Two-stage Sample Selection Model Research Articles

The drivers and intensity of adoption of beekeeping in northwest Ethiopia

BackgroundBeekeeping activity is carried out in most parts of Ethiopia. However, despite the favorable agro-ecology for beekeeping practices and the high number of bee colonies the country is endowed with, the level of beekeeping adoption is low.MethodsThis study was conducted to identify determinants of the decision to adopt beekeeping, and the intensity of adoption by using a cross-sectional data collected from 772 rural households in Northwest Ethiopia. Stratified random sampling method was used to select the households, and the data were collected using a questionnaire. To achieve the objectives, Heckman two-stage sample selection model was used.ResultsThe result of the first step Heckman model revealed that age and educational level of the household head, household size, extension visits, training, incentive, home consumption of honey, major economic activities of the household, perception towards better hives, distance to the nearest marketplace, the number of years the household stayed in the village, and location were the significant variables influencing rural households’ beekeeping adoption decision. The second step Heckman model revealed that livestock holding of a household head, number of extension visits, credit use, presence of honey bee pests, whether a household is engaged in swarm catching practices, and major economic activities of a household head were the variables that influence the intensity of beekeeping adoption significantly.ConclusionsThe findings of the study can be used to make evidence-based policy interventions to improve beekeeping adoption and the intensity of beekeeping adoption by rural households, which could also help to improve their livelihoods.

Empirical Analysis of Multiple Infrastructural Covariates: An Application of Gravity Model on Asian Economies

This study assesses the simultaneous influence of hard and soft infrastructural indicators on exports in 46 Asian states from 2001 to 2017 with panel data. We constructed two main aggregate indicators such as hard infrastructure and soft infrastructure with fifteen primary or sub-group variables. The infrastructural aggregate indicator contains four groups: transportation, communication, and financial infrastructural and border-transport efficiency. These indicators are taken into account in estimating the influence on export levels and probability between bilateral Asian trading partners by using factor analysis, the augmented gravity model and the two-stage sample selection model. Findings show that simultaneous improvement in hard infrastructure such as transport and telecommunication improved bilateral exports. Similarly, simultaneous improvement in soft infrastructure like financial infrastructure and border-transport efficiency increased bilateral exports in Asian countries. For both export and import, the coefficients of infrastructural indicators have a positive impact and are statistical significant at each econometric scale. Improving financial infrastructure is important in achieving a higher level of trade volume and increasing the probability of exporting between partners. We also examine the evidence on the complementarity or substitutability between hard and soft infrastructure as captured by indicators which shows us that main aggregate indicators have a lower impact on exports.

Determinants of Awareness and Adoption of Improved Rice Varieties in North Central, Nigeria

The Nigerian rice sector and WARDA has adopted several development initiatives in the past years towards increasing development and production of improved rice varieties. However, the country has not been able to produce enough rice for her teeming population and as such has to be bridged by rice imports. Therefore, factors influencing awareness and adoption of improved rice varieties were examined. Data collected from the rice baseline field survey designed by AfricaRice from Nasarawa and Benue states in 2013 was used. Multistage sampling technique was used to obtain a sample of 149 rice farmers. Probit regression and heckman two-stage sample selection model was then used to analyze the data. Results revealed that 95.3% of the sampled households were aware of improved rice varieties while 87.25% had grown at least one of the improved rice varieties at the time of visit. In the awareness model, access to media was positive and statistically significant at 5%. Access to credit, access to media, farm size, gender, household size and agricultural income significantly influence the probability and intensity of adoption in the study area. Credit facilities should be provided at no collaterals with very low interest rates for adoption of improved rice varieties in the study area.

