The effect of natural disaster on fertility,birth spacing,and child sex ratio: evidence from a major earthquake in India

Natural disasters can lead to significant changes in health, economic, and demographic outcomes. However, the demographic effects of earthquakes have been studied only to a limited degree. This paper examines the effect of the 2001 Gujarat earthquake on reproductive outcomes. This earthquake killed more than 20,000 people; injured 167,000; and caused massive losses to property and civic assets. Using data from two large-scale District-Level Household Surveys (2002–2004 and 2007–2008), we employ difference-in-difference and fixed-effect regression models to compare the outcomes across earthquake-affected districts and their neighboring districts during 5 years before and after the earthquake. We find that the earthquake led to significant rises in childbirth rates. It also reduced birth spacing among uneducated, tribal, and Muslim women, and the incidence of male births among rural women. We find considerable variation in the demographic effects of the earthquake across location, household socioeconomic status, and parental age and education.     

Causal Flow between Human Well-being and per Capita Real Gross Domestic Product

Whether a country should try to improve human well-being of the masses or should concentrate solely on economic upliftment of the country is an alarming question in development economics. Survey of literature evidences a number of views in this respect. Present study uses the lagged dependent variable model to address the question. It attempts to find causal priority between human well-being and per capita gross real product (an indicator of economic achievement of a country). In this context it uses three core indicators of human well-being life expectancy at birth, infant survival rate and adult as well as three broad income groups: high, middle and low.     

Measuring the Well-Beings of the Developing Countries: Achievement and Improvement Indices

The paradigm of development in the recent days has been shifted from Per Capita Gross National Product (PCGNP) to Human Well-being. It has also been admitted broadly that PCGNP does not automatically transformed into human well-being. It has thus become common practice to use a range of socio-economic variables for measuring the human Well-being (quality of life) in a country. In order to measure the well-being of about 70 developing countries, this study widens the scope of the Physical Quality of Life Index (PQLI) introduced by Morris, by incorporating a number of social aspects of life. On the basis of these indicators, the achievements in the quality of life of these countries are analyzed in this study. An attempt is also made to compare the decadal improvements in their performances in respect of the quality of life over a period of 1960 to 1990. The study also attempts to analyze the relations between achievements and improvements of quality of life and achievements and improvements in terms of PCRGDP of the countries.     

Pairwise Comparisons of Social Development of Selected Countries: 1960–1994: An application of D2-statistics

The paradigm of development has shifted from gross national product to human well-being: from economic growth to social development. It has been broadly accepted that economic growth doesnot automatically translate into a better quality of life. Consequently, the main objective of the most of the countries is to achieve better standard of living for their people. In this context it is of interest to have a look at the pairwise convergence or divergence of countries in social development over a period of time. This study uses D²-statistics to obtain a pairwise distance matrix for the sample countries for each point of time 1960 and 1994. The paper presents a gap matrix portraying the change in the distances between each pair of country over the period. This also finds distance of a country from all other countries. All the analysis has been offered for the entire sample as well as three income groups low, middle and high.     

Non-parametric MANOVA approaches for non-normal multivariate outcomes with missing values

Between-group comparisons often entail many correlated response variables. The multivariate linear model, with its assumption of multivariate normality, is the accepted standard tool for these tests. When this assumption is violated, the non-parametric multivariate Kruskal–Wallis (MKW) test is frequently used. However, this test requires complete cases with no missing values in response variables. Deletion of cases with missing values likely leads to inefficient statistical inference. Here we extend the MKW test to retain information from partially observed cases. Results of simulated studies and analysis of real data show that the proposed method provides adequate coverage and superior power to complete case analyses.     

Improvements in Life Expectancy: 1960–1995 an Exploratory Analysis

Human history observed significant changes in life expectancy over the last three and half decades (1960–1995). This study attempts to explore the determinant factors behind improvements in life expectancy in most of the countries in the Post World War II period. In this context the study estimates a double logarithmic convergence type model where the dependent variable is relative change of life over the period and the explanatory variables are initial per capita real gross domestic product, initial per capita calorie intake as percentage of requirement, adult literacy rate at the initial point of time, and initial physician per thousand of population. The study estimates different variants of the basic model. Female population has been treated separately. All estimates have been offered for three broad income groups low, middle and high. An attempt is also made to take into account the effects of some public health measures such as access to safe drinking water and access to sanitation.     

A monte carlo study of variable selection and inferences in a two stage random coefficient linear regression model with unbalanced data

A growth curve analysis is often applied to estimate patterns of changes in a given characteristic of different individuals. It is also used to find out if the variations in the growth rates among individuals are due to effects of certain covariates. In this paper, a random coefficient linear regression model, as a special case of the growth curve analysis, is generalized to accommodate the situation where the set of influential covariates is not known a priori. Two different approaches for seleaing influential covariates (a weighted stepwise selection procedure and a modified version of Rao and Wu’s selection criterion) for the random slope coefficient of a linear regression model with unbalanced data are proposed. Performances of these methods are evaluated by means of Monte-Carlo simulation. In addition, several methods (Maximum Likelihood, Restricted Maximum Likelihood, Pseudo Maximum Likelihood and Method of Moments) for estimating the parameters of the selected model are compared Proposed variable selection schemes and estimators are appliedtotheactualindustrial problem which motivated this investigation.     

Partial Correlation in Terms of Path Coefficients

The distribution of members of the American Statistical Association (ASA) by several demographic and professional measures is characterized. The analysis is based on a 1979 survey of ASA members who resided in the U.S. More detailed analysis focuses on how demographic and professional characteristics affect the distribution of income and the odds of participating in ASA activities. Several interesting observations are made. Years of experience since highest degree is the most important predictor of income. Highest degree and whether employed in academia are also important. There is a significant difference between the distribution of income for men and women; although the difference is insignificant at one year of experience, it increases with the experience level. Although the survey represented a 100% sample of blacks, Hispanics, and native Americans in the ASA, there is insufficient data to formulate a detailed model of income for this minority group; its average income, however, falls below the expected income of the majority population for higher experience levels. For both the odds of attending annual meetings and serving ASA as an officer or committee member, the most important discriminator is the highest degree of the member. Race is also a significant factor: blacks, Hispanics, and native Americans were least likely to attend annual meetings, and Asians and Pacific Islanders were least likely to hold office. Years of experience is important also for predicting the odds of having held office but not of having attended meetings.     

Meta-analysis of Proportions of Rare Events–A Comparison of Exact Likelihood Methods with Robust Variance Estimation

The conventional random effects model for meta-analysis of proportions approximates within-study variation using a normal distribution. Due to potential approximation bias, particularly for the estimation of rare events such as some adverse drug reactions, the conventional method is considered inferior to the exact methods based on binomial distributions. In this article, we compare two existing exact approaches—beta binomial (B-B) and normal-binomial (N-B)—through an extensive simulation study with focus on the case of rare events that are commonly encountered in medical research. In addition, we implement the empirical (“sandwich”) estimator of variance into the two models to improve the robustness of the statistical inferences. To our knowledge, it is the first such application of sandwich estimator of variance to meta-analysis of proportions. The simulation study shows that the B-B approach tends to have substantially smaller bias and mean squared error than N-B for rare events with occurrences under 5%, while N-B outperforms B-B for relatively common events. Use of the sandwich estimator of variance improves the precision of estimation for both models. We illustrate the two approaches by applying them to two published meta-analysis from the fields of orthopedic surgery and prevention of adverse drug reactions.