Does crop diversification reduce downside risk of external maize yield-enhancing technology? Evidence from Ethiopia

Unexpectedly lower yield outcomes (downside risks) challenge farmers’ use of external inputs that can enhance crop productivity. Using household-level panel data collected from Ethiopia, we estimated the effects of crop diversification through maize-legume intercropping/rotation on maize yield distribution and downside risk. Results from endogenous switching regression models and quintile moment approaches show that plots with maize-legume intercropping/rotation have the highest average maize yield. Such crop diversification reduces the downside risk in maize yield more when applied to plots receiving external inputs. The results imply that, in addition to the technical support around external input use in smallholder maize production, Ethiopia’s agricultural extension may also need to give due emphasis to both spatial and temporal crop diversification practices. This could enhance crop productivity further and reduce the potential downside risks typically hampering smallholders’ external input use in maize production.

Percolation models of pathogen spillover

A series of logical events must occur for a pathogen to spill over from animals to people. The pathogen must be present in an animal reservoir, it must be shed from the reservoir into the environment or be transferred from the reservoir to a vector, it must persist in the environment or vector until contact with a human or amplifier host, and it must successfully enter, colonize, and reproduce within the human. These events each represent a barrier the pathogen must cross to successfully infect a human. Percolation models of pathogens completing the series of barriers or logical events can connect models of spillover risk with standard tools for statistical inference. Here, we develop percolation-based models of spillover risk and a theoretical framework for managing spillover as an inextricably multilevel process. Through analysis and simulation, we show that estimated associations between level-specific covariates and spillover events will err towards associations from dominant pathway to spillover, a potential problem if there are alternative pathways to spillover with different associations with covariates. Furthermore, estimated associations between covariates and spillover will better reflect associations between covariates and success probabilities of bottleneck events with the highest pathogen attrition rates in the data observed. If one agrees with a percolation model for spillover, then GLMs should not be used to estimate relative importance of various levels. We recommend always using nonlinear models for predicting spillover risk with quantitative covariates and discuss why switching regression models may be well suited for avoiding some obvious pitfalls in predicting spillover from alternative pathways or wildlife reservoirs. Finally, we demonstrate how percolation models formalize an intuitive management paradigm for mitigating risk in the inherently multilevel process of pathogen spillover.

The same deep water as you? The impact of alternative governance arrangements of water service delivery on efficiency

This work contributes to the literature on water governance by attempting to provide an answer to the question of what are the differences in efficiency of alternative governance arrangements of water utilities. We test hypotheses derived from property rights, principal–agent, and transaction costs theories using a comprehensive database of 260 water utility systems provided by the Portuguese Regulatory Authority of Water and Waste Services. Using endogenous switching regression models estimated through maximum likelihood, the study is designed in two steps. First, we investigate differences in efficiency between in-house options and externalization and find that in-house solutions as a set (direct provision and municipal companies) are more efficient than externalization options (mixed companies and concessions). Second, we test differences in efficiency within both in-house and externalization solutions, and fail to find statistically significant differences in efficiency between in-house bureaucracies and municipal companies and between mixed companies and concessions.

Buying out of familial obligation: The tradeoff between financially supporting versus living with elderly parents in Urban China

For Chinese families, coresidence with elderly parents is both a form of support and a moderator of financial support. Previous literature on intergenerational support in Chinese societies has studied either coresidence or financial support independently, but not these two forms of support jointly. Using data from the 1999 ‘Study of Family Life in Urban China' in Shanghai, Wuhan, and Xi’an, we examined whether or not adult children, especially sons, buy out of the obligation to live with their parents by providing greater financial support. To account for the potential selection bias associated with coresidence, we treated coresidence and financial transfer as joint outcomes by using endogenous switching regression models. The results showed that children who coreside with their parents would have provided more financial support had they lived away and children who live away from their parents would have provided more financial support had they coresided. These findings suggest a self-selection mechanism that maximizes children’s interests rather than parents’ interests.

Semi-Supervised Sequential Kernel Regression Models with Penalty Functions

Switching regression models can output multiple clusters and regression models. However, there is one problem: the results have a strong dependency on the predefined number of clusters. To avoid these drawbacks, we have researched sequential extractions. In sequential extractions process, one cluster is extracted at a time using a method of noise-detection, and the number of clusters are determined automatically. We propose semi-supervised sequential kernel regression models with penalty functions. Additionally, we also find that the sensitivity against the regularization parameter λ can be alleviated by semi-supervisions using penalty functions. We show the effectiveness of the proposed method by using numerical examples.

Sequential Regression Models with Pairwise Constraints Using Noise Clusters

Switching regression models are useful in a variety of real applications. Semi-supervised clustering with pairwise constraints is also well-known to be important and many researchers recently study this subject. In spite of their usefulness, there is one drawback: the results have a strong dependency on the predefined number of clusters. To avoid this drawback, we use a method of sequentially extracting one cluster at a time using noise-detecting method, and propose constrained switching regressionmodels which enables an automatic determination of clusters. We show the effectiveness of the proposed method by using numerical examples.