Historical Thinking and Sports History: A Case Study Using the Brooklyn Dodgers

Abstract

This article discusses how the history of sports can serve as way to understand abstract concepts associated with local history and social studies education. An introductory discussion outlines how sports can engage and interest students, focusing especially on ideas related to history thinking (such as change and continuity). A case study using the Brooklyn Dodgers’ move to Los Angeles is used to illustrate these ideas, focusing on themes associated with suburbanization and urban renewal. A lesson plan is also provided.     

We are not walking wave functions. A response to “Quantum Mind and Social Science” by Alexander Wendt

Alexander Wendt raises many interesting questions in this book, but to get to the answers he wants, he relies on a misunderstanding of the nature of the quantum states of macroscopic objects.     

THE INTERACTION BETWEEN MARITAL STANDARDS AND COMMUNICATION PATTERNS: HOW DOES IT CONTRIBUTE TO MARITAL ADJUSTMENT?

Couples' cognitions about marriage and their communication patterns have both been found to be highly associated with marital distress. To examine the relationships among marital cognitions, communication, and marital adjustment, we had a sample of 387 community couples complete three self-report measures: the Dyadic Adjustment Scale, the Inventory of Specific Relationship Standards, and the Communication Patterns Questionnaire. Results indicated that the correlation of communication with marital adjustment is higher for women with more relationship-focused standards than for women with less relationship-focused standards; this interaction does not occur for men. The implications of the interaction and the gender difference as well as the importance of teaching communication skills, and working with associated cognitions are discussed.     

Choosing the Number of Instruments

Properties of instrumental variable estimators are sensitive to the choice of valid instruments, even in large cross‐section applications. In this paper we address this problem by deriving simple mean‐square error criteria that can be minimized to choose the instrument set. We develop these criteria for two‐stage least squares (2SLS), limited information maximum likelihood (LIML), and a bias adjusted version of 2SLS (B2SLS). We give a theoretical derivation of the mean‐square error and show optimality. In Monte Carlo experiments we find that the instrument choice generally yields an improvement in performance. Also, in the Angrist and Krueger (1991) returns to education application, when the instrument set is chosen in the way we consider, it turns out that both 2SLS and LIML give similar (large) returns to education.     

A Bias–Reduced Log–Periodogram Regression Estimator for the Long–Memory Parameter

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^d_r, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The reduction in bias is obtained using assumptions on the spectrum only in a neighborhood of the zero frequency. Following the work of Robinson (1995b) and Hurvich, Deo, and Brodsky (1998), we establish the asymptotic bias, variance, and mean–squared error (MSE) of ^d_r, determine the asymptotic MSE optimal choice of the number of frequencies, m, to include in the regression, and establish the asymptotic normality of ^d_r. These results show that the bias of ^d_r goes to zero at a faster rate than that of the GPH estimator when the normalized spectrum at zero is sufficiently smooth, but that its variance only is increased by a multiplicative constant. We show that the bias–reduced estimator ^d_r attains the optimal rate of convergence for a class of spectral densities that includes those that are smooth of order s≥1 at zero when r≥(s−2)/2 and m is chosen appropriately. For s>2, the GPH estimator does not attain this rate. The proof uses results of Giraitis, Robinson, and Samarov (1997). We specify a data–dependent plug–in method for selecting the number of frequencies m to minimize asymptotic MSE for a given value of r. Some Monte Carlo simulation results for stationary Gaussian ARFIMA (1, d, 1) and (2, d, 0) models show that the bias–reduced estimators perform well relative to the standard log–periodogram regression estimator.     

The “difference between heaven and earth”: Urban–rural disparities in well-being in China

Although China is a rapidly developing nation, rural–urban disparities in well-being remain large, and perhaps have become larger than in the early years of the Communist period because the urban sector has benefited from China's transition to a market economy much more than has the rural sector; or perhaps have become smaller as earning opportunities in the export-oriented manufacturing sector have increased for those from rural origins. Economic disparities are exacerbated by institutional arrangements that have created a two-class society based on registration (hukou) status with sharp rural–urban distinctions in the public provision of schooling, health care, housing, and retirement benefits. Indeed, it is fair to say that China built an urban welfare state on the backs of the peasants.     

Excluded losses and the demand for insurance

The demand for insurance is examined when the insured asset can incur losses that are excluded from insurance coverage. These losses are negatively correlated with covered losses and hence cannot be treated as background risk. Excluded losses have strikingly different effects on the demand for insurance than does background risk and lead to a modification of many standard insurance demand results. A number of new theorems concerning the effects of excluded losses are also presented. Risk-averse and prudent decision makers reduce their demand for insurance when excluded losses increase in size or riskiness. Excluded losses are a possible explanation for why many decision makers fail to take up insurance when it is offered.