Добірка наукової літератури з теми "SLX tobit model"

Abstract To analyze fresh vegetable consumption using household survey data, the tobit model and a more flexible parameterization to the tobit model—the “double hurdle” model—were considered. Based on the likelihood ratio test, the tobit model was rejected against the “double hurdle” specification. Moreover, the results suggest that the tobit model underestimated the impact of the explanatory variables on fresh vegetable expenditures. Other results indicate that total food expenditures (proxy for income), age, household composition, sex, race, marital status, urbanization, region, and seasonality are all important determinants of fresh vegetable expenditures.

Regression analysis is used to study the relationship between dependent (response) variable with one or more independent (causal) variables. While response data were censored, then Tobit regression model could be applied. According to Greene (2003), censored data were data with incomplete observation or the dependent variable has a value of zero, while for the other observations have particular value. This research aimed to model dairy milk’s consumption from households at Bali Province. By using data from Survey SosialEkonomiNasional (SUSENAS) or Social Economy’s National Survey (SENS) for year 2012, 615 households were selected as sampling unit using simple random sampling technique, and found 123 households who consumed dairy milk. The independent variables in our model were last education level completed by head of household’s (X1), head of household’s work (X2), age of head of household’s (X3), amount of expenditure for food consumption’s (X4), number of household members (X5), and household income (X6), the response variable was budget for buying dairy milk (Y). From six independent variables, is found only last education level by head household and amount of expenditure for food consumption had siginficant effect on Y’s. The final Tobit regression model were obtained using AIC (Akaike Information Criterion) method is Y = -3314724 + 565429,7 X1 + 0,014278 X4 with pseudo R2 as much as 16.79 per cent.

This study analyzes the expenditure patterns of cruise ship passengers during stopovers in the Canary Islands, which is a key variable for evaluating the economic impact of this type of tourism from the point of view of local key stakeholders and residents. Information on cruisers’ expenditure was collected in each port of call by survey responses for six cruise seasons, during the period 2001–2015. Through a latent class model in a framework of censured regression, three distinct groups of passengers were identified. This approach fills a gap in the empirical tourism literature in order to achieve more accurate results to deal with cruise travelers’ heterogeneity. Moreover, the identification of these homogeneous groups is important so as to better design policies that allow for higher tourism economic impact onshore. Our findings suggest that it would be ideal to attract more class 1 cruise passengers.

This study presents an indicator to measure the performance of domestic water management that focuses on raw water consumption and sewage treatment by using the data envelopment analysis (DEA) technique. The data are extracted from twenty-two counties/cities in Taiwan covering the period of 2009–2011, and eventually sixty-six observations are obtained. We compare the management performance between urban and rural regions and examine the factor that affects the performance variation by using the Tobit regression. The results find that a spatial inequality exists across urban regions and rural regions. The analysis results derived from the Tobit model find that the extent rate of sewerage systems, the volunteer participation rate and the education level play significant roles in affecting management performance. An increase in each per cent of the extent rate of sewerage systems, public participation and high-level educated citizens may lead to an increase of management performance by 0.37339%, 0.9543% and 0.9756%, respectively.

With tourism carbon dioxide emission efficiency (TCDEE) as an undesired output, this study establishes an index system based on the inputs and outputs of TCDEE and measures the provincial TCDEE of China in 2010–2018, using the epsilon-based measure (EBM). In addition, the impactors of TCDEE were tested by the Tobit model. The main results are as follows: China’s TCDEEs had obvious provincial differences. Only six provinces reached the efficient frontier of TCDEE, namely, Beijing, Tianjin, Inner Mongolia, Shanghai, Jiangsu, and Guangdong. The other provinces failed to reach this state, leaving a room for improvement. Most eastern provinces had relatively high TCDEEs, while the central and western provinces had relatively low TCDEEs. In the sample period, the TCDEEs in eastern, central, and western parts all changed in the shape of letter N. The TCDEEs of the eastern part were much higher than those of the central and western parts. According to the results of the Tobit model, TCDEE is clearly enhanced by the urbanization level, strongly inhibited by industrial structure, technical progress, opening-up, and environmental regulation, and not significantly affected by the tourism level.