Impact of start-up support through guided preparation

Purpose– The purpose of this paper is to examine if and how entrepreneurial assistance programs, through guided preparation, affect start-up success.Design/methodology/approach–This study uses Heckman's two-stage sample selection model to predict the effect of contact and interactions with entrepreneurial support programs on start-up outcomes while taking into account the entrepreneur's self-selection into obtaining support from these programs.Findings– The results indicate that, after controlling for individual characteristics, activities undertaken during the start-up process, organizational characteristics and external factors, guided preparation contributes to a greater likelihood of achieving positive start-up outcome. This finding holds even after controlling for the entrepreneur's self-selection into contacting and using outside assistance.Research limitations/implications– Results suggest that self-selection bias remains a concern when studying the impact of assistance programs on start-up outcomes. Future research should make sure to address self-selection in their analysis.Practical implications– The study's results have implications for the design of start-up programs. It highlights the importance of delivery structures that are fluid, flexible, interactive, experiential, and tailored to the individual entrepreneur's needs.Originality/value– This study focusses on assistance programs broadly defined (includes many different types of programs) and provides an empirical analysis that addresses self-selection.

Effects of Urban Spatial Form on Individuals’ Footprints: Empirical Study Based on Personal GPS Panel Data from Rotterdam and Eindhoven Area

It is crucial for urban planners to understand the effects of physical planning on individuals’ behavior. A well-designed built environment allows individuals to engage in activities at shorter travel distances, using environmentally friendly transport modes. This paper examines how individuals’ CO2 footprints co-vary with urban form indicators. GPS technology was used to collect activity-travel data. The data for the analyses were collected between May 2012 and July 2013 covering more than 200 respondents from the Eindhoven and Rotterdam regions, the Netherlands. The data includes personal and household characteristics, detailed travel information, GPS traces and detailed car information, including brand, type and year of production. A Bayesian network was used to extract activity-travel diaries from the collected GPS traces, while a Web-based prompted recall survey instrument was used to validate the imputed data. To identify their living environment, respondents’ home coordinates were matched with map data from the municipality. A two-stage sample selection model was used to estimate the effects of physical planning on individual transport mode choice and daily CO2 emissions.

Dynamics between patent latent variables and patent price

This paper focuses on studying the relationship between patent latent variables and patent price. From the existing literature, seven patent latent variables, namely age, generality, originality, foreign filings, technology field, forward citations, and backward citations were identified as having an influence on patent value. We used Ocean Tomo's patent auction price data in this study. We transformed the price and the predictor variables (excluding the dummy variables) to its logarithmic value. The OLS estimates revealed that forward citations and foreign filings were positively correlated to price. Both the variables jointly explained 14.79% of the variance in patent pricing. We did not find sufficient evidence to come up with any definite conclusions on the relationship between price and the variables such as age, technology field, generality, backward citations and originality. The Heckman two-stage sample selection model was used to test for selection bias.

Generators' bidding behavior in the NYISO day-ahead wholesale electricity market

This paper proposes a statistical and econometric model to analyze the generators' bidding behavior in the NYISO day-ahead wholesale electricity market. The generator level bidding data show very strong persistence in generators' grouping choices over time. Using dynamic random effect ordered probit model, we find that persistence is characterized by positive state dependence and unobserved heterogeneity and state dependence is more important than unobserved heterogeneity. The finding of true state dependence suggests a scope for economic policy intervention. If NYISO can implement an effective policy to switch generators from higher price groups to lower price groups, the effect is likely to be lasting. As a result, the market price can be lowered in the long-run. Generators' offered capacity is estimated by a two-stage sample selection model. The estimated results show that generators in higher-priced groups tend to withhold their capacity strategically to push up market prices. It further confirms the importance of an effective policy to turn generators into lower price groups in order to mitigate unexpected price spikes. The simulated market prices based on our estimated aggregate supply curve can replicate most volatility of actual DA market prices. Applying our models to different demand assumptions, we find that demand conditions can affect market prices significantly. It validates the importance of introducing demand side management during the restructure of electricity industry.

