Food Poverty,Vulnerability, and Food Consumption Inequality Among Smallholder Households in Ghana: A Gender-Based Perspective

We examined gender-based household welfare differences in Ghana among smallholder households. We measured disparities in welfare outcomes (food poverty, vulnerability, and food consumption inequality) across male and female household heads and identified the set of covariates influencing them. The study utilizes a dataset from a farm household survey undertaken in Northern Ghana from October to December 2018. A multistage sampling approach was adopted in selecting 900 farm households. The Oaxaca–Blinder mean and Recentered Inference Function decomposition techniques highlighted the sources of gender differentials in household welfare outcomes. The findings indicate a significant gap in food consumption expenditure per capita and household dietary diversity scores between male- and female- headed households, and these gaps are as high as 28.2% and 18.1%, respectively. However, there are no statistically significant differences in vulnerability to food poverty between male- and female-headed households. The Lorenz curves confirm inequality in gendered households’ food consumption expenditure and dietary diversity scores. This study highlights the existence of systemic female-headed household vulnerability to food poverty in Ghana. This study provides significant evidence of the need for policymakers to address food systems’ structural deficiencies and inequalities with gender in mind.

     

The Quality of Life of Hong Kong’s Poor Households in the 1990s: Levels of Expenditure,Income Security and Poverty

This paper examines the changes in the quality of life of poor households in Hong Kong in the late 1990s by analyzing their levels of expenditure, income security and poverty before and after 1997. Though there have been significant increases in the levels of expenditure among CSSA recipients, the expenditure among these poorest households in Hong Kong is still below that of non-CSSA recipients. Increasing poverty in Hong Kong is the result of increasing housing costs borne by these low expenditure households, who have to squeeze their expenditure on food and other items in order to meet the rising cost of housing. De-industrialisation and mass unemployment have given capital and the state unchecked authority to restructure the economy and to deregulate the labour market. Many low-income households have been hit hard as they faced redundancy, unemployment and wage-cuts after the 1997 Asian financial crisis. What jobs have been created are mostly part-time, temporary and contract jobs, and there has thus been an erosion in both job and income security. The quality of life of poor households is devastating not only in the sense that their living standards are low, but also that they are socially excluded from the mainstream of society.     

A Decomposition of Copulas and Its Use

ABSTRACT

In this article, we create a decomposition that represents and describes the depen-dence structure between two variables. Since copulas provide a deep understanding of the dependence structure by eliminating the effects of the marginals, they play a key role in this study. We define a discretized copula density matrix and decompose it into a set of permutation matrices by using the Birkhoff–von Neumann theorem. This decomposition provides a way to effectively apply the concepts of copulas to solve problems in multivariate statistical data analysis.     

Preliminary Goodness-of-Fit Tests for Normality do not Validate the One-Sample Student t

One of the most basic topics in many introductory statistical methods texts is inference for a population mean, μ. The primary tool for confidence intervals and tests is the Student t sampling distribution. Although the derivation requires independent identically distributed normal random variables with constant variance, σ², most authors reassure the readers about some robustness to the normality and constant variance assumptions. Some point out that if one is concerned about assumptions, one may statistically test these prior to reliance on the Student t. Most software packages provide optional test results for both (a) the Gaussian assumption and (b) homogeneity of variance. Many textbooks advise only informal graphical assessments, such as certain scatterplots for independence, others for constant variance, and normal quantile–quantile plots for the adequacy of the Gaussian model. We concur with this recommendation. As convincing evidence against formal tests of (a), such as the Shapiro–Wilk, we offer a simulation study of the tails of the resulting conditional sampling distributions of the Studentized mean. We analyze the results of systematically screening all samples from normal, uniform, exponential, and Cauchy populations. This pretest does not correct the erroneous significance levels and makes matters worse for the exponential. In practice, we conclude that graphical diagnostics are better than a formal pretest. Furthermore, rank or permutation methods are recommended for exact validity in the symmetric case.     

A Unified Method for Checking Compatibility and Uniqueness for Finite Discrete Conditional Distributions

Checking compatibility for two given conditional distributions and identifying the corresponding unique compatible marginal distributions are important problems in mathematical statistics, especially in Bayesian inferences. In this article, we develop a unified method to check the compatibility and uniqueness for two finite discrete conditional distributions. By formulating the compatibility problem into a system of linear equations subject to constraints, it can be reduced to a quadratic optimization problem with box constraints. We also extend the proposed method from two-dimensional cases to higher-dimensional cases. Finally, we show that our method can be easily applied to checking compatibility and uniqueness for a regression function and a conditional distribution. Several numerical examples are used to illustrate the proposed method. Some comparisons with existing methods are also presented.     

Bayesian Inference for the Precision Matrix for Scale Mixtures of Normal Distributions

Baysian inference is considered for the precision matrix of the multivariate regression model with distribution of the random responses belonging to the multivariate scale mixtures of normal distributions. The posterior distribution and some identities involving expectations taken with respect to this posterior distribution are derived when the prior distribution of the parameters is from the conjugate family. The results are specialized to the case where the random responses have a matrix-t distribution and thus generalizing the results of Zellner (1976 Zellner , A. ( 1976 ). Bayesian and non-Bayesian analysis of the regression model with multivariate Student-t error terms . J. Amer. Statist. Assoc. 71 : 400 – 405 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Muirhead (1986 Muirhead , R. J. ( 1986 ). A note on some Wishart expectations . Metrika 33 : 247 – 251 .[Crossref] , [Google Scholar]).     

Bayesian analysis of linear models exhibiting changes in mean and precisionat an unknown time point

This paper analyses a linear model in which both the mean and the precision change exactly once at an unknown point in time. Posterior distributions are found for the unknown time point at which the changes occurred and for the ratio of the precisions. The Bayesian predictive distribution of k future observations is also derived. It is shown that the unconditional posterior distribution of the ratio of precisions is a mixture of F-type distributions and the predictive distribution is a mixture of multivariate t distributions.     

A relation between product-limit estimate and joint-risk estimate

The joint-risk estimate of the survival function, used for censored survival data grouped into fixed intervals, is shown to be the geometric mean of all the product-limit estimates that correspond to all the possible orderings of all the failure times and censoring times in the group. The joint-risk estimate is proposed as a more appropriate and better means of dealing with ties for data containing tied failure times and censoring times. It is also applicable to competing risk problems with tied failure times involving different causes. It could be used as a substitute for the product-limit estimate in discrete failure time analysis.     

Optimizing Latin hypercube designs by particle swarm

Latin hypercube designs (LHDs) are widely used in many applications. As the number of design points or factors becomes large, the total number of LHDs grows exponentially. The large number of feasible designs makes the search for optimal LHDs a difficult discrete optimization problem. To tackle this problem, we propose a new population-based algorithm named LaPSO that is adapted from the standard particle swarm optimization (PSO) and customized for LHD. Moreover, we accelerate LaPSO via a graphic processing unit (GPU). According to extensive comparisons, the proposed LaPSO is more stable than existing approaches and is capable of improving known results.