scholarly journals FACTORS AFFECTING HEALTH SPENDING OF FARMERS IN THE TRA VINH PROVINCE

2018 ◽  
Vol 1 (29) ◽  
pp. 9-19
Author(s):  
Trinh Thi Thuy Nguyen ◽  
Xuyen Thi Kim Nguyen ◽  
An Van Vu Nguyen
Keyword(s):  
Health Care ◽  
Regression Model ◽  
Health Expenditure ◽  
Household Head ◽  
Health Spending ◽  
Research Data ◽  
Probit Regression ◽  
Tobit Regression ◽  
Factors Affecting ◽  
Tobit Regression Model

The objective of the article is to identify the factors affecting the decision and the amount of money spent on health care by households in Tra Vinh province. The research data were collected from 200 households in Tra Cu and Cau Ke Districts in Tra Vinh province. Based on the application of Probit regression model, the research team identified the major decisions affecting the health expenditure of households. The estimation results show that the factors affecting the inclination to spend money on the health of the farmers are: Ethnicity; Age of household head; Educational attainment of the household head; Distance from the residence to the nearest health establishments; Economic situation of households. Tobit regression model identified the factors that affect the amount of money spent on health care of the household.

Operating Efficiency Analysis of Energy Industries in Taiwan

2013 ◽  
Vol 869-870 ◽  
pp. 612-620 ◽  
Author(s):  
Nattanin Ueasin ◽  
Anupong Wongchai
Keyword(s):  
Regression Model ◽  
Stock Exchange ◽  
Efficiency Score ◽  
Operating Efficiency ◽  
Data Envelopment ◽  
Tobit Regression ◽  
Factors Affecting ◽  
Taiwan Stock Exchange ◽  
Energy Companies ◽  
Tobit Regression Model

The energy business has played an important role in an economic growth of Taiwan because the market share is in the high value that can make a significant contribution towards regional and local employment. However, Taiwan is lack of energy resources, making the country highly relies on an import for more than 98 percent of its all energy. At present, a top priority of the countrys policy is to develop clean, sustainable, independent, and efficient energy in order to eliminate the vulnerability from external disruption. Therefore, this research aims to assess the operating efficiency and to analyze factors affecting the efficiency scores of the registered energy companies in the Taiwan Stock Exchange (TWSE) recorded during 2003-2012. The super-efficiency data envelopment analysis (SE-DEA) was initially applied to reveal the additional efficiency scores, followed by the Tobit regression model used to analyze what factors determine the efficiency scores. The empirical results showed that seven DMUs performed efficiently, ranking from 7.29 to 1.02. The company with the best operating performance was Taiwan Cogeneration Corporation (TCC), while the Great Taipei Gas Corporation (GTG) revealed the worst efficiency score. Furthermore, the Tobit regression model explained that the higher number of the local employees, the greater the efficiency scores were. Besides, the lower number of the shareholders, the greater the efficiency scores were. As a result, the Taiwans government is supposed to encourage all energy companies to have a higher number of local employees and shareholders to increase their efficiency scores.

scholarly journals Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Lixia Liu ◽  
Xueli Zhan
Keyword(s):  
Random Forest ◽  
Regression Model ◽  
Turnover Rate ◽  
Listed Companies ◽  
Tobit Regression ◽  
Random Forest Regression ◽  
Factors Affecting ◽  
Agricultural Enterprises ◽  
Inventory Turnover ◽  
Tobit Regression Model

Agricultural enterprises play a significant role in China’s economic development. However, compared with other enterprises, agricultural enterprises are facing serious financial problems. Financing difficulty is essentially a question of financing efficiency. Based on the DEA method, this paper evaluates the financing efficiency of 39 agricultural listed companies in China from 2013 to 2017. The results suggest that the financing efficiency is generally low, and the Total Factor Productivity of agricultural enterprises’ financing has a tendency to decrease first and then increase. The influencing factors of financing efficiency are analyzed using the Tobit regression model and the random forest regression model. And we find the following: (1) The random forest regression model significantly outperformed the Tobit regression model, with determination coefficients (R2) greater than 0.9 in full sample sets. (2) Total liability, financial expenses, return on total assets, and inventory turnover rate are important factors affecting financing efficiency of agricultural listed companies. (3) Return on total assets and inventory turnover rate promote the financing efficiency, while total liability and financial expenses reduce financing efficiency. Finally, the paper makes some suggestions for the financing of agricultural enterprises.

