in regions with more Taiwan tourists are more efficient with higher capital productivity. Tourists from Hong Kong, Macau, and foreign countries have brought about both positive and negative linkages to efficiency and partial productivities. In general, hotels can reallocate their resources to diversify their services for better efficiency and productivities. In contrast, expanding external markets cannot guarantee higher efficiency and better productivities. Hotel management should be careful in formulating their marketing strategies.

Stochastic Frontier Analysis and Measurement of Productivity and Technical Efficiency of Indian Manufacturing Sector

2017 ◽  
Vol 5 (1) ◽  
pp. 52-69
Author(s):  
Manoj Kumar
Keyword(s):  
Technical Efficiency ◽  
Stochastic Frontier Analysis ◽  
Manufacturing Sector ◽  
Stochastic Frontier ◽  
Value Added ◽  
Frontier Analysis ◽  
Use Of Resources ◽  
Plant Level ◽  
Indian Manufacturing Sector ◽  
Factor Inputs

It is generally believed the structural reforms that usher in competition and force companies to become more efficient were introduced later in India following the macroeconomic crisis in 1991. However, whether or not the post-1991 growth is an outcome of more efficient use of resources or greater use of factor inputs, especially capital, remains an open empirical question. In this article the author uses plant-level data from 1990 and 2015 to address this question. The results indicate that while there was an increase in the productivity of factor inputs during the 1990s, most of the growth in value added is explained by growth in the use of factor inputs. The author also finds that median technical efficiency declined in all but one of the industries between the two years, and change in technical efficiency explains a very small proportion in the change in gross value added.

Which food prices were more vulnerable to the COVID-19 blockade regulations in China? Comparison between perishable and storable vegetables

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yupeng Wang ◽  
Satoru Shimokawa
Keyword(s):  
Spatial Dependence ◽  
Fixed Effects ◽  
Design Methodology ◽  
Food Prices ◽  
Panel Var ◽  
Fixed Effects Model ◽  
Dynamic Panel ◽  
Data Set ◽  
Content Type ◽  
The Impact

PurposeThis paper aims to investigate how differently the COVID-19 blockade regulations influence the prices of perishable and storable foods. The authors focus on the cases of the 2020 blockade at Hubei province and the 2021 blockade at Shijiazhuang city in China, and the authors examine how the blockade influenced the prices of Chinese cabbages (perishable) and potatoes (storable) within and around the blockade area.Design/methodology/approachThe paper employs the fixed effects model, the panel VAR (PVAR) model, and the spatial dynamic panel (SPD) model to estimate the impacts of the blockade on the food prices. It constructs the unique data set of 3-day average prices of Chinese cabbages and potatoes at main wholesale markets in China during the two urban blockade periods from January 1 to April 8 in 2020 and from January 1 to March 1 in 2021.FindingsThe results from the SPD models indicate that the price of Chinese cabbages was more vulnerable and increased by 7.1–9.8% due to the two blockades while the price of potatoes increased by 1.2–6.1%. The blockades also significantly influenced the prices in the areas adjacent to the blockade area. The SPD results demonstrate that the impacts of the blockades would be overestimated if the spatial dependence is not controlled for in the fixed effects model and the PVAR model.Research limitations/implicationsBecause the research focuses on the cases in China, the results may lack generalizability. Further research for other countries is encouraged.Originality/valueThis paper demonstrates the importance of considering food types and spatial dependence in examining the impact of the COVID-19 blockades on food prices.

Decomposition of Output and Productivity Growth in the Organised Manufacturing Industries in India: An Inter-State Analysis

2018 ◽  
Vol 66 (1-2) ◽  
pp. 25-41
Author(s):  
Prasanta Kumar Roy
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Manufacturing Sector ◽  
Stochastic Frontier ◽  
Output Growth ◽  
Manufacturing Industries ◽  
Growth Effect ◽  
Major Contributor ◽  
Factor Productivity ◽  
Reform Period

This article examines and applies the theoretical foundation of the decomposition of output and total factor productivity growth (TFPG) of the aggregate manufacturing industries in 15 major industrialised states in India as well as in all-India during the period from 1981–1982 to 2010–2011, during the entire period, during the pre-reform period (1981–1982 to 1990–1991) and post-reform period (1991–1992 to 2010–2011), and also during two different decades of the post-reform period, that is, during 1991–1992 to 2000–2001 and 2001–2002 to 2010–2011. Output growth is decomposed into input growth effect and TFPG where the three attributes of TFPG are adjusted scale effect, technological progress (TP) and technical efficiency change. A stochastic frontier model with a translog production function is used to estimate the growth attributes of output and total factor productivity (TFP). The empirical results show that input growth is the major contributor to output growth, whereas TP is found to be the major contributor to TFPG and the decline in TFPG of the organised manufacturing sector in India and in its major industrialised states during the post-reform period is mainly due to the decline in TP of the same during that period. JEL Codes: C23, D24, L6, O47

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