<abstract> <p>This paper presents a two-stage method combining data envelopment analysis (DEA) and a Tobit model to analyze the comprehensive operating efficiency of 28 airports in China in 2016. At the first stage, the DEA-BCC (Banker-Charnes-Cooper) model was employed to obtain the comprehensive operating efficiency of the combination of flight departure punctuality, non-cancellations, landing bridge rates from the perspective of airport infrastructure, surrounding airspace, route layouts, flight volume and weather. At the second stage, a Tobit model was used to analyze the influence of nine input variables from four aspects on obtained comprehensive operating efficiency, ultimately providing a clear and straightforward basis for formulating and testing policies. The comprehensive operating efficiency with this combination was further compared with each of the three efficiencies respectively. The important findings included the following: (1) The comprehensive operation efficiencies of most airports were greater than the individual efficiency; (2) These four types of operation efficiencies for most airports did not achieved DEA validity (100% efficiency), except for six airports (i.e., Haikou, Dalian, Jinan, Fuzhou, Nanning and Lanzhou); (3) These factors affecting each of the four types of operation efficiencies were different in that the number of terminals, duration of impact and average daily inbound and outbound flights had a negative impact on airport operational efficiency, while the average number of overnight aircraft per day and peak hour sorties had positive effects.</p> </abstract>

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.

The excessive use of inputs per unit of agricultural land poses a great threat to ecological sustainability. Using an input-oriented data envelopment analysis (DEA) model, this study analyzes ecoefficiency of intensive agricultural production in 31 provinces in China. The results show that the total efficiency of only six provinces can be considered fully efficient and that scale efficiencies are generally lower than technical efficiencies. Then, the spatial distribution of ecoefficiency is analyzed. The findings demonstrate that the provinces whose ecoefficiencies are maximal are primarily located in western China. The technical efficiencies in the western region are better than those in the eastern and middle regions. Imperfect scale efficiencies are distributed across all three regions. Furthermore, using the Tobit model, an analysis of the factors that influence ecoefficiency shows that the variables of farmland area per capita (FA), income per capita (IC), population per household (PH), and population burden coefficient (PB) have statistically significant impacts on total efficiency. The distinct effects of the variables on total efficiency are caused by their differential effects on technical efficiency and scale efficiency. Finally, suitable policies designed to improve ecoefficiency are proposed according to the local circumstances of each of the three regions.

Beekeeping delivers the rural people with sources of income, dietary use, pollination service and social benefit. In spite of the benefits obtained from beekeeping, farmer’s resistivity to adopt the recommended modern beekeeping technological components (packages) is among the major problem. Therefore, the objectives of this study were to examine the intensity of adoption of beekeeping technology package, and identify factors influencing adoption and intensity of adoption of beekeeping technology package in Sheko district. Stratified sampling procedure was used to select sample households. A total of 200 household heads were selected randomly from adopters and non-adopters categories of four potential beekeeping localities. Date was collected by using household survey and key informant interview and analyzed by using censured Tobit model. From sampled households 68% of respondents who have bee hive; the majority of them fall under medium adopters’ category. Variations in intensity of adoption of bee hive components were observed among adopter category. The output of censured Tobit model was indicate that six variables such as total livestock unit, apiary site visit by farmer, perceived cost of technology by farmer, distance of market center from home, knowledge of farmers for bee management and availability of bee hive technology were found to have significant influence on adoption and intensity of adoption of bee hive technological package. Therefore, agricultural policy and development interventions should give emphasis to the improvement of institutional support system so as to achieve wider adoption and intensity of adoption for small scale farmers.