scholarly journals Tingkat Efisiensi Perbankan Syariah Pasca Spin Off Dengan Metode Two-Stage Data Envelopment Analysis

AL-TIJARY ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 13-24
Author(s):  
Sallsa Khairunnisa
Keyword(s):  
Regression Model ◽  
Significant Influence ◽  
Third Party ◽  
Islamic Banks ◽  
Tobit Regression ◽  
Two Stage ◽  
Factors Affecting ◽  
Operating Income ◽  
Efficiency Performance ◽  
Tobit Regression Model

This study aims to determine the level of efficiency of Islamic banks after the spin off during the period of 2011-2016. To measure the performance, Two-Stage Data Analysis Envelopment (DEA) is used. The first step of this method is measuring efficiency performance of Islamic banks using DEA method with CRS assumption. The second step estimating factors affecting the efficiency performance using Tobit regression model. The determination of input and output variables in this study uses intermediation approaches. Input variable consists of Fix assets, Third Party Fund (DPK) and Operating Expenses, while the output variable consists of Total financing and Operating Income. The results of the research shows the level of efficiency of BNI Syariah during the 2011-2016 period is still not efficient with 99%, while the level of efficiency of BJB Syariah from 2011 to 2016 is 98%. The result of Tobit regression model showed that Total assets and ROA has a positive and significant to the level of efficiency of BNI Syariah , while the coefficient of FDR and NPF are not significant influence. Furthermore, that influence the level of efficiency of BJB Syariah is total assets, while the coefficient of ROA, FDR and NPF are not significant influence.

scholarly journals Allocative efficiency and adoption of improved maize variety: A case of eastern hills of Nepal

2020 ◽  
Vol 3 (1) ◽  
pp. 148-159
Author(s):  
Tilak Katel ◽  
Bhishma Raj Dahal ◽  
Sandesh Bhatta
Keyword(s):  
Regression Model ◽  
Insect Pest ◽  
Allocative Efficiency ◽  
Climatic Conditions ◽  
Probit Regression ◽  
Maize Production ◽  
Factors Affecting ◽  
Face To Face ◽  
Maize Variety ◽  
Structured Questionnaire

Production and  profit from maize farming can be substantially increased by allocating resources efficiently and adopting improved maize variety. In this context, a study was undertaken to determine the allocative efficiency and factors affecting adoption of improved maize variety in Eastern hills of Nepal. Random sampling was conducted in eastern part of Khotang district namely, Halesi municipality and Diktel Rupakot Majuwagadi municipality during month of March 2019. Pretested semi-structured questionnaire was administered among 80 randomly selected farmers cultivating maize since last two years. Face to face interview was scheduled to obtain data. Cobb Douglas production function was used to determine allocative efficiency; probit regression model was launched to determine factors affecting adoption of improved maize variety.  Significant positive relation of cost of seed, planting, and weeding with income has suggested to increase expenditure on certified maize seed over own farm seed, line sowing over broadcasting, and weeding. The model revealed that increasing all the factors of production by 100% would result in increase in income by 71.83%. Furthermore, cultivating improved maize variety is more profitable than own farm seed. Probit regression model showed that, farmers who have received training, who were member of cooperatives and who have received high schooling were more likely to adopt open-pollinated improved maize variety. Unavailability of inputs (seed, fertilizer, and labor), insect pest attack and adverse climatic conditions were major constraint of maize farming. Therefore, it would be better to suggest maize producers to increase expenditure on seed; make maize field weed free and adopt line sowing method. In addition, providing training, increasing access over inputs and encouraging farmers towards cooperatives could be virtuous for sustainable maize production.

scholarly journals Analysis of Severe Injury Accident Rates on Interstate Highways Using a Random Parameter Tobit Model

