scholarly journals Allocative Efficiency Analysis of Capsicum Cropping System under Tunnels

2021 ◽  
Vol 17 ◽  
pp. 502-509
Author(s):  
Hina Fatima ◽  
Lal K Almas ◽  
Bushra Yasmin
Keyword(s):  
Stochastic Frontier Analysis ◽  
Cropping Systems ◽  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Cropping System ◽  
Minimum Cost ◽  
Optimum Level ◽  
Frontier Analysis ◽  
Farm Inputs ◽  
Allocative Inefficiency

The focus of this study was to analyze the allocative efficiency of the capsicum cropping system under tunnels. The data is collected from those farmers that were cultivating the capsicum crops under the tunnels in Pakistan. Cultivation of the crops under tunnels has a rising trend in Pakistan. To achieve the best possible output with a minimum cost of newly opted farm technology can be beneficial if farmers have proper skill and know-how regarding the application of balanced use of inputs. The sample size was around 150 capsicum farms. The Stochastic Frontier Analysis (SFA) was used to examine the allocative efficiency of capsicum farms in Pakistan. The result of the study demonstrated that the average allocative efficiency of capsicum cropping systems under tunnels in Pakistan was around 65%. Around 35% of allocative inefficiency is present in the capsicum cropping system. Mismanagement of resources related to the utilization of farm inputs i one of the main reasons for allocative inefficiency. After removing the mismanagement of resources, most farms have another way of reducing allocative inefficiency. It is linked with reallocating inputs or changing the input combination used, to achieve an optimum level of capsicum output at a given level of input prices. Hence, the objective of adoption of advanced farm technologies along with balanced application of farm inputs will result in higher farm productivity and allocative efficiency.

scholarly journals Analisi Efisiensi Teknis, Alokatif dan Ekonomi Produksi Kubis di Kabupaten Karo

JURNAL AGRICA ◽  
2021 ◽  
Vol 14 (2) ◽  
Author(s):  
Esra Frandika Barus
Keyword(s):  
Economic Efficiency ◽  
Stochastic Frontier Analysis ◽  
New Technologies ◽  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Frontier Analysis ◽  
Managerial Skills ◽  
Low Efficiency ◽  
Farming Efficiency ◽  
Input Amount

The needs of cabbages tends to increase. Meanwhile, cabbage production is not optimal and just seasonal. However, demands of cabbages in export field are high so, it makes the cabbage farming is a strategic area to be developed. To increase production through increasing the use of inputs and using new technologies is relatively difficult because it requires high costs. Therefore,  increasing production by improving farming efficiency is a relevant option. Farmers’ managerial skills and their combining various production inputs ability will affect the production. Efficient use of input amount (allocative efficiency) and a good technical efficiency are important for increasing the production. Production of cabbage farming efficiency was analyzed by using stochastic frontier analysis (SFA). The results of the analysis showed that the technical, allocative and economic efficiency of cabbage farming in Karo District had not achieved. The average of the technical, allocative and economic efficiency values of the respondent were 0.697, 0.374 and 0.215. These results indicate that low efficiency is a contributing factor to the low productivity of the cabbage farmer respondents' in Karo District. Several factors that have a significant effect on the efficiency are farming experience, labor ratio, land ownership status and cabbage lifespan.

scholarly journals Productivity of Nations: A Stochastic Frontier Approach to TFP Decomposition

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Jorge Oliveira Pires ◽  
Fernando Garcia
Keyword(s):  
Technical Change ◽  
Stochastic Frontier Analysis ◽  
Scale Effects ◽  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Developing Nations ◽  
Production Frontier ◽  
Stochastic Frontier Approach ◽  
Frontier Analysis ◽  
Long Period

This paper tackles the problem of aggregate TFP measurement using stochastic frontier analysis. We estimate a world production frontier for a sample of 75 countries over a long period. The “Bauer-Kumbhakar” decomposition of TFP is applied to a smaller sample in order to evaluate the effects of changes in efficiency (technical and allocative), scale effects, and technical change. Estimated technical efficiency scores are compared to productivity indexes offered by nonfrontier studies. We conclude that differences in productivity are responsible for virtually all the differences of growth performance between developed and developing nations and that a large part of this is due to allocative efficiency.

STOCHASTIC FRONTIER ANALYSIS OF SINGAPORE MANUFACTURING INDUSTRIES

2004 ◽  
Vol 49 (01) ◽  
pp. 85-103 ◽  
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.

scholarly journals Allocative Inefficiency of General Hospitals in Poor Countries: A Case Study of Uganda

2021 ◽  
Vol 3 (1) ◽  
pp. 128-143
Author(s):  
Max Ajanga
Keyword(s):  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Healthcare Services ◽  
General Hospitals ◽  
Frontier Analysis ◽  
Poor Countries ◽  
The Public ◽  
Exerted Pressure ◽  
Allocative Inefficiency