Purpose This study aims to examine the determinants of poverty among microcredit beneficiaries in Pakistan Poverty Alleviation Fund. Design/methodology/approach The study is based on a nationwide survey of microcredit beneficiaries of Pakistan Poverty Alleviation Fund. Using the national poverty line, this study classified borrowers into the poor and the non-poor. A Tobit model was estimated to examine the determinants of poverty among the borrowers. Findings The model was found to fit the data well and six out of the ten specified independent variables are found to be statistically significant. Practical implications The results of the study can be helpful in fully characterizing poverty dynamics and in policy formulation in using microcredit to reduce poverty. Originality/value The paper is the first to examine the determinants of poverty among Pakistan Poverty Alleviation Fund recipients.

Overall, this thesis seeks to explore – using three case studies - the environmental and economic influences and outcomes of on-farm natural capital in the Australian agricultural landscape over space and time. In particular, it explored: 1) the spatial influences on the adoption of certified organic farming (which is used as a proxy indicator of natural capital conservation technologies) at a regional level in Australia using agricultural census data from 2010/11 and 2015/16; 2) the association between the presence of certified organic farming and regional biodiversity at the postcode level over sixteen years in South Australia; and 3) the association between farm land value and natural capital in the forms of native woody vegetation coverage and climate in South Australia over sixteen years. The first case study focused on Australia as a whole and modelled farmers’ adoption behaviour of certified organic farming (using it as a proxy for sustainable agriculture technologies to conserve on-farm natural capital such as soil, water, and biodiversity). Spatial diffusion of organic farming represents an interesting case study, given the large amount of skills and knowledge regarding management of natural resources that organic farmers need to apply/learn for their farms’ viability. Although farmers’ adoption and diffusion behaviour is well studied in the literature, modelling of the role of spatial spill-over effects on diffusion intensity, especially in regards to the adoption of organic farming, is not well known. This thesis uses national Australian agricultural census data from 2010/11 and 2015/16 and a SLX Tobit model (N=2,134) to model the influences on the intensity of the diffusion of organic farming (namely percentage of organic land holding) in regional areas, and found statistically significant local spatial spill-over effects from neighbouring regions’ characteristics. In addition, a higher share of organic farmland in regions is associated with regional characteristics such as: larger irrigated farms; lower stocking rates; increased proportion of grazing and horticultural land; increased labour supply; increased green vegetation; rural areas with low human population density; and higher community income. The second study explored the associations between farmers’ land use behaviour (i.e. the extent of certified organic farming in a region) and regional biodiversity outcomes (vascular plant and bird species richness) at the postcode level. This study put together a new dataset on certified organic farming presence and locations in South Australia, using databases from organic certifiers. The spatial association between biodiversity indicators and organic farming was analysed using a spatial Durbin error model, while controlling for the effects of landscape attributes, human population footprint, climate and productivity from 2001 to 2016 (N=5,456) in South Australia. The results found that increased organic farming presence in postcode areas had a statistically significant positive association with vascular plant species richness, but little to no statistically significant association was found for bird species richness. Environmental heterogeneity in terms of land cover diversity, elevation range, and plant productivity seems to be the other prime determinants of plant and bird species richness. The third study focused on the association between native woody vegetation on agricultural properties and their economic values in South Australia, using both sales and valuation prices of agricultural properties from 1998 to 2013 (N=10,513). Findings from the spatio-temporal Durbin model revealed that the presence of native woody vegetation on agricultural properties significantly increased the per hectare market price (i.e. price sold in the market), but at a decreasing rate as the proportion of vegetation increased. The marginal return of vegetation was highest for small size properties and lowest for larger properties. In addition, the direct effects of increased annual rainfall, increased soil natural productivity, increased market accessibility, proximity to locational amenities, smaller size properties, availability of irrigation, and higher commodity price were also positively capitalised into sales prices. On the other hand, increased drought and high soil erodibility significantly reduced per hectare sales prices. Comparing valuation price models with sales price models, it was found that the valuation prices seem to undervalue the presence of native vegetation on agricultural properties and hence provide weaker evidence of the value of on-farm natural capital in the South Australian context.
Thesis (Ph.D.) -- University of Adelaide, Centre for Global Food and Resources, 2021