Cognitive and interpersonal abilities related to the primary activities of R&D managers

This study attempted to identify the primary activities performed by R&D managers at different supervisory levels, and the cognitive and interpersonal ability requirements that underlie these activities. A task inventory containing 244 managerial tasks was completed by 117 R&D managers in nine organizations. The 48 tasks which managers rated as having spent the most time were factor analyzed, resulting in the identification of three primary activities: project management, personnel supervision, and strategic planning. Furthermore, these activities varied with supervisory level, with R&D managers performing a wider range of these activities with the progression from first-line to upper-level management.Thirty R&D managers later rated the degree to which 19 cognitive and interpersonal abilities were required for performing these primary activities. Strategic planning was rated as requiring significantly higher levels of oral expression, logical reasoning, originality, fluency of ideas, oral defense, and resistance to premature judgement than was required for the other two primary activities. Personnel supervision required a higher level of social sensitivity than was required for the other two activities. Project management required high levels of information ordering, oral fact- finding ability, problem sensitivity, and oral and written comprehension. Implications of the findings for understanding R&D managerial performance and personnel staffing functions are discussed.     

Tax-based research and data on non-profit organisations

Tax-exempt, non-profit organisations represent a significant and growing sector within the US economy. Between 1975 and 1990, assets of tax-exempt organisations increased in real terms by over 150 per cent while the revenue increased by over 227 per cent. This compares to a growth in real GDP of 52 per cent over the same period. A variety of tax policy issues on tax-exempt organisations and the non-profit sector can be addressed using several sources of data collected by the IRS from federal information and tax returns of exempt organisations. The Statistics of Income (SOI) Division, using sample data, conducts studies of many of the different components of the tax-exempt sector, including non-profit charitable organisations, organisations exempt under sections 501(c)(4)-(c)(9), private foundations and 4947(a) charitable trusts, and the unrelated business income of tax-exempt organisations. Income statement, balance sheet and other financial data, as well as a great amount of non-financial information, are collected in these SOI studies. The primary purposes of this article are: first, to document the role of the non-profit sector in the US economy and the evolving growth and change within the sector from the mid-1970s through to the present; and, second, to describe the ongoing SOI studies of tax-exempt organisations, the products and services available through SOI, and the future statistical plans at SOI for data collection and analysis of tax-exempt organisations and the non-profit sector.     

What to Do at Low Doses: A Bounding Approach for Economic Analysis

To quantify the health benefits of environmental policies, economists generally require estimates of the reduced probability of illness or death. For policies that reduce exposure to carcinogenic substances, these estimates traditionally have been obtained through the linear extrapolation of experimental dose-response data to low-exposure scenarios as described in the U.S. Environmental Protection Agency's Guidelines for Carcinogen Risk Assessment (1986). In response to evolving scientific knowledge, EPA proposed revisions to the guidelines in 1996. Under the proposed revisions, dose-response relationships would not be estimated for carcinogens thought to exhibit nonlinear modes of action. Such a change in cancer-risk assessment methods and outputs will likely have serious consequences for how benefit-cost analyses of policies aimed at reducing cancer risks are conducted. Any tendency for reduced quantification of effects in environmental risk assessments, such as those contemplated in the revisions to EPA's cancer-risk assessment guidelines, impedes the ability of economic analysts to respond to increasing calls for benefit-cost analysis. This article examines the implications for benefit-cost analysis of carcinogenic exposures of the proposed changes to the 1986 Guidelines and proposes an approach for bounding dose-response relationships when no biologically based models are available. In spite of the more limited quantitative information provided in a carcinogen risk assessment under the proposed revisions to the guidelines, we argue that reasonable bounds on dose-response relationships can be estimated for low-level exposures to nonlinear carcinogens. This approach yields estimates of reduced illness for use in a benefit-cost analysis while incorporating evidence of nonlinearities in the dose-response relationship. As an illustration, the bounding approach is applied to the case of chloroform exposure.     

Racial differences in urban neighboring

Despite mixed expectations generated by existing theories and evidence, this analysis documents clear racial differences in urban neighboring behavior. Using data from a survey of Nashville, Tennessee, residents, we show that blacks interact with their neighbors more often than whites do, and in a greater variety of ways. The only noteworthy similarity between the two groups is the positive impact of neighboring on feelings of community affect. Overall, our results support the view that neighbor relations — like other kinds of informal participation — have helped blacks cope with constrained social opportunities and provided them with access to resources unavailable through formal institutional channels.Revised version of a paper presented at the annual meeting of the American Sociological Association, San Francisco, August 1989.     

The impact of unionization,right-to-work laws,and female labor force participation on earnings inequality across states

This study examines the effect of unionization, right-to-work laws, and participation of women in the labor force on income inequality. Two distinct models are developed using 1970 and 1980 census data on the 50 states in the U.S. First, an income inequality model is specified as a beta distribution of the second kind to estimate Gini measures of income inequality. Second, these Gini estimates are used in a simultaneous equations model. The 1970 results indicate that higher unionization rates decreased inequality while right-to-work laws increased inequality. In 1980, the measure of inequality was lower in states with higher female labor force participation. We thank an unknown referee and the editor for comments and criticisms that greatly improved the paper. The usual caveat applies.     

Economic Reform in the Czech Republic: Economic Strain,Depression, Hostility,and the Difference Gender Makes*

The Czech Republic is changing rapidly and its current economic transformation is a strain on Czech families. In this study, the connection between economic strain and individual well-being is investigated via mailed questionnaires from 234 households in the Czech Republic. For Czech women, depression rises more with economic strain than does hostility, while Czech men become more hostile than depressed in the face of economic strain. For women, social support is a buffer between economic strain and hostility, whereas self-esteem is a buffer for depression. For men, both self-esteem and social support exacerbate hostility in the context of economic strain.     

Grounded Neural Networking: Modeling Complex Quantitative Data

The latest advances in artificial intelligence software (neural networking) have finally made it possible for qualitative researchers to apply the grounded theory method to the study of complex quantitative databases in a manner consistent with the postpositivistic, neopragmatic assumptions of most symbolic interactionists. The strength of neural networking for the study of quantitative data is twofold: it blurs the boundaries between qualitative and quantitative analysis, and it allows grounded theorists to embrace the complexity of quantitative data. The specific technique most useful to grounded theory is the Self‐Organizing Map (SOM). To demonstrate the utility of the SOM we (1) provide a brief review of grounded theory, focusing on how it was originally intended as a comparative method applicable to both quantitative and qualitative data; (2) examine how the SOM is compatible with the traditional techniques of grounded theory; and (3) demonstrate how the SOM assists grounded theory by applying it to an example based on our research.