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Minho Park ◽  
Dongmin Lee
Keyword(s):  
Regression Model ◽  
Unobserved Heterogeneity ◽  
Random Parameter ◽  
Tobit Model ◽  
Severe Injury ◽  
Parameter Estimates ◽  
Random Parameters ◽  
Tobit Regression ◽  
Accident Rates ◽  
Tobit Regression Model

In this study, a random parameter Tobit regression model approach was used to account for the distinct censoring problem and unobserved heterogeneity in accident data. We used accident rate data (continuous data) instead of accident frequency data (discrete count data) to address the zero cell problems from data where roadway segments do not have any recorded accidents over the observed time period. The unobserved heterogeneity problem is also considered by using random parameters, which are parameter estimates that vary across observations instead of fixed parameters, which are parameter estimates that are fixed/constant over observations. Nine years (1999–2007) of panel data related to severe injury accidents in Washington State, USA, were used to develop the random parameter Tobit model. The results showed that the Tobit regression model with random parameters is a better approach to explore factors influencing severe injury accident rates on roadway segments under consideration of unobserved heterogeneity problems.

Tobit model-based analysis on the influencing factors of wheat loss during harvesting by the combine harvester

2021 ◽  
pp. 1-14
Author(s):  
Hong Ji ◽  
Xun He ◽  
Li Ding ◽  
Zhe Qu ◽  
Wenkang Huang ◽  
...  
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loss Rate ◽  
Henan Province ◽  
Normal Operation ◽  
Tobit Regression ◽  
Wheat Field ◽  
Combine Harvester ◽  
Main Factors ◽  
Tobit Regression Model

Based on the investigation data of wheat mechanized harvest in eight major wheat producing areas from the south to the north of Henan Province, the main factors affecting wheat mechanized harvest loss were identified and the influence of each factor was decomposed. In this article, the loss rate of wheat mechanical harvest was calculated by using the method of artificial measurement of wheat yield in the field, and the influencing factors of wheat mechanical harvest operation in 8 regions of Henan province were treated and analyzed by using Tobit regression model. In this paper, the loss rate of wheat mechanical harvest was calculated by using the method of wheat field artificial yield measurement and the influencing factors of wheat mechanical harvest operation in eight regions of Henan province were treated and analyzed by using Tobit regression model. The results show that the average harvest loss rate in the field amounts to 2.96%, the average harvest loss rate at the edge of field amounts to 3.06%, whereas the loss rate in the normal operation area amounts 2.86%. The main factors that caused the harvest loss of wheat field machinery were the maturity of wheat, the area of operation field, the diseases and pests, weather conditions and the accumulated working hours of harvester drivers in a single day. Therefore, the main technical measures to reduce the operation loss of wheat combine harvester were put forward to provide a theoretical basis for promoting the deep integration of agricultural machinery and agronomy.

scholarly journals An Experimental Investigation of Consumer Willingness to Pay for Non-GM Foods When an Organic Option Is Present

2006 ◽  
Vol 35 (2) ◽  
pp. 374-385 ◽  
Author(s):  
John C. Bernard ◽  
Chao Zhang ◽  
Katie Gifford
Keyword(s):  
Experimental Investigation ◽  
Willingness To Pay ◽  
Regression Model ◽  
Tobit Regression ◽  
Milk Chocolate ◽  
Gm Foods ◽  
Consumer Demographics ◽  
Tortilla Chips ◽  
Marginal Effects ◽  
Tobit Regression Model

This research compared bids that consumers placed on non genetically modified (GM), organic, and conventional versions of food products in order to determine if the organic market well serves those seeking to avoid GM foods. Auction experiments using potato chips, tortilla chips, and milk chocolate were conducted with 79 subjects. Bids were modeled as a function of consumer demographics using a heteroskedastic tobit regression model. Results with the non-GM attribute nested into the organic characteristic showed that the latter's marginal effects were insignificant. This suggested the potential to further develop non-GM products for consumers not willing to pay extra for the remaining organic attributes.

Sign in / Sign up

Export Citation Format

Share Document