Multidimensional Human Opportunity Index

One fact that emerges from the evaluation of the Millennium Development Goals is that not all countries met all the goals and there are significant complementaries among failing on specific goals. This paper proposes the Multidimensional Human Opportunity Index (MHOI) that focuses on the complementaries among access to multiple services. We focus on access to services for children, with the aim of capturing equality in opportunity for children from diverse socio-economic backgrounds. This index builds on the Human Opportunity Index of the World Bank that measures children’s access to a basic service, such as access to clean water. However, the MHOI differs from the parent index in that we measure joint access to multiple services or access to a bundle of services. We apply the MHOI on two Himalayan states of South Asia, Nepal and Bhutan, and show that although each basic service is available to a large proportion of the population, only two-thirds in Bhutan and one half in Nepal have access to the bundle of basic services in 2011–2012.     

Estimation and hypothesis testing in multivariate linear regression models under non normality

This paper discusses the problem of statistical inference in multivariate linear regression models when the errors involved are non normally distributed. We consider multivariate t-distribution, a fat-tailed distribution, for the errors as alternative to normal distribution. Such non normality is commonly observed in working with many data sets, e.g., financial data that are usually having excess kurtosis. This distribution has a number of applications in many other areas of research as well. We use modified maximum likelihood estimation method that provides the estimator, called modified maximum likelihood estimator (MMLE), in closed form. These estimators are shown to be unbiased, efficient, and robust as compared to the widely used least square estimators (LSEs). Also, the tests based upon MMLEs are found to be more powerful than the similar tests based upon LSEs.     

Household empowerment as the key to eradicate poverty incidence

The current study aimed to investigate how the household empowerment can potentially reduce the rural poverty incidence. The data were collected through multistage random sampling from the rural households of 24 villages in Southern Punjab, Pakistan. A total of 600 households were selected as sample pool. Face‐to‐face interviews with the household heads were conducted. The household empowerment was measured through an index that was adopted from Abrar‐ul‐haq (“An assessment of the role of household empowerment in alleviating poverty in Southern Punjab, Pakistan” (Ph.D. Dissertation), School of Economics, Finance and Banking, Universiti Utara Malaysia, 2017). The binary logit model was used to analyze the impact of household empowerment with other control variables on poverty incidence. The results of the current study confirm the negative nexus between household empowerment and poverty incidence of rural households as the study reported that the probabilities (odds ratio: 0.877) of being poor are reduced by increasing the empowerment (in every aspect, economically, politically, and socially) of the households, and the results are statistically significant at the 1% level of significance. Based on empirical results, this study suggests that household empowerment has significant potential to reduce poverty incidence in the rural areas of Southern Punjab, Pakistan.     

A guide to the design and analysis of small clinical studies

Clinical studies, which have a small number of patients, are conducted by pharmaceutical companies and research institutions. Examples of constraints that lead to a small clinical study include a single investigative site with a highly specialized expertise or equipment, rare diseases, and limited time and budget. We consider the following topics, which we believe will be helpful for the investigator and statistician working together on the design and analysis of small clinical studies: definitions of various types of small studies (exploratory, pilot, proof of concept); bias and ways to mitigate the bias; commonly used study designs for randomized and nonrandomized studies, and some less commonly used designs; potential ethical issues associated with small underpowered clinical studies; sample size for small studies; statistical analysis methods for different types of variables and multiplicity issues. We conclude the paper with recommendations made by an Institute of Medicine committee, which was asked to assess the current methodologies and appropriate situations for conducting small clinical studies.     

Demographic transition and the emerging windows of opportunities and challenges in Bangladesh

The aim of this paper is to examine the demographic transition and the emerging window of opportunities and challenges in Bangladesh. The study utilizes time series data from national level population surveys, censuses and the population projections and estimates produced by the United Nations Population Division. The ongoing demographic transition in Bangladesh leads to many changes in the size and age structure of the rapidly growing population of the country, giving rise to economic and social opportunities as well as policy challenges. The window of opportunity that has emerged since the 1990s will not last long and will not be repeated in the near future. It will reach its peak during the 2020s and will remain open until the 2030s. This demographic dividend needs to be managed efficiently in order to be transformed into better and sustainable economic growth. Understanding demographic challenges must therefore be a priority for the Government of Bangladesh, which must formulate policies to harvest the benefits of the demographic opportunities.     

Social behavior practices for child protection and well-being among low-income urban households in Bangladesh

The main objective of this study was to explore the status of social behavior practices for child protection and well-being among low-income urban households in Bangladesh. This study used a survey method, and data were collected through a face-to-face structured interview from 378 low-income household heads from 12 wards (Konabari and Kashimpur) of Zone 5 of the Gazipur City Corporation. Results found that the current status of the presence of child protection services is very low (in many cases, this number is zero except children having birth registered). A significant number (55%) of them were married between 15 and 19 years. The study found a low level of access on the components of social behaviors where around 50% to 60% caregivers stated 4 benefits of key behaviors, necessity of hand washing after defecation and before and after taking a meal, the age of boys and girls to get married off, and know the effect and consequences of child marriage. The chi-squared tests confirmed extremely high significance in almost all of the components. Findings will be an important guideline for the policymakers, child service practitioners, and human rights workers.     

