Social security has been the most, perhaps the only, popular social welfare program in the United States. Until recently it has steadily expanded in coverage, beneficiaries, and costs with little fanfare or notice. Since the mid-1970s that expansion has begun to threaten its financial soundness. Literal bankruptcy has become a short-run possibility as expenditures continually outrun receipts. Worse, in some respect, is the realistic possibility that the retirement costs of the baby-boom generation in the twenty-first century may be too great a burden for future workers to bear. How well social security has done, is doing, and is projected to do are analyzed in terms of the system's twin goals of adequacy and individual equity. Options for change are severely limited by our stumbling economy and the high costs of a mature pension system. It is not likely that the traditional groups and alliances that played important roles in the expansion of social security will play predictable roles in its retrenchment.
MEASURES USED TO PROTECT SUBJECTS in publicly distributed microdata files often have a significant negative impact on key analytic uses of the data. For example, it may be important to analyze subpopulations within a data file such as racial minorities, yet these subjects may present the greatest disclosure risk because their records tend to stand out or be unique. Files or records that are linkable create another type of disclosure risk-common elements between two files can be used to link files with sensitive data to externally available files that disclose identity. Examples of disclosure limitation methods used to address these types of issues include blanking out data, coarsening response categories, or withholding data altogether. However, the very detail that creates the greatest risk also provides insight into differences that are of greatest interest to analysts. Restricted-use agreements that provide unaltered versions of the data may not be available, or only selectively so. The public-use version of the data is very important because it is likely to be the only one to which most researchers, policy analysts, teaching faculty, and students will ever have access. Hence, it is the version from which much of the utility of the data is extracted and often it effectively becomes the historical record of the data collection. This underscores the importance that the disclosure review c ommittee s trikes a g ood b alance b etween protection and u tility. In this paper we d escrib e our disclosure review committee's (DRC) analysis and resulting data protection plans for two national studies and one administrative data system. Three distinct disclosure limitation methods were employed, taking key uses of the data into consideration, to protect respondents while still providing statistically accurate and highly useful public-use data. The techniques include data swapping, microaggregation, and suppression of detailed geographic data. We describe the characteristics of the data sets that led to the selection of these methods, provide measures of the statistical impact, and give details of their implementations so that others may also utilize them. We briefly discuss the composition of our DRC, highlighting what we believe to be the important disciplines and experience represented by the group.
Microeconomics refers to the economics of decision-making units, such as an individual consumer, a household, or a firm. In this paper we are concerned with microeconomic simulation models. Such models have been used for the purpose of analyzing the impact of various policies, such as tax and welfare reform, upon the distribution of income of households. For a comprehensive exposition of microeconomic simulation models see Orcutt, Caldwell and Wertheimer (1976). A microeconomic simulation model often employs Monte Carlo methods to alter the time-varying characteristics of a population; see Orcutt and Smith (1979). The U.S. government uses such a model to simulate a data base of individuals, their earnings histories and demographic characteristics in order to plan and execute policy decisions dealing with social security taxes and benefits (United States Department of Health and Human Services 1985 and Congressional Budget Office 1986).
In this article, the author describes a possibly unfamiliar unconscious, namely the unconscious that arises from how our brains have evolved. That unconscious is active in sense perceptions, in many verbal and nonverbal aspects of language, and from our brains' propensity to posit instant explanations independent of relevant information. All of the sources used in the exposition are readily available to persons without any formal knowledge of sense perception, evolutionary psychology, linguistics, or neuroscience. As the narrative proceeds, applications to social work knowledge are presented.
Objective: The objective of this article is to estimate and validate a logistic model of alcohol-impaired driving using previously ignored alcohol consumption behaviors, other risky behaviors, and demographic characteristics as independent variables. Methods: The determinants of impaired driving are estimated using the US Centers for Disease Control and Prevention's (CDC) Behavioral Risk Factor Surveillance System (BRFSS) surveys. Variables used in a logistic model to explain alcohol-impaired driving are not only standard sociodemographic variables and bingeing but also frequency of drinking and average quantity consumed, as well as other risky behaviors. We use interactions to understand how being female and being young affect impaired driving. Having estimated our model using the 1997 survey, we validated our model using the BRFSS data for 1999. Results: Drinking 9 or more times in the past month doubled the odds of impaired driving. The greater average consumption of alcohol per session, the greater the odds of driving impaired, especially for persons in the highest quartile of alcohol consumed. Bingeing has the greatest effect on impaired driving. Seat belt use is the one risky behavior found to be related to such driving. Sociodemographic effects are consistent with earlier research. Being young (18–30) interacts with two of the alcohol consumption variables and being a woman interacts with always wearing a seat belt. Our model was robust in the validation analysis. Conclusions: All 3 dimensions of drinking behavior are important determinants of alcohol-impaired driving, including frequency and average quantity consumed. Including these factors in regressions improves the estimates of the effects of all variables.
