Comparative Capitalism without Capitalism,and Production without Workers: The Limits and Possibilities of Contemporary Institutional Analysis

The aim of this paper is to consider the extent to which the comparative capitalism literature fully reflects the available empirical evidence in its attempts to model different versions of capitalism and, in particular, whether it adequately captures the roles of diverse stakeholders within the capitalist system. In doing so, particular attention is accorded to the varieties of capitalism literature, business systems theory and regulation theory. In addition, there is reflection in the paper on whether any strand of the literature is able to deal effectively with the recent economic crisis and systemic change. It is argued that more attention needs to be devoted to exploring the structural causes of change and the marginalization of the interests of key social groupings, most notably workers, from the process of institutional redesign.     

Is neighborhood racial succession place-specific?

This paper evaluates the geographic generality of the succession model of neighborhood racial change for the period 1970 to 1980. Using census data on racially mixed tracts, we determine whether white-to-black compositional shifts were equally common across the four regions and 58 central cities in our sample. Substantial variation occurred by region in the incidence and magnitude of racial succession; tracts in western cities departed most markedly from expectations. Even in other regions, some cities experienced more numerous instances of stability and displacement than of succession. These region and city effects persist when neighborhood characteristics believed to influence racial transition are controlled.     

Patenting Our Food Future: Intellectual Property Rights and the Global Food System

Intellectual Property Rights (IPRs) underpin the new knowledge economy and their ownership and control affects the distribution of wealth and power. This paper explores the changing international IPRs regime and its possible impact on the food system, in particular through their connection with biotechnology and marketing. It outlines how IPRs have been extended into agriculture globally through the Agreement on the Trade–Related Aspects of Intellectual Property Rights ( TRIPS) at the World Trade Organization ( WTO) and the other related international agreements. It identifies two problems. One is with the way the global rules are made—which is unbalanced with too few of the interested parties taking part in the process. The other is with whether the rules themselves are appropriate in food and agriculture. Their possible impact, especially patents, on increasingly proprietary, agricultural research and development is discussed as well as the possible effects on seed provision of the requirement for some form of IPRs protection to be available for plant varieties in all WTO members, sooner or later. Possible options for rethinking the rules to make them more appropriate for food and agriculture are discussed, as well as effects on market structure and the environment. Brief mention is made of other IPRs—trademarks, trade secrets and geographical indications—upon the way business in the food system is carried out. The paper concludes that this complex subject area is worthy of much more scrutiny because of the potential impact of these esoteric and complex rules.     

The New Statistics for Better Science: Ask How Much,How Uncertain,and What Else Is Known

Abstract

The “New Statistics” emphasizes effect sizes, confidence intervals, meta-analysis, and the use of Open Science practices. We present three specific ways in which a New Statistics approach can help improve scientific practice: by reducing overconfidence in small samples, by reducing confirmation bias, and by fostering more cautious judgments of consistency. We illustrate these points through consideration of the literature on oxytocin and human trust, a research area that typifies some of the endemic problems that arise with poor statistical practice.     

Statistical equality and cultural difference in Indigenous wellbeing frameworks: A new expression of an enduring debate

Recent years have seen a burgeoning interest in developing indicator frameworks for ‘Indigenous wellbeing’. Implicit in each of the frameworks are particular conceptions of what constitutes the ‘good life’ for Indigenous peoples and what ‘Indigenous development’ should entail. In developing these frameworks, then, certain judgements must be made about whether statistical equality should be prioritised as a ‘development’ goal. This issue has generated long‐standing debate and in this context must be broached anew. In this paper we briefly examine the growing interest in Indigenous wellbeing and outline three prominent indicator frameworks: the Productivity Commission's indicators for ‘Overcoming Indigenous Disadvantage’; the ‘capability indicators’ developed by the Cape York Institute for Policy and Leadership; and the indicators of wellbeing developed by the United Nations Permanent Forum on Indigenous Issues. The first prioritises statistical equality between Indigenous and non‐Indigenous Australians; the second adds a concern with ‘capabilities’; and the last emphasises the importance of distinct cultural preferences. We offer an assessment of these approaches, drawing in part on Amartya Sen's work. We argue that in seeking to improve the wellbeing of Indigenous Australians, policy‐makers should not only make their own normative assumptions clear, but also be aware of the implications of their decisions for constituents with different worldviews.     

A Geometric Examination of Linear Model Assumptions

From a geometric perspective, linear model theory relies on a single assumption, that (‘corrected’) data vector directions are uniformly distributed in Euclidean space. We use this perspective to explore pictorially the effects of violations of the traditional assumptions (normality, independence and homogeneity of variance) on the Type I error rate. First, for several non‐normal distributions we draw geometric pictures and carry out simulations to show how the effects of non‐normality diminish with increased parent distribution symmetry and continuity, and increased sample size. Second, we explore the effects of dependencies on Type I error rate. Third, we use simulation and geometry to investigate the effect of heterogeneity of variance on Type I error rate. We conclude, in a fresh way, that independence and homogeneity of variance are more important assumptions than normality. The practical implication is that statisticians and authors of statistical computing packages need to pay more attention to the correctness of these assumptions than to normality.