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Standard stratified sampling (SSS) is a popular non-random sampling scheme. Maximum likelihood estimator (MLE) is inconsistent if some sampled strata depend on the response variable Y (‘endogenous samples’) or if some Y-dependent strata are not sampled at all (‘truncated sample’ – a missing data problem). Various versions of MLE have appeared in the literature, and this paper reviews practical likelihood-based estimators for endogenous or truncated samples in SSS. Also a new estimator ‘Estimated-EX MLE’ is introduced using an extra random sample on X (not on Y) to estimate the distribution EX of X. As information on Y may be hard to get, this estimator's data demand is weaker than an extra random sample on Y in some other estimators. The estimator can greatly improve the efficiency of ‘Fixed-X MLE’ which conditions on X, even if the extra sample size is small. In fact, Estimated-EX MLE does not estimate the full FX as it needs only a sample average using the extra sample. Estimated-EX MLE can be almost as efficient as the ‘Known-FX MLE’. A small-scale simulation study is provided to illustrate these points.

Applied econometric analysis is often performed using data collected from large-scale surveys. These surveys use complex sampling plans in order to reduce costs and increase the estimation efficiency for subgroups of the population. These sampling plans result in unequal inclusion probabilities across units in the population. The purpose of this paper is to derive the asymptotic properties of a design-based nonparametric regression estimator under a combined inference framework. The nonparametric regression estimator considered is the local constant estimator. This work contributes to the literature in two ways. First, it derives the asymptotic properties for the multivariate mixed-data case, including the asymptotic normality of the estimator. Second, I use least squares cross-validation for selecting the bandwidths for both continuous and discrete variables. I run Monte Carlo simulations designed to assess the finite-sample performance of the design-based local constant estimator versus the traditional local constant estimator for three sampling methods, namely, simple random sampling, exogenous stratification and endogenous stratification. Simulation results show that the estimator is consistent and that efficiency gains can be achieved by weighting observations by the inverse of their inclusion probabilities if the sampling is endogenous.

This chapter proposes M-estimators of a fractional response model with an endogenous count variable under the presence of time-constant unobserved heterogeneity. To address the endogeneity of the right-hand-side count variable, I use instrumental variables and a two-step procedure estimation approach. Two methods of estimation are employed: quasi-maximum likelihood (QML) and nonlinear least squares (NLS). Using these methods, I estimate the average partial effects, which are shown to be comparable across linear and nonlinear models. Monte Carlo simulations verify that the QML and NLS estimators perform better than other standard estimators. For illustration, these estimators are used in a model of female labor supply with an endogenous number of children. The results show that the marginal reduction in women's working hours per week is less as women have one additional kid. In addition, the effect of the number of children on the fraction of hours that a woman spends working per week is statistically significant and more significant than the estimates in all other linear and nonlinear models considered in the chapter.

The purpose of this study is to estimate the profitability of rice production for irrigated and rain-fed farmers; determine the factors that influence farmers' decision to participate in irrigation and the impact of irrigation on rice farmers' profitability in northern Ghana.

Using cross-sectional data collected from 543 rice farmers in northern Ghana, the study employed both non-parametric (cost benefit analysis) and parametric (endogenous switching regression) approaches to analyse the data.

The empirical results reveal a significant difference between the profits of irrigated (GHS 2442.30) and rain-fed farmers (GHS 576.20), as well as the cost-benefit ratios between irrigators (2.53) and rain-fed farmers (1.37). Also, participation in irrigation was found to be influenced by relatively small farm size and off-farm income; while profitability was influenced by membership in a farmer-based organization, access to agricultural extension services and perception of decreasing rainfall intensity. Irrigation also had a positive significant net impact on profitability of rice production.

The results provide justification for development partners and the government of Ghana through the “one-village-one-dam” policy, to invest in irrigation in northern Ghana in order to improve household welfare as well as build resilience for sustainable production systems.

This study is the first of its kind to provide a robust analysis of the difference in profits of rain-fed and irrigated rice farmers while estimating the determinants of Ghanaian farmers' choice of either of the regimes within a bias-corrected framework.

The paper aims to examine impact of adopted maize seed technology on farm profitability. It assesses both the revenue and cost implication of the adopted technology on farmers' welfare. The study aims to expand the domain of farm investment assessment analysis by including a broader range of production outcome indicators than is normally found in the adoption impact on farm profitability literature.

The paper opted for an empirical study using field survey data. A structured questionnaire was used to interview 400 maize farmers across four regions of Ghana. The survey was complemented with focus group discussions in each region with participants representing male and female farmers cultivating maize on a commercial basis. The data captured detail household level (i.e. demographic and socioeconomic characteristics) as well as farm-level information on maize production (i.e. input use and farm output).

The paper provides empirical insights about maize farmers' perception on the adopted maize seed technology and the factors influencing adoption. It also shed light on the fact that maize farmers do not base their adoption decisions solely on farm output and revenue indicators alone, but mainly on the return on their investment and the cost associated with the maize seed technology adopted.

