Empirical Study of the Production Efficiency Change of Chinese Regional Construction Industry Basing on Stochastic Frontier Analysis

The primary problems pertaining to productivity or – more precisely – efficiency are: how to define it and how to measure it. This article studies technical efficiency in Stochastic Frontier Analysis (SFA) – the input-oriented frontier model – in the construction industry and compares it with Data Envelopment Analysis (DEA) results. The models explored in this paper were constructed on the basis of two outputs and personnel cost as an input. The research sample consisted of European countries. The aim was to determine whether there are substantial differences in estimation of efficiency derived from those two alternative frontier approaches. The comparison of results according to the models may translate into higher reliability of the undertaken labour efficiency analysis in construction and its conclusions. Although the results are not characterized by high compatibility, the conducted analysis indicated the most attractive countries taking into account labour cost to profit and turnover ratios of enterprises. One of the determinants which should not be ignored when analysing the labour efficiency is the level of development of a country; however, it is not the sole factor affecting the efficiency of the sector.

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A stochastic frontier analysis of technical progress, efficiency change and productivity growth in the Pacific Northwest sawmill industry

Forest Policy and Economics ◽

10.1016/j.forpol.2009.04.006 ◽

2009 ◽

Vol 11 (4) ◽

pp. 280-287 ◽

Cited By ~ 11

Author(s):

Ted L. Helvoigt ◽

Darius M. Adams

Keyword(s):

Pacific Northwest ◽

Technical Progress ◽

Productivity Growth ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

Efficiency Change ◽

Frontier Analysis ◽

The Pacific ◽

Sawmill Industry

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Do investors value firm efficiency improvement? Evidence from the Australian context

Corporate Ownership and Control ◽

10.22495/cocv13i3c2p4 ◽

2016 ◽

Vol 13 (3) ◽

pp. 293-308

Author(s):

Hai Yen Pham ◽

Richard Chung ◽

Eduardo Roca ◽

Ben-Hsien Bao

Keyword(s):

Stock Returns ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

Market Liquidity ◽

Sample Period ◽

Efficiency Change ◽

Frontier Analysis ◽

Level Change ◽

Change Effect ◽

Weighted Portfolio

Do investors value improvement in efficiency? This paper investigates the relation between the firm’s technical efficiency change and subsequent stock returns. We employ a stochastic frontier analysis to evaluate a firm’s efficiency for a large panel of non-financial companies in Australia from January 1990 to October 2012. The results show that over the sample period, the estimated mean improvement in firm’s efficiency is 3% per year. We find that an equally-weighted (value-weighted) portfolio of stocks with the top tertile level change in efficiency outperforms an equally-weighted (value-weighted) portfolio of stocks with the bottom tertile level change in efficiency, by an average of 11% (7%) per annum during the sample period. We also find a significant efficiency change effect on a cross-section of stock returns after controlling for other risk factors such as size, book-to-market, market liquidity, industry concentration, and seasonality effect.

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Evaluation of Manufacturing Competitiveness of Hubei Province Based on Stochastic Frontier Analysis

Discrete Dynamics in Nature and Society ◽

10.1155/2021/3364968 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Weidong Gai ◽

Lei Zhou ◽

Chun Chen

Keyword(s):

Manufacturing Industry ◽

Production Efficiency ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

Hubei Province ◽

Central China ◽

Frontier Analysis ◽

Industrial Enterprises ◽

Promotion Policies ◽

Manufacturing Competitiveness

The manufacturing level directly manifests the comprehensive strength of a country or region. Production efficiency provides an important metric of the competitiveness of the manufacturing industry. Based on the data of China’s industrial enterprises of 1999–2011, this paper estimates the production efficiency of manufacturing in Central China’s Hubei Province through stochastic frontier analysis (SFA) and thus characterizes the differences between prefectures of Hubei in manufacturing competitiveness. The results show that, on the prefecture level, Xianning and Wuhan saw a decline in manufacturing competitiveness, while Xiangfan and Xiaogan witnessed an increase in manufacturing competitiveness. To enhance local manufacturing competitiveness and make Hubei the forerunner and cornerstone of Central China, different prefectures should adopt different industrial promotion policies, pay attention to cultivating the technological innovation capabilities of enterprises, and strengthen the integration of production, education, and research.

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Southeastern Peanut-Production Cost Efficiency Under the Quota System: Implications for the Farm-Level Impacts of the 2002 Farm Act

Journal of Agricultural and Applied Economics ◽

10.1017/s1074070800022173 ◽

2006 ◽

Vol 38 (1) ◽

pp. 213-224 ◽

Cited By ~ 2

Author(s):

Denis A. Nadolnyak ◽

Stanley M. Fletcher ◽

Valentina M. Hartarska

Keyword(s):

Production Efficiency ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

The United States ◽

Farm Size ◽

Quota System ◽

Frontier Analysis ◽

Farm Level ◽

Peanut Production ◽

Growing Conditions

In the article, stochastic frontier analysis of peanut-production efficiency in the Southeastern region of the United States is conducted with a view of assessing the likely farm-level impacts of the 2002 Farm Act. Results indicate that, although quota ownership did not significantly impact inefficiency, it is likely that limitations on the quota's transferability to areas with better growing conditions were a significant cause of inefficiency. The acreage shifts and improved yields following the passage of the 2002 Farm Act support this conclusion. Certain farm characteristics, such as farm size and operator's education and age, were also important for efficiency.

