A Romerian contribution to the empirics of economic growth

Mankiw, Romer, and Weil (1992) made the Solovian set up widely used to test the determinants of economic growth and the speed of convergence. In accordance with the nature of the Solow framework, almost all empirical growth studies considered technological progress constant and identical across countries and over time, and hence underemphasized its role. In this study, in order to overcome this weakness, we propose that the Mankiw, Romer, and Weil (1992) set-up should be replaced by the Solovianized Romer (1990) framework, thus allowing the role of technology to be considered in the empirical analysis. In particular, within this framework, the growth rate of technology varies across economies and over time. We estimate the convergence equation derived from Solovianized Romer model for 31 OECD countries for the period 1980–2008 by applying the system GMM approach. The empirical findings of the model support the conditional convergence hypothesis, but reveal a lower convergence rate than that predicted by the existing literature. As a policy implication, we argue that, investment in R&D and human capital are important determinants of convergence, and in cases where economies are unable to allocate sufficient resources to R&D, policy makers should ease the diffusion of technology (e.g., via FDI or trade) in order to retain a high convergence rate.     

Bargaining without a Common Prior—An Immediate Agreement Theorem

In sequential bargaining models without outside options, each player's bargaining power is ultimately determined by which player will make an offer and when. This paper analyzes a sequential bargaining model in which players may hold different beliefs about which player will make an offer and when. Excessive optimism about making offers in the future can cause delays in agreement. The main result states that, despite this, if players will remain sufficiently optimistic for a sufficiently long future, then in equilibrium they will agree immediately. This result is also extended to other canonical models of optimism.     

Concurrent generation of binary and nonnormal continuous data through fifth-order power polynomials

Data collection process in most observational and experimental studies yield different types of variables, leading to the use of joint models that are capable of handling multiple data types. Evaluation of various statistical techniques that have been developed for mixed data in simulated environments requires concurrent generation of multiple variables. In this article, I present an important augmentation to a unified framework proposed in our previously published work for simultaneously generating binary and nonnormal continuous data given the marginal characteristics and correlation structure, via fifth-order power polynomials that are known to extend the area covered in the skewness-elongation plane and to provide a better approximation to the probability density function of the continuous variables. I evaluate how well the improved methodology performs in comparison to the original one, in a simulated setting with illustrations of algorithmic steps. Although the relative gains for the associational quantities are not substantial, the augmented version appears to better capture the marginal quantities that are pertinent to the higher-order moments, as indicated by very close resemblance between the specified and empirically computed quantities on average.     

PoisNor: An R package for generation of multivariate data with Poisson and normal marginals

In this article, the operational details of the R package PoisNor that is designed for simulating multivariate data with count and continuous variables with a prespecified correlation matrix are described, and examples of some important functions are given. The data-generation mechanism is a combination of the “NORmal To Anything” principle and a recently established connection between Poisson and normal correlations. The package provides a unique and useful tool that has been lacking for generating multivariate mixed data with Poisson and normal components.     

Multiple Imputation Under Power Polynomials

Although the normality assumption has been regarded as a mathematical convenience for inferential purposes due to its nice distributional properties, there has been a growing interest regarding generalized classes of distributions that span a much broader spectrum in terms of symmetry and peakedness behavior. In this respect, Fleishman's power polynomial method seems to have been gaining popularity in statistical theory and practice because of its flexibility and ease of execution. In this article, we conduct multiple imputation for univariate continuous data under Fleishman polynomials to explore the extent to which this procedure works properly. We also make comparisons with normal imputation models via widely accepted accuracy and precision measures using simulated data that exhibit different distributional features as characterized by competing specifications of the third and fourth moments. Finally, we discuss generalizations to the multivariate case. Multiple imputation under power polynomials that cover most of the feasible area in the skewness-elongation plane appears to have substantial potential of capturing real missing-data trends.     

Why Do We Care About Having More than Others? Socioeconomic Determinants of Positional Concerns in Different Domains

This paper uses survey data to provide empirical evidence regarding the importance of positional concerns in different domains and their determinants. In the survey, respondents are asked to choose between a world in which they have more of a good than others and a world in which their endowment along with everyone else is greatly improved, but they have less than others. Our results lend support to the claim that positional concerns play a crucial role in the decision-making process of individuals, though they appear to be more influential in some domains than others. Moreover, empirical results point out relationships between the age, gender, education, and income level of the respondents and their positional concerns in different domains.     

On accurate and precise generation of generalized Poisson variates

This article is concerned with comparison of a few random variate generation techniques for the generalized Poisson distribution. An evaluation is conducted on the degree of proximity between the estimates for its two distributional parameters and first four moments, and the specified or computed true population values via commonly accepted accuracy and precision measures in a simulated setting.     

Support Needs of the Survivors of the August 17, 1999 Earthquake in Turkey

This paper aims to present a sociological analysis of social supportrelated to disaster sociology in general and earthquakes specifically.The analysis is based on field research conducted a year after the17 August 1999 East Marmara Earthquake where 18000 people died;50000 people injured; 5000 buildings collapsed and 340000 of themdamaged; 14513 businesses closed; 150000 people became unemployed;and 129338 forced to live in prefabricated houses. The researchprimarily aims to understand the support needs by focusing mainlyon the following issues about the earthquake survivors: (i) whichand what support types and areas are mostly desired by them; (ii)which independent variables are the most effective on their demands;and (iii) from which persons or institutions they have receivedsupport and wanted to be supported by. Marginal development of thecivil society; high degree of expectations of almost everythingfrom the state; psychological factors being less influentialcompared with West and education being the primary influentialfactor are some of the significant results of this research.     

Performances of some goodness-of-fit tests for sampling designs in ranked set sampling

In this study, we consider different sampling designs of ranked set sampling (RSS) and give empirical distribution function (EDF) estimators for each sampling designs. We provide comparative graphs for the EDFs. Using these EDFs, power of five goodness-of-fit tests are obtained by Monte Carlo simulations for Tukey's g–h distributions under RSS and simple random sampling (SRS). Performances of these tests are compared with the tests based on the SRS. Also, critical values belong to these tests are obtained for different set and cycle sizes.     

Reliable Supply Chain Network Design

Risk management in supply chains has been receiving increased attention in the past few years. In this article, we present formulations for the strategic supply chain network design problem with dual objectives, which usually conflict with each other: minimizing cost and maximizing reliability. Quantifying the total reliability of a network design is not as straightforward as total cost calculation. We use reliability indices and develop analytical formulations that model the impact of upstream supply chain on individual entities’ reliability to quantify the total reliability of a network. The resulting multiobjective nonlinear model is solved using a novel hybrid algorithm that utilizes a genetic algorithm for network design and linear programming for network flow optimization. We demonstrate the application of our approach through illustrative examples in establishing tradeoffs between cost and reliability in network design and present managerial implications.