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.     

The Schwarzian derivative as a ranking of downside risk aversion

It is observed that the measure S _u  = u′′′/u′ − (3/2)(u′′/u′)², previously shown to be a relevant measure of the degree of downside risk aversion, is known in the mathematics literature as the Schwarzian derivative. The Schwarzian derivative has invariance properties under composition of functions that make it particularly well-behaved as a ranking of downside risk aversion. Indeed, it has the same invariance properties as the measure R _u  = −u′′/u′, familiar to economists as a ranking of utility functions by degree of Arrow-Pratt risk aversion.     

Community College Health Service Programs: A Progress Report

Abstract

What progress has been made in improving the health service programs available to the millions of students attending the nation's public community colleges? This question was investigated by sending a follow-up survey to deans of students in 1977; the original study was done five years earlier and resulted in a national profile of health service programs at public two-year institutions.

Some of the significant findings were: the percent of colleges offering health service programs has increased from 41.5% to 51.3%; the campus nurse continues to be the most common health professionals available to students; facilities are considered to be adequate and accessible; and a broad range of student health needs are met. New elements in the survey covered health education and mental health services, and these were positively evaluated.

On the basis of survey results it was concluded that community colleges have made considerable progress in improving student health services, but there is still much to be done.     

The International Migration of Nurses in Long-Term Care

This article describes five major factors that are affecting patterns of international migration among nurses who work in long-term care settings:

• Demographic drivers–The aging of the populations in developed countries and the low to negative growth in the working-age population will increase the demand for international workers to provide long-term care services.

• Gender and race–A dual labor market of long-term care workers, increasingly made up of women of color, is becoming internationalized by the employment of migrating nurses from developing countries.

• Credentialing–The process of credentialing skilled workers creates barriers to entry for migrating nurses and leads to “decredentialing” where registered nurses work as licensed practical nurses or aides.

• Colonial history and geography–The colonial histories of many European countries and the United States have increased migration from former colonies in developing countries to former colonial powers.

• Worker recruitment–Efforts to limit the recruitment of health care workers from some developing countries have had little effect on migration, in part because much of the recruitment comes through informal channels of family and friends.