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STOCHASTIC FRONTIER ANALYSIS OF SINGAPORE MANUFACTURING INDUSTRIES

The Singapore Economic Review ◽

10.1142/s0217590804000810 ◽

2004 ◽

Vol 49 (01) ◽

pp. 85-103 ◽

Cited By ~ 5

Author(s):

SOO-WEI KOH ◽

SHAHIDUR RAHMAN ◽

G. K. RANDOLPH TAN

Keyword(s):

Technical Efficiency ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

Learning By Doing ◽

Alternative Measure ◽

Efficiency Change ◽

Frontier Analysis ◽

Manufacturing Productivity ◽

Using Data ◽

Allocative Inefficiency

Previous papers on Singapore manufacturing productivity have focused almost exclusively on total factor productivity (TFP) growth rates and ignored the problem of measuring the extent of learning-by-doing. In this paper, we examine an alternative measure: the rate of technical efficiency change. Using data from 1974–1998, a translog production frontier is estimated. Following a conceptual framework popularised by Bauer (1990), productivity growth is decomposed into components arising from technical progress, technical efficiency change, a scale economies effect and an allocative inefficiency effect.

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Total Factor Energy Efficiency of China’s Industrial Sector: A Stochastic Frontier Analysis

10.20944/preprints201704.0068.v1 ◽

2017 ◽

Author(s):

Xiaobo Shen ◽

Boqiang Lin

Keyword(s):

Energy Efficiency ◽

Technical Efficiency ◽

Technical Change ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

Industrial Sector ◽

Scale Efficiency ◽

Efficiency Change ◽

Output Data ◽

Frontier Analysis

Based on stochastic frontier analysis and translog input distance function, this paper examines the total factor energy efficiency of China’s industry using input-output data of 30 sub-industries from 2002 to 2014, and decomposes the changes in estimated total factor energy efficiency into the effects of technical change, technical efficiency change, scale efficiency change and input-mix effect. The results show that during this period the total factor energy efficiency in China’s industry grows annually at a rate of 3.63%, technical change, technical efficiency change and input-mix effect contribute positively to the change in total factor energy efficiency, while scale efficiency change contributes negatively to it.

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Hope for Change: Is Diversifying Production Portfolios an Ideal Strategy to Boost Farming Efficiency in Afghanistan?

Progress in Development Studies ◽

10.1177/14649934211031745 ◽

2021 ◽

pp. 146499342110317

Author(s):

Hayatullah Ahmadzai

Keyword(s):

Production Efficiency ◽

Management Practices ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

Farm Households ◽

Crop Diversification ◽

Endogeneity Bias ◽

Frontier Analysis ◽

Farming Efficiency ◽

Production Efficiencies

In this article, I present empirical evidence on the extent of crop diversification and assess its merits as a strategy for improving production efficiency in Afghanistan. The transformed Herfindahl–Harshman index is used to measure the scale and magnitude of crop diversification. I find a compelling evidence that diversifying production portfolios significantly improves production efficiencies. This finding is critical, given that the data show that nearly a third of the farm households do not diversify, achieving, on average, about 52% of potential revenues. The estimated efficiency scores reveal that, on average, the farm households in our analytical sample of over 7,000 households achieve 74% of potential revenue, with nearly 15% of households realizing less than 50% and about 23% between 50% and 70% of potential revenue. These results infer that there exist substantial inefficacies in agricultural production that can be eliminated by employing improved management practices without having to use additional inputs and production resources and rising cost of production. Our results are robust to potential endogeneity bias in crop diversification; I account for the endogeneity problem in the stochastic frontier analysis, by employing a recent estimation approach, using instrumental variable techniques. Mapping the spatial distribution of crop diversification index and estimated efficiency scores across the country revealed that districts with higher diversification levels correspond to higher efficiency indices. Aside from crop diversification, other socio-economic factors also have critical implications for efficiency; households with access to farm assets (such as land, cattle, oxen and tractor) and extension services appear to realize substantially higher production efficiencies. A direct policy recommendation that can be generated from the findings of this study is that crop diversification should be given more recognition by policymakers to enhance productivity and resilience in agriculture.

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The Determinants of Technical Efficiency of Hazelnut Production in Azerbaijan: An Analysis of the Role of NGOs

Sustainability ◽

10.3390/su11164332 ◽

2019 ◽

Vol 11 (16) ◽

pp. 4332 ◽

Cited By ~ 4

Author(s):

Orkhan Guliyev ◽

Aijun Liu ◽

Gershom Endelani Mwalupaso ◽

Jarkko Niemi

Keyword(s):

Technical Efficiency ◽

Production Efficiency ◽

Stochastic Frontier Analysis ◽

Stochastic Frontier ◽

Stakeholder Involvement ◽

Cross Sectional ◽

Frontier Analysis ◽

Government Organizations ◽

Good Organization

The role of non-government organizations (NGOs) has been commendable in promoting sustainable farming. Through mobilization of existing resources and provision of training to farmers on various agriculture subjects, NGOs could trigger increased productivity and agricultural sustainability. However, empirical evidence on this claim is limited and no study recognizes the supporting conditions required for NGO intervention to improve productivity. Cross-sectional data from hazelnut farmers in Azerbaijan are used to evaluate the role of NGO intervention in improving farmers’ technical efficiency. To this end, stochastic frontier analysis (SFA) is applied to study hazelnut farmers’ production efficiency. Three different measures are employed to estimate NGO intervention: Training, subsidy and, a combination of training and subsidy. The results indicate that NGO intervention is not significant in influencing technical efficiency. This is attributable to the absence of good organization, innovation orientation, accountability and stakeholder involvement and support which are the necessary supporting conditions facilitating an enabling environment for NGO intervention to improve farmers’ technical efficiency. Therefore, we recommend policy directed at addressing these issues in order to simultaneously enhance farmers’ productivity and improve the functioning of the NGOs. Beyond NGO intervention, encouraging farmers to specialize in hazelnut production and allocating more suitable land for hazelnut production will also improve farmers’ technical efficiency significantly.

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