Informal Work in Nonmetropolitan Pennsylvania1

Abstract It is well recognized that the informal economy—unregulated economic activities that generate real or in-kind income—features prominently in the day-to-day lives of many in the developing world. Researchers have begun to explore the informal economy in developed countries but this work has focussed primarily on urban areas to the neglect of rural areas. In this paper the nature and correlates of informal work in nonmetropolitan Pennsylvania are described through an analysis of survey data on 505 families. Results indicate that participation in informal activities is widespread, is not more typical of the poor, does not contribute greatly to family income on average but does help many poor families weather difficult economic times, is both economically and noneconomically motivated, and, net of other sociodemographic variables, is positively related to rurality of residence and formal labor supply.     

Using the lessons learned from US and English law to create a regulatory framework for charities in evolving democracies

The evolving democracies of Eastern Europe and the former USSR republics are keenly interested in establishing charities. They have little law on this issue because their former governments did not encourage the development of charities. This article compares US and English regulation of charities and proposes a regulatory framework based on what the authors believe are the best attributes of both. Although the framework is based on the common law system, it can be adapted to the civil law systems of the Eastern European nations and the former USSR republics. The article includes such issues as developing a body of law, tax relief and creating a regulatory agency.Mr Hopkins is a lawyer with Powers, Pyles & Sutter, 1275 Pennsylvania Ave., N.W., Washington, D.C. 20007, and author ofThe Law of Tax-Exempt Organizations (1992a).Ms Moore is also a lawyer, 5908 N. 35th St., Arlington, Virginia 22207.The authors would like to thank Mssrs Kersi Shroff and Stephen F. Clarke, Senior Legal Specialists, American-British Law Division, Library of Congress, and Mr. William H. Morris, an attorney at Steptoe & Johnson, Washington, D.C., for their kind assistance during the researching of this article.     

Bankrupt Rhetoric: How Misleading Information Affects Knowledge about Social Security

Most citizens know little about politics. Scholars often attributepolitical ignorance to individual-level factors, but we concentrateon the quality of the information environment. Employing a combinationof experimental methods and content analysis, we code statementsfrom the 1998–99 debate over Social Security reform aseither misleading or not misleading. Then, using surveys conductedduring the debate, we examine the impact of individual- andenvironmental-level variables on political knowledge about theprogram’s future. We show that misleading statements aboutSocial Security’s future cause some citizens to get animportant fact about the program wrong. More precisely, manycitizens mistakenly believe that Social Security will run outof money because political elites occasionally use words thatlead to overly pessimistic assessments of the program’sfinancial future. Our findings have important implications forpolicymakers who are attempting to remake America’s largestfederal program, scholars who study citizen competence, andcitizens in a representative democracy.     

Sektion Frauen- und Geschlechterforschung

Ohne Zusammenfassung     

Determinants and Consequences of Social Trust*

This article provides an overview of social trust, examining its various aspects and components. Trust is best understood in a sociological sense by focusing on its important relational characteristic. Following this lead, the article discusses briefly how social trust relates to social capital and examines factors that shape the development of social trust, along with outcomes related to variations in trust, classifying them by analytical level (i.e., individual, community, group, organizational, and societal). The article concludes by assessing the strengths and weaknesses of existing research and by identifying some important questions that have not yet been adequately addressed.     

Low dose risk estimation via simultaneous statistical inferences

Summary.  The paper develops and studies simultaneous confidence bounds that are useful for making low dose inferences in quantitative risk analysis. Application is intended for risk assessment studies where human, animal or ecological data are used to set safe low dose levels of a toxic agent, but where study information is limited to high dose levels of the agent. Methods are derived for estimating simultaneous, one-sided, upper confidence limits on risk for end points measured on a continuous scale. From the simultaneous confidence bounds, lower confidence limits on the dose that is associated with a particular risk (often referred to as a bench-mark dose ) are calculated. An important feature of the simultaneous construction is that any inferences that are based on inverting the simultaneous confidence bounds apply automatically to inverse bounds on the bench-mark dose.     

Divergence-based estimation and testing with misclassified data

The well-known chi-squared goodness-of-fit test for a multinomial distribution is generally biased when the observations are subject to misclassification. In Pardo and Zografos (2000) the problem was considered using a double sampling scheme and ø-divergence test statistics. A new problem appears if the null hypothesis is not simple because it is necessary to give estimators for the unknown parameters. In this paper the minimum ø-divergence estimators are considered and some of their properties are established. The proposed ø-divergence test statistics are obtained by calculating ø-divergences between probability density functions and by replacing parameters by their minimum ø-divergence estimators in the derived expressions. Asymptotic distributions of the new test statistics are also obtained. The testing procedure is illustrated with an example.     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.