The increasing costs of healthcare arising largely from the growing population and emergence of non-communicable diseases have exerted pressure on healthcare budgets in poor countries. With a funding gap of 7% to realize WHO recommended target of 15 percent of GDP in Uganda, there is a need for hospitals to be efficient in allocation of financial resources in order to provide the required level of healthcare services. Most studies on Uganda have focused on the technical inefficiency of general hospitals and evidence on their allocative inefficiency is limited.  Understanding the sources of inefficiency in the allocation of finances in general hospitals in Uganda is important to improve their performance. The purpose of this study was to determine the allocative inefficiency of the general hospitals in Uganda in order to provide a source of misuse of public allocations to a particular general hospital. Panel data from 22 general hospitals for the period 1997-2007 were used. Allocative inefficiency was estimated using Stochastic Frontier Analysis. The findings show that general hospitals are systematically allocatively inefficient in distributing the public funds given to them.  The allocative inefficiencies value is high on payments of employee benefits (34.8 percent), followed by the purchase of drugs (29.2%) and lastly, costs on utilities like electricity and water (14.1%). To address the existing allocative inefficiencies, general hospitals in Uganda can improve the process of hiring of labour and management of staff payroll; monitor procurement of drugs, and reduce wastages in the use of utilities.

scholarly journals Empirical Measurement of Competition in the Thai Banking Industry

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 44
Author(s):  
Jirawan Prayoonrattana ◽  
Thanarak Laosuthi ◽  
Bundit Chaivichayachat
Keyword(s):  
Stochastic Frontier Analysis ◽  
Banking Industry ◽  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Banking System ◽  
Banking Competition ◽  
Frontier Analysis ◽  
Average Value ◽  
Lerner Index ◽  
Efficiency And Effectiveness

The degree of competition in the banking industry can be observed and measured by two approaches, structural and nonstructural. Based on these two approaches, there are various indicators, which are different factors and methods. This paper aims to provide calculations, determine a good indicator, and assess the competitive environment of the Thai banking industry. Specifically, there are four indicators—concentration ratio, Herfindahl–Hirschman Index, Lerner Index, and Panzar–Rosse H statistic—which are widely used to examine the efficiency and effectiveness of policies in the banking industry. The findings indicate that the Lerner Index, calculated by stochastic frontier analysis, is the most reliable indicator of the banking competition environment in Thailand. It has a range of 0.36 to 0.60 and an average value of 0.40. Furthermore, during the period of study, the degree of Thai banking competition had a tendency to increase over time, which reflects an increase in allocative efficiency of resources in the banking industry. This is in accordance with the Financial Sector Master Plan of the country. However, this result probably leads to instability of the financial system. Therefore, policy-makers should carefully regulate competition policy by considering the systematic risk of the banking system at the same time.

Determinants of Cost Inefficiency of Maize Farming in Different Agro-climatic Regions of Sikkim, India

2020 ◽  
Vol 16 (2) ◽  
pp. 177-198
Author(s):  
Pradyut Guha ◽  
Tiken Das
Keyword(s):  
Cost Function ◽  
Best Practice ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Minimum Cost ◽  
Primary Data ◽  
Frontier Analysis ◽  
Farm Level ◽  
Climatic Regions ◽  
Cost Inefficiency

The present study makes an attempt to analyse farm level cost inefficiency of maize farming and its determinants in different agro-climatic regions of Sikkim. The primary data for the study were collected during the third and fourth quarter of 2018 from different agro-climatic regions of Sikkim. Both data envelopment and stochastic frontier analysis were used for measurement of the farm level inefficiency across different agro-climatic regions of the study area. Based on the Cobb–Douglas cost function for maize output, the article simultaneously estimated stochastic frontier cost function and examined the effect of exogenous factors on farm level cost inefficiency. The results of this study showed that, on an average, the farmer incurred cost which was 8 per cent to 72 per cent above the minimum cost defined by the best practice frontier. Further, cost inefficiency was relatively higher among the farmers in temperate agro-climatic region. Greater cost inefficiency seems to be directly associated with remoteness of farmland from input market. The study also found that the additional years of farming experience and farming in the rented plots were useful in reducing cost inefficiency.

scholarly journals Farm level allocative efficiency of rice production in Gulu and Amuru districts, Northern Uganda

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Micheal Okello ◽  
Jackline Bonabana-Wabbi ◽  
Basil Mugonola
Keyword(s):  
Stochastic Frontier ◽  
Allocative Efficiency ◽  
Minimum Cost ◽  
Rice Production ◽  
Farm Size ◽  
Northern Uganda ◽  
Smallholder Farming ◽  
Cross Sectional ◽  
Farm Level ◽  
Allocative Inefficiency

Abstract Smallholder farming, predominant in Uganda, is characterized by low productivity for most crops including rice which is gaining prominence as both a food and income crop. The low productivity is mostly attributed to allocative inefficiency. Allocative efficiency (AE) considers farmers’ ability to allocate resources efficiently, by producing the maximum possible output at minimum cost. Increasing AE requires an understanding of the specific sources of inefficiency that vary across farm enterprises, geographically and temporally. A cross-sectional study was carried out in Gulu and Amuru districts of Northern Uganda to assess the sources of farm-level allocative inefficiency in rice production using the stochastic frontier approach. Data were collected from a random sample of 200 smallholder rice farmers. Results show that the mean AE was 75%. Household size, distance to trading centre, farm size, number of crop enterprises, use of hired labour, use of ox-plough, and access to credit had significant effects on AE. We recommend adoption of technologies such as the use of ox-ploughs and reallocation of farm resources especially labour.

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