Because of data limitation, the influence of some important actors (market power) could not be directly captured in the analysis; this may lead to over simplification of a very complex situation in the maize sector. Therefore, researchers are encouraged to future investigate the influence of such important phenomenon on farm profitability.

Improving maize productivity and farm profitability across the sectors is important in order to improve farmer income. This study, therefore, highlights the effect of adopted seed technology and its impact on farm profitability.

This paper fulfils an identified need to study how investment cost in maize seed technology affect farm profitability.

This chapter estimates a dynamic reduced-form model of intra-firm promotions using an employer–employee panel of over 300 of the largest corporations in the United States in the period from 1981 to 1988. The estimation conditions on unobserved individual heterogeneity and allows for both an endogenous initial condition and sample attrition linked to individual heterogeneity in demonstrating the relative importance of variables that influence promotion. The role of the executive’s functional area in promotion is considered along with the existence and source of promotion fast tracks. We find that while the principal determinant of promotions is unobserved individual heterogeneity, functional area has a high explanatory power, resulting in promotion probabilities that differ by functional area for executives at the same reporting level and firm. No evidence is found that an executive’s recent speed of advancement in pay grade has a positive causal impact on in-sample promotions after conditioning on the executive’s career speed of advancement, except for the lowest level executives the data. Fast tracks appear to largely result from heterogeneity in persistent individual characteristics, not from an inherent benefit in recent advancement itself.

Virtual teams had become a fundamental way to conduct business for organizations to remain competitive and sustain in a global environment. Organizations which willing to understand what makes virtual teams more effective can likely accrue positive results from virtual teams. Hence, the purpose of this paper is to investigate what are the factors that impact the performance of virtual team. This study highlights factors that influence virtual team performance, which not been widely studied in developing countries (i.e. Malaysia). This study tests the impact of six factors (coordination, communication, relationship building, cohesion, trust and reward) on team performance.

A quantitative approach via self-administered questionnaire was adopted. A total of 205 questionnaires were successfully collected at the end of the data collection. Structural equation modelling using partial least squares approach (i.e. ADANCO 2.0 software) was then used to assess the relationships among variables in the virtual team.

After ensuring the data have acceptable reliability and validity, structural model assessment was performed to test the hypotheses. The results indicated that a significant and positive relationship exists between communication, relationship building, cohesion and trust towards team performance. On the other hand, no significant relationship was found between coordination and reward towards team performance.

First, the study may be slightly biased due to the differences in the size of the multinational companies. The result might tend to represent the bigger companies where more virtual teams are available for sampling of this study. Second, the duration of virtual team (long-term and short-term) and the number of sites in the team should be clearly identified. The respondents might have very different experiences and perceptions in different settings of virtual team environment. Therefore, taking care of these characteristics would have provided a more detailed and comprehensive understanding of factors influencing virtual team performance in Malaysia.

The findings from this study can serve as guideline for managers to manage the virtual teams effectively, as well as to optimizes the resources usage and implement the most efficient tools of communication, and subsequently improve the overall efficiency of the teams. This study is useful for researchers, managers and organizations to highlight the factors that contribute to virtual team performance.

The present study has not only extended the use of Media Richness Theory in the context Virtual Team Performance in a developing country but also broadened the previous study through inclusion of other crucial factor, namely, Reward. Given the significant role in the Global marketplace, the understanding of factors influencing virtual team performance in Malaysia using the extended theory of Media Richness would contribute knowledge not only to technology management but also to manufacturing service industry.

We consider the Bayes estimation of a multivariate sample selection model with p pairs of selection and outcome variables. Each of the variables may be discrete or continuous with a parametric marginal distribution, and their dependence structure is modeled through a Gaussian copula function. Markov chain Monte Carlo methods are used to simulate from the posterior distribution of interest. The methods are illustrated in a simulation study and an application from transportation economics.

Climate change is expected to cause larger and more frequent precipitation events in key agricultural regions of the United States, damaging crops and soils. Subsurface tile drainage is an important technology for mitigating the risks of a wetter climate in crop production. In this study, the authors examine how quickly farmers adapt to increased precipitation by investing in drainage technology.

Using farm-level data from the 2018 Agricultural Resource Management Survey (ARMS) of soybean producers, the authors construct a drainage adoption timeline based on when the operator began farming their land and when tile drainage was installed, if at all. The authors examine both the initial investment decision and the speed with which drainage is installed by adopters. A Heckman-style Poisson regression is used to model the count nature of adoption speed (measured in years taken to install tile drainage) and to correct for potential sample-selection bias.

The authors find that local precipitation is not a significant determinant of the drainage investment decision but may be highly influential in the timing of adoption among drainage users. Farms exposed to crop-damaging levels of precipitation install tile drainage faster than those with low to moderate levels of rainfall. Estimates of farm adaptation speeds are heterogeneous across farm and operator characteristics, most notably land tenure status.

Understanding how US farmers adapt to extreme weather through technology adoption is key to predicting the long-term impacts of climate change on America's food system. This study extends the existing climate adaptation literature by focusing on the speed of adoption of an important and increasingly common climate-mitigating technology – subsurface tile drainage.