How Does Spatial Injustice Affect Residents' Policy Acceptance of the Economic-Social-Ecological Objectives of Construction Land Reduction?

Construction land reduction (CLR) is a policy innovation for Shanghai to explore high-quality economic development, but it will also lead to spatial injustice in the implementation process. Although the literature on spatial injustice and CLR is increasing, very little is known about the influence of spatial injustice in CLR on residents' policy acceptance of the economic-social-ecological objectives of CLR. To fill the knowledge gap, this study uses micro-survey data to identify the factors that influence residents' policy acceptance of the economic-social-ecological objectives of CLR. Results show that: (1) Spatial injustice in CLR significantly reduces residents' policy acceptance of the social and ecological objectives of CLR. (2) The locational disadvantage of villages significantly reduces residents' policy acceptance of the ecological objectives of CLR. (3) The more educated the residents are, the more they recognize the social and ecological objectives of CLR. (4) The higher the percentage of household workers, the more residents endorse the economic and social objectives of CLR. (5) Compared with ordinary residents, cadres are more accepting of the economic objectives of CLR. (6) Robustness tests support the findings of this study. The findings of this study provide insights for sustainable CLR policy reform.

Chronic Diseases and Labor Force Participation Among Presenile and Senile Chinese

Background: The incidence of chronic diseases has increased dramatically due to rapid aging and lifestyle changes of China in recent decades. The population aged more than 45 years is an important participant in the labor force market, and the health status directly affects their labor force participation decision. This study aims to explore the relationship between chronic diseases and the labor force participation among the elderly Chinese population aged more than 45 years.

Method: We employ a multivariate probit (MVP) model to construct five structural equations for an analysis. The advantage of this model is that it can deal with the endogeneity of chronic diseases.

Results: Firstly, compared with the elderly, younger people are more likely to participate in the labor force market; the influence of chronic diseases is the largest for presenile women in the decision-making of labor force participation; the impact of psychological problems on labor force participation cannot be ignored, especially for men aged more than 45 years. In addition, sociodemographic factors such as geographical location and marital status also have direct effects on the probability of labor force participation while the impact of both family wealth and family number is much smaller. Finally, unhealthy lifestyles through chronic diseases have negative and indirect marginal effects on labor force participation.

Conclusions: This article proves that chronic diseases have a negative impact on the labor force participation for Chinese aged more than 45 years. The public should give more tolerance and opportunities to these groups. The population aged more than 45 years are more vulnerable and face more psychological problems, which will lead to a decline in labor force participation. Psychological health counseling and services are urgently needed. As the urban areas enjoy more social welfare, Chinese welfare policy needs to be tilted toward the rural elderly. For individuals, maintaining healthy lifestyles can help you stay away from chronic diseases and stay in the labor force market.

The Impact of Village Rules and Formal Environmental Regulations on Farmers' Cleaner Production Behavior: New Evidence from China

Village rules and formal environmental regulations are of great significance for standardizing farmers’ cleaner production behavior, promoting green transformation of agriculture and realizing sustainable development of agriculture. Based on the survey data of 946 farmers in five provinces of China, taking seed coating technology, soil testing and formulated fertilization technology, subsoiling tillage technology, green technology for pest and disease control and straw returning technology as examples, this article empirically analyzes the impact of village rules and formal environmental regulations on farmers’ cleaner production behavior by using the multivariate probit model. When formal environmental regulations are relatively lacking or weak, village rules can be used as a useful supplement to formal environmental regulations to promote farmers’ participation in cleaner production. Based on this, this article argues that the important reason for formal environmental regulations falling into relative system failure is that village rules have not been paid enough attention in promoting farmers’ cleaner production behavior. In the future, we should not only continue to strengthen the role of formal environmental regulations in farmers’ cleaner production, but also cultivate the informal institution represented by the village rules, and build the regulatory system of mutual support between informal institution and formal institution.

Mitigating Catastrophic Risks and Food Security Threats: Effects of Land Ownership in Southern Punjab, Pakistan

In the wake of environmental challenges, the adoption of risk management strategies is imperative to achieve sustainable agricultural production and food security among the Pakistani farmers of Punjab. For a deeper insight into farmers’ adaptive behavior towards climate change, this study explored the role of land tenancy in the adoption of risk management instruments, such as off-farm diversification, improved varieties, and crop insurance. Off-farm diversification was found to be a preferred instrument among landless tenants. The study also employed a multivariate probit model that further signified the role of land tenure in risk-related decisions. Apart from land tenancy, the results identified the prominence of risk perception, information access, and extension access in adoption decisions. This study also investigated the association between risk management approaches and food security indicators (household hunger scale, food consumption score). Analysis revealed a significant association between risk management tools and food security indicators.

Farm household access to agricultural services in northern Ghana

Access to agricultural services promotes agricultural production and livelihoods of smallholders in most developing countries. This study explores the factors affecting smallholders' access to agricultural services in northern Ghana. The study relied on survey data for the analysis. We estimated a multivariate probit model to assess the factors influencing access to agricultural services and a generalized Poisson regression model to evaluate the factors affecting the degree of access to these services. The results indicated that farmers had varying degree of access to different agricultural services. The most accessed service was agricultural extension (63% access) while the least was farm credit (40% access). Access to services and the degree of access (number of services accessed) were both influenced by household size, farming experience, household income, farmer group membership, and geographical location. In addition, the number of services accessed by farmers was influenced by participation in off-farm work. The paper proposed measures to enhance smallholders' access to agricultural services to promote agricultural production, food security and rural livelihoods.