Cost analysis in a Medicaid program for patients with bipolar disorder who initiated atypical antipsychotic monotherapy

ABSTRACTBackground: Use of atypical antipsychotics in treatment of patients with bipolar disorder is increasingly common, yet few studies have systematically investigated or compared medical costs associated with use of specific atypical antipsychotics.Objective: To evaluate the direct healthcare costs associated with olanzapine, risperidone, or quetiapine monotherapy among Medicaid patients diagnosed with bipolar disorder (ICD-9: 296.4x-296.8x).Methods: North Carolina Medicaid patients with bipolar disorder were followed for 12 months after initiation of atypical antipsychotic monotherapy (index date). They had no bipolar-related medical visit, hospitalization, or use of atypical antipsychotics 90-days prior to the treatment initiation. Costs of index drug, all bipolar-related medical care, and all health-related costs were examined. A two-stage sample selection model was employed to account for potential confounders and sample selection bias. Medication adherence measures using cumulative medication acquisition and cumulative medication gap were calculated as separate outcomes.Results: Inclusion criteria were met by 838 continuously eligible patients (393 olanzapine, 262 risperidone, 183 quetiapine). Drug-taking adherence was similar across the drug cohorts. After adjusting for potential confounders, patients taking olanzapine incurred $863 (p < 0.001) and $449 higher (p < 0.01) index drug costs than patients taking risperidone and quetiapine, respectively. Bipolar-related medical costs for patients taking olanzapine were higher ($616; p = 0.06) than for patients taking risperidone at 10% significance level, while such costs for patients taking olanzapine and quetiapine were similar. Total health-related costs did not differ across patient cohorts, including or excluding the index drug costs.Limitations: The final study sample is a highly selected one based on the study design. This sample may not represent the entire bipolar population. The criteria used to guard against omitting bipolar disorder patients misdiagnosed with major depression need further validation.Conclusions: No evidence was identified that there is any difference for total health-related costs in using olanzapine, risperidone, or quetiapine monotherapy to treat bipolar disorder, from a Medicaid payer's perspective. The clinical difference between these atypical antipsychotic drugs for the treatment of bipolar disorder could be of more interest than the economic difference.

Analysis of Variations in Vehicle Ownership Expenditures

There is significant concern about the high cost of vehicle ownership, although several authors have acknowledged in the literature that owning and operating a vehicle have accessibility benefits. This paper uses Consumer Expenditure Survey data to explore vehicle ownership expenditures of American households. Differences in vehicle ownership expenditures among urban and rural households are estimated in different regions of the United States, controlling for socioeconomic, demographic, life cycle, and other household factors. First, patterns in vehicle ownership expenses are examined over a year. Three models of expenditures for vehicle ownership are estimated: the first model is estimated by ordinary least squares, the second is a Tobit model estimated to take into account zero expenditures on vehicles by a segment of households during the survey year, and the third model is a two-stage sample selection model that explicitly models first the household vehicle ownership decision and then, given that firs...

Analysis of Variations in Vehicle Ownership Expenditures

There is significant concern about the high cost of vehicle ownership, although several authors have acknowledged in the literature that owning and operating a vehicle have accessibility benefits. This paper uses Consumer Expenditure Survey data to explore vehicle ownership expenditures of American households. Differences in vehicle ownership expenditures among urban and rural households are estimated in different regions of the United States, controlling for socioeconomic, demographic, life cycle, and other household factors. First, patterns in vehicle ownership expenses are examined over a year. Three models of expenditures for vehicle ownership are estimated: the first model is estimated by ordinary least squares, the second is a Tobit model estimated to take into account zero expenditures on vehicles by a segment of households during the survey year, and the third model is a two-stage sample selection model that explicitly models first the household vehicle ownership decision and then, given that first decision, the expenditure intensity decision for the subsample of households that own vehicles. It was found that locational and regional effects were not constant but that urban–rural differences depended on region, and differences among regions depended on whether a household was located in an urban or a rural area.