Bivariate Selection Model Research Articles

Market participation and pastoral welfare in drought-prone areas: A dose-response analysis

The low market participation of pastoral livestock producers is a challenge to the development of a climate-resilient economy in drought-prone areas. Without deliberate and well-designed efforts to transform livestock production into a thriving profit-oriented commercial business, the development of pastoral societies will remain far-fetched. By applying bivariate selection model and dose-response function to a case study of 357 pastoral households in Ethiopia, this study examines pastoralists’ participation in goat markets and the impact of participation on per capita income, poverty headcount, and poverty gap. Our results show that the propensity and intensity of participation of pastoralists in goat marketing were influenced by flock size, transaction costs (TC), and access to veterinary services. An important finding in this study is that pastoralists are willing to pay for marketing services up to 97 % of the total variable TC, indicating the financial feasibility of public investment in the development of market infrastructure through cost recovery schemes. The results also reveal that participation in goat marketing has a positive impact on per capita income, poverty headcount, and poverty gap. These findings shed some light on more practical strategies for poverty reduction among pastoralists.

Earnings gap between men and women in the informal labor market in Cameroon

AbstractThis paper investigates the extent of gender earnings gap among informal labor market workers in Cameroon. We have used the 2010 employment and informal sector survey, the Heckman two‐step bivariate selection model, and the Neuman–Oaxaca–Ransom decomposition framework to address these concerns. We find that, compared to men, women are more likely to make the joint decision of participating in the labor market and choosing informal work. Findings also reveal that among informal workers, male workers have an earnings premium over their female counterparts. Returns to informal labor market experience are solidly consistent in narrowing the gender earnings gap. Among the self‐employed workers, male workers also enjoy an earnings premium. However, when the inverse Mills ratio is decomposed, the overall and the self‐employed gender discrimination virtually disappears. Meanwhile, female wage earners enjoy a wage premium and have an unambiguous education‐based advantage over their male counterparts. The implications of these findings support the wisdom that promoting the human capital development of the girl child would play a vital role in alleviating gender‐based differentials by eroding sociocultural conventions and advancing women's agency.

Access to healthcare in Portugal in the wake of the crisis. Not everything is money?

Unmet healthcare needs have been used to assess access to healthcare. In scenarios of recession and financial constraints on public policies, it is important to identify which factors besides income can be used to mitigate barriers to access. This was the focus of our study on Portugal's case in the wake of the crisis. We used 17,698 observations from the 5th National Health Survey (2014). We analyzed self-reported unmet needs for medical appointments and treatments, dental healthcare, and prescribed medications. We used a bivariate selection model, considering the fact that unmet needs could only be observed in the subsample of individuals that felt the need for healthcare. The risk of unmet needs for healthcare decreased in individuals with higher income and schooling levels and in the elderly and men. Exclusive coverage by the National Health Service increased the risk of unmet dental healthcare needs. The absence of a circle of close friends to whom one can ask for help and lack of trust in others increased the likelihood of unmet healthcare needs. Better health decreased the risk of unmet needs. While income is an important predictor of unmet needs, we found the impact of other factors such as gender, age, and education. Participation in informal groups reduced the likelihood of unmet needs. Individuals with more healthcare needs end up suffering additional risk.

The economics of land use reveals a selection bias in tree species distribution models

AbstractAimsIn human‐dominated ecosystems, the presence of a given species is the result of both the ecological suitability of the site and human impacts such as land‐use choices. The influence of land‐use choices on the predictions of species distribution models (SDMs) has, however, been often neglected. Here, we provide a theoretical analysis of the land‐use selection bias affecting classical SDMs in the case of either presence‐only or presence–absence datasets. Land‐use selection bias in the predictions of SDMs is then quantified for four widespread European tree species.LocationContinental France.MethodsWe describe a bivariate selection model (BSM) that estimates simultaneously the economics of land‐use choices and species responses to bioclimatic variables. The land‐use equation, based on an econometric model of landowner choices, is joined to an equation of species responses to bioclimatic variables.ResultsWe found a significant land‐use selection bias in all the species studied. The sign and the magnitude of the bias varied among species and were strongly related to the type of dataset used in the SDM calibration (presence‐only or presence–absence). In addition, the BSM estimates the spatial covariance between the probability of presence and the presence of compatible land use. We found that, depending on the species, sites with high ecological suitability could present a high probability of compatible land use (positive covariance) or a low probability (negative covariance).Main conclusionWe showed that the use of classical SDMs in human‐dominated areas can lead to strong miss‐estimations of actual species distributions and could therefore prevents sound projections of the effects of climate change. The proposed BSM represents a crucial step to account for the economic forces shaping species distribution in anthropized areas and paves the way for a direct assessment of trade‐offs and opportunities that may arise in a context of global change.

