This paper first derives an adaptive estimator when heteroskedasticity is present in the individual specific error in an error component model and then compares the finite sample performance of the proposed estimator with various other estimators. While the Monte Carlo results show that the proposed estimator performs adequately in terms of relative efficiency, its performance on the basis of empirical size is quite similar to the other estimators considered.Heteroskedasticity, Kernel estimation, Error component model, JEL Classification , C14, C23,
Planning strategies that maximize the Human Development Index (HDI) tend towards minimizing consumption and maximizing non-investment expenditures on education and health. Interestingly, such strategies also tend towards equitable outcomes, even though inequality aversion is not modelled in the HDI. A problematic feature of strategies that maximize the HDI is that the income component in the index only role is to distort the allocation between health and education expenditure. Because the income component does not play its intended role of securing resources for a decent standard of living, we argue that it is better to drop income from the index in considering optimal plans. Alternatively, we consider net income, income net of education and health expenditures, as indicator of capabilities not already reflected in the education and life expectancy components of the index. When net income is used in a modified HDI index, optimal plans yield a balance between allocations for consumption, education, and health. Finally, we calculate our modified indexes for OECD countries and compare them with the HDI.
The world has experienced impressive improvements in wealth and health, with, for instance, the world's real GDP per capita having increased by 180% from 1970 to 2007 accompanied by a 50% decline in infant mortality rate. Healthier and wealthier. Are health gains arising from wealth growth? Or, has a healthier population enabled substantial growth in wealth? The answers to these questions have serious policy implications. We contribute to understanding dynamic links between wealth and health by analyzing the relationship between health (as measured by infant mortality rate) and wealth (as measured by GDP per capita) for a panel of 58 developing countries using quinquennial data covering the period 1960 through 2005. We examine for causal rather than associative links between these fundamental macro measures of economic development. The panel enables us to examine for causal links using several methods that differ in how cross-country and temporal heterogeneity is imposed: cross-country homogeneity with temporal heterogeneity and cross-country heterogeneity with temporal homogeneity. Under the latter case, we consider sensitivity to assuming fixed versus random causal coefficients. In addition, we explore robustness of outcomes to level of economic development (as measured by national income) and inclusion of another covariate (education). . Key from these microeconometric applications is that health and wealth clearly affect each other, with some demonstrating how improved health can raise education level, adult labour quantity, and labour productivity, so influencing an individual's wealth (income) via auxiliary factors.However, usually due to the nature of the datasets, most studies explore for associative, rather than causal, relationships between health and wealth; Adams et al. (2003) and Michaud and van Soest (2008) are exceptions, examining for direct causal links between health and wealth, 3 analogous to the notion of Granger (1969) causality in macroeconomics -we denote this as G-causality and G-noncausality.A large literature also uses cross-country data to study the aggregate health-wealth relationship; e.g.
In this paper, a semiparametric model is used to examine the relationship between pollution and income for three non-point source pollutants. Statistical tests reject the quadratic specification in favor of the semiparametric model in all cases. However, the results do not support the inverted-U hypothesis for the pollution-income relationship.
That mortgage lenders have complex underwriting standards, often differing legitimately from one lender to another, implies that any statistical model estimated to approximate these standards, for use in fair lending determinations, must be misspecified. Exploration of the sensitivity of disparate treatment findings from such statistical models is, thus, imperative. We contribute to this goal. This article examines whether the conclusions from several bank-specific studies, undertaken by the Office of the Comptroller of the Currency, are robust to changes in the link function adopted to model the probability of loan approval and to the approach used to approximate the finite sample null distribution for the disparate treatment hypothesis test. Our evidence, of discrimination findings that are reasonably robust to the range of examined link functions, suggests that regulators and researchers can be reasonably comfortable with their current use of the logit link. Based on several features of our results, we advocate regular use of a resampling method to determine p-values.
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