Soil health as a predictor of lettuce productivity and quality: A case study of urban vacant lots

Urban agriculture offers a framework for local self-reliance and resilience in cities. However, there is a concern over the capacity of urban soil to provide sustainable and safe food production. We tested the effectiveness of several soil health indicators to predict food crop productivity and quality in vacant lots in a disadvantaged neighborhood in the city of Cleveland, Ohio. We defined soil health as a state of composite well being in terms of biological, chemical, and physical properties of the soil as they relate to crop productivity. Twelve city-owned vacant lots, three close to each of the four city schools, were selected for soil properties and plant growth analyses. Soil samples were analyzed for pH, moisture content (θv), soil texture, soil organic matter (SOM), active carbon (AC), ammonium (NH₄-N), nitrate (NO₃-N), microbial biomass N (MBN), and nematode community parameters including total (TNN), bacteria-feeding (BFN), fungal-feeding (FFN), and plant-parasitic (PPN) nematodes, number of nematode genera (NNG), and nematode food web enrichment index (EI) and structure index (SI). Lettuce was planted in the selected vacant lots and its growth was documented through measures of dry biomass, numbers of leaves/plant, and complementary subjective appearance scores related to physiological status. All measured parameters varied considerably among vacant lots except soil pH. Principal components analysis revealed that among the primary soil physical, chemical, and biological parameters, soil clay, NO₃-N, MBN, SOM, AC, TNN, BFN, FFN, and PPN contributed most to the variance of the entire dataset. There were also several positive correlations among these key soil health predictor variables: AC was positively correlated with clay, SOM, MBN, TNN, BFN, FFN and PPN, and TNN was positively correlated with AC, SOM, MBN, BFN, FFN and PPN. Of the identified primary soil health indicators, only clay, SOM, and MBN positively correlated with lettuce dry biomass, which was also positively correlated with a secondary soil health indicator, the nematode food web EI. Lettuce leaf necrosis was negatively correlated with clay, AC, SOM, MBN, TNN, FFN, and PPN, and the proportion of withered leaves was negatively correlated only with SOM. It is concluded that AC, PPN, TNN, SOM, MBN, clay, and nematode food web EI can serve as important soil health indicators that have potential for predicting crop productivity and quality in urban soils. It is also concluded that lettuce can serve as an important indicator of soil health with respect to crop productivity and quality in vacant lots.     

Joint estimation of the mean and dispersion parameters in the analysis of proportions: A comparison of efficiency and bias

This paper deals with joint estimation of the mean and dispersion parameters in the analysis of proportions. We consider a parametric model, namely the extended beta-binomial model, and several semiparametric procedures. We study large-sample efficiency and small-sample bias and efficiency properties of the estimates of the mean and intraclass correlation parameters. Estimation and efficiency calculations are présentés for the regression model. However, for simplicity, numerical large-sample efficiency and small-sample bias and efficiency calculations are performed for the two-parameter model only. Numerical efficiency results are présentés in terms of graphs. Estimated asymptotic efficiencies of various estimates are also compared for two data sets. Our findings suggest that for the estimation of the mean (regression) parameters the quasilikelihood procedure performs best. However, for the joint estimation, the Gaussian likelihood estimates perform best.     

Microcredit for rural poverty alleviation and social well‐being: A study of Sabah,Malaysia

Microcredit is an influential intervention used to alleviate poverty and improve social well‐being in rural communities in Sabah, Malaysia. This study examined the effects of a microcredit scheme, Amanah Ikhtiar Malaysia (AIM), on the well‐being of these communities. Using a survey method, a pre‐tested interview schedule was administered to 277 AIM recipients in Sabah, Malaysia. Social well‐being was examined based on the three indicators of Midgley's social development approach: the management of problems, the fulfillment of basic needs, and social opportunities provided. AIM microcredit schemes were successful in improving the well‐being of AIM recipients, although the recipients faced a number of problems, including unemployment, lack of education, and access to public services and healthcare facilities. The findings of this study represent a useful guideline to the AIM authority, development practitioners, policymakers, and governmental organization and non‐governmental organization workers.     

Classification rules for identifying individuals at high risk of developing myocardial infarction based on ApoB,ApoA1 and the ratio were determined using a Bayesian approach

We have developed a new approach to determine the threshold of a biomarker that maximizes the classification accuracy of a disease. We consider a Bayesian estimation procedure for this purpose and illustrate the method using a real data set. In particular, we determine the threshold for Apolipoprotein B (ApoB), Apolipoprotein A1 (ApoA1) and the ratio for the classification of myocardial infarction (MI). We first conduct a literature review and construct prior distributions. We then develop classification rules based on the posterior distribution of the location and scale parameters for these biomarkers. We identify the threshold for ApoB and ApoA1, and the ratio as 0.908 (gram/liter), 1.138 (gram/liter) and 0.808, respectively. We also observe that the threshold for disease classification varies substantially across different age and ethnic groups. Next, we identify the most informative predictor for MI among the three biomarkers. Based on this analysis, ApoA1 appeared to be a stronger predictor than ApoB for MI classification. Given that we have used this data set for illustration only, the results will require further investigation for use in clinical applications. However, the approach developed in this article can be used to determine the threshold of any continuous biomarker for a binary disease classification.