The built environment determinants of activity participation and walking near the workplace

Investigating the built environment determinants of commuting behaviors to and from the workplace has long been of interest to travel behavior researchers. Specific attention has centered on examination of how smart growth policies encourage both trip-chaining and active travel. Yet, limited research has investigated the impact of the built environment on activity participation and pedestrian travel once the worker has arrived at his/her workplace. A research omission that exists despite the prospect that built environment densification and diversification within employment districts may encounter less opposition from the local community and commuter. Our study investigates these identified gaps by analyzing how the built environment near an individual’s workplace as well as personal, household, and tour-related attributes relate to work-based sub-tour activity participation and walking for activity fulfillment. A bivariate selection model estimated the workplace built environment determinants of work-based sub-tour participation and the likelihood to initiate travel for these sub-tour activities on foot. Findings from this Portland, Oregon study revealed that design and diversity features predicted work-based sub-tour participation; while, the decision to walk to start a sub-tour was strongly associated with a workplace built environment characterized by a traditional neighborhood design and increased residential density.

Differentiating the Influence of Accessibility, Attitudes, and Demographics on Stop Participation and Frequency during the Evening Commute

Understanding the stop-making behavior of commuters is important for planners and policy makers in their efforts to improve travel demand models and to develop transportation demand management strategies. Previous studies have identified various determinants of commuters' stop generation. However, the way in which accessibility affects stop-making decisions is not clear. Further, few studies have considered the impacts of attitudes and predispositions, which extensively influence travel behavior. Using 1256 commuters in Northern California, this study investigates the influences of accessibility and attitudes on evening commute stop-making behavior. Based on a bivariate selection model, we found that the propensity of workers to make stops is dependent on their attitudes, work-related attributes, neighborhood accessibility, and sociodemographic characteristics, whereas the frequency of trip chaining is primarily determined by sociodemographic characteristics. Accordingly, if we offer commuters opportunities to stop through land-use and transportation policies, they may tend to do so, but the intensity of chaining behavior depends on household needs, the individual's role in the household, and personal time constraints.

A Sample Selection Model of Consumer Adoption of Computer Banking

We develop a bivariate probit selection model of consumer access and adoption that accounts for the cross equation correlations of the errors. The Survey of Consumers, collected by the University of Michigan, is the database used to estimate the model. We find a significant cross equation correlation between consumers’ perceived access and their use of computer banking. Based on our results, the bivariate selection model provides asymptotically more efficient estimates than does a single equation model because the bivariate selection model accounts for the sample selection bias associated with access. In addition, the bivariate selection model has a higher percentage of correctly predicted adopters than does the corresponding single equation (univariate) model.

Do the School Nutrition Programs Supplement Household Food Expenditures?

The extent to which the school nutrition programs-the National School Lunch Program (NSLP) and the School Breakfast Program (SBP)-supplement household food expenditures is an important indication of whether the objectives of the programs are being met. Using data from the National Evaluation of School Nutrition Programs and a bivariate selection model, this paper obtains estimates of such supplementation. Ifind that somewhat less than one-half of each additional dollar of NSLP benefits is used by households to supplement food expenditures, while all of each additional dollar of SBP benefits is allocated to such expenditures.

The effect of food stamps on labor supply: A bivariate selection model

Although there is a large literature on the effects of U.S. transfer programs on labor supply, this is the first such study of the Food Stamp Program. We model the nonlinearity and nonconvexity of the budget constraint and use maximum-likelihood methods to estimate hours- of-work functions for female heads of household, the largest recipient group in the program. We also model the joint response to AFDC and Food Stamps and we account for the existence of a significant number of eligible households who do not participate in the programs. A three- equation, bivariate selection model is estimated on a sample of eligible households with maximum likelihood.