Parameter-expanded data augmentation for analyzing correlated binary data using multivariate probit models

Data augmentation has been commonly utilized to analyze correlated binary data using multivariate probit models in Bayesian analysis. However, the identification issue in the multivariate probit models necessitates a rigorous Metropolis-Hastings algorithm for sampling a correlation matrix, which may cause slow convergence and inefficiency of Markov chains. It is well-known that the parameter-expanded data augmentation, by introducing a working/artificial parameter or parameter vector, makes an identifiable model be non-identifiable and improves the mixing and convergence of data augmentation components. Therefore, we motivate to develop efficient parameter-expanded data augmentations to analyze correlated binary data using multivariate probit models. We investigate both the identifiable and non-identifiable multivariate probit models and develop the corresponding parameter-expanded data augmentation algorithms. We point out that the approaches, based on one non-identifiable model, circumvent a Metropolis-Hastings algorithm for sampling a correlation matrix and improve the convergence and mixing of correlation parameters; the identifiable model may produce the estimated regression parameters with smaller standard errors than the non-identifiable model does. We illustrate our proposed approaches using simulation studies and through the application to a longitudinal dataset from the Six Cities study.

Reference-based pattern-mixture models for analysis of longitudinal binary data

Pattern-mixture model (PMM)-based controlled imputations have become a popular tool to assess the sensitivity of primary analysis inference to different post-dropout assumptions or to estimate treatment effectiveness. The methodology is well established for continuous responses but less well established for binary responses. In this study, we formulate the copy-reference and jump-to-reference PMMs for longitudinal binary data using a multivariate probit model with latent variables. We discuss the maximum likelihood, Bayesian, and multiple imputation methods for estimating the treatment effect under the specified PMM. Simulation studies are conducted to evaluate the performance of these methods. These methods are also illustrated using data from a bipolar mania study.

'Get a Fish' vs. 'Get a Fishing Skill': Farmers' Preferred Compensation Methods to Control Agricultural Nonpoint Source Pollution

Ecological compensation is an important means for controlling agricultural nonpoint source pollution, and compensation methods comprise an essential part of the compensation policy for mitigating this form of pollution. Farmers' choice of compensation methods affects their response to compensation policies as well as the effects of pollution control and ecological compensation efficiency. This study divides ecological compensation methods into two distinct philosophies-the "get a fish" method (GFM) and "get a fishing skill" method (GFSM)-based on policy objectives, to determine farmers' choice between the two methods and the factors influencing this choice. Furthermore, by analyzing survey data of 632 farmers in the Ankang and Hanzhong cities in China and using the multivariate probit model, the study determines farmers' preferred option among four specific compensation modes of GFM and GFSM. The three main results are as follows. (1) The probability of farmers choosing GFM is 82%, while that of choosing GFSM is 51%. Therefore, GFM should receive more attention in compensation policies relating to agricultural nonpoint source pollution control. (2) Of the four compensation modes, the study finds a substitution effect between farmers' choice of capital and technology compensations, capital and project compensations, material and project compensations, while there is a complementary relationship between the choice of material and technology compensations. Therefore, when constructing the compensation policy basket, attention should be given to achieving an organic combination of different compensation methods. (3) Highly educated, young, and male farmers with lower part-time employment, large cultivated land, and a high level of eco-friendly technology adoption and policy understanding are more likely to choose GFSM. Hence, the government should prioritize promoting GFSM for farmers with these characteristics, thereby creating a demonstration effect to encourage transition from GFM to GFSM.

How Do Anticipated and Self Regulations and Information Sourcing Openness Drive Firms to Implement Eco-Innovation? Evidence from Korean Manufacturing Firms

Building upon institutional theory and the concept of openness to external sources in terms of breadth and depth, this study investigates the following three understudied drivers of eco-innovation in terms of external and internal factors: Anticipated regulation and self-regulation as external drivers, and information sourcing openness comprised of breadth and importance as internal drivers. Toward this end, this study employs a sample of 1824 Korean manufacturing firms collected from the Korean Innovation Survey 2010, which is compatible with the Oslo Manual and the Eurostat Community Innovation Survey (CIS). The current research adopts a multivariate probit model for the nine binary outcome variables and a zero-inflated negative binomial (ZINB) regression model for a count variable. It is found that, both anticipated regulation and self-regulation positively affect eco-process innovation and eco-product innovation across all of the nine eco-innovation types. The empirical findings on the effects of the breadth of external sources and the importance of used information acquired from external sources for innovative activities indicate that both the breadth and the importance have positive impacts on the number of types of eco-innovation with which a firm is engaged.

Secondary phenotype analysis in ascertained family designs: application to the Leiden longevity study

The case-control design is often used to test associations between the case-control status and genetic variants. In addition to this primary phenotype, a number of additional traits, known as secondary phenotypes, are routinely recorded, and typically, associations between genetic factors and these secondary traits are studied too. Analysing secondary phenotypes in case-control studies may lead to biased genetic effect estimates, especially when the marker tested is associated with the primary phenotype and when the primary and secondary phenotypes tested are correlated. Several methods have been proposed in the literature to overcome the problem, but they are limited to case-control studies and not directly applicable to more complex designs, such as the multiple-cases family studies. A proper secondary phenotype analysis, in this case, is complicated by the within families correlations on top of the biased sampling design. We propose a novel approach to accommodate the ascertainment process while explicitly modelling the familial relationships. Our approach pairs existing methods for mixed-effects models with the retrospective likelihood framework and uses a multivariate probit model to capture the association between the mixed type primary and secondary phenotypes. To examine the efficiency and bias of the estimates, we performed simulations under several scenarios for the association between the primary phenotype, secondary phenotype and genetic markers. We will illustrate the method by analysing the association between triglyceride levels and glucose (secondary phenotypes) and genetic markers from the Leiden Longevity Study, a multiple-cases family study that investigates longevity. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd.