Europe's missing yollies

In this Policy Brief, Bruegel Senior Fellow Reinhilde Veugelers and Michele Cincera, Professor at ULB, draw our attention to young leading innovators ('yollies'). They explain why the European Union's business research and development deficit, relative to the United States, can be attributed to the EU having fewer yollies, especially those that are less R&D intensive. This paper raises important and timely questions about the EU's innovation policy. The authors argue why policy makers should pay attention to the heterogeneity across young sectors and design sector-specific measures to boost innovation and growth in the EU.

Missing McVeigh

The bombing that killed at least 169 people became an event by which time was thereafter measured — at least in Oklahoma. Ninety minutes after the bombing, a state trooper arrested Timothy McVeigh on a traffic charge; within hours, he was linked to the bombing, and the legal process began. Terry Nichols, who had met McVeigh when they were in the army together, was arrested in Herington, Kansas, where he lived with his wife and daughter. The Tenth Circuit chief judge designated Richard Matsch, chief judge for the District of Colorado, to preside over the case. Judge Matsch came to Oklahoma City, where he heard — and on February 20, 1996, granted — a motion to change venue to Colorado. In later hearings, he granted McVeigh and Nichols separate trials. Victims groups — to give them a name that includes survivors, families, and supporters — exerted influence on the trials and on public policy. The victims groups focused on three principal tasks: first, to plan and build a memorial in Oklahoma City; second, to lobby for changes in the law to limit appeals in capital cases; and third, to witness, influence, and provide evidence in the criminal trials and posttrial events in the McVeigh and Nichols cases (pp. 70–78). Now comes Professor Madeira’s thoughtful and timely study, Killing McVeigh: The Death Penalty and the Myth of Closure. Madeira examines the ways that victims groups came together and the goals they set for themselves. Her book is based on dozens of interviews with dozens of victims, her study of the trial record and media coverage, and a survey of literature on group memory and victim participation in trials. The book’s title is apt. Madeira exposes and dismisses the myth that killing a perpetrator gives victims any benefit that can meaningfully be called closure. She focuses, rather, on “memory work,” a term she uses over 100 times to describe words, actions, and feelings of victims. Madeira derives the idea of memory work from two distinct sources and amply documents the many sources on which she relies. First, she refers to the work of such groups as survivors of the Nazi holocaust, whose shared loss has led them to create memorial spaces and to seek redress, healing, and accountability. Second, she draws on her own and others’ work with and about crime victims, who seek to have their narratives and interests play a role in the criminal process. Killing McVeigh centers primarily on the actions, trial, and execution of Timothy McVeigh. Madeira discusses Terry Nichols and his defense team as a sort of contrast, to share victims’ reactions to, and resentment of, the fact that Nichols was acquitted of the most serious charges against him and did not receive the death penalty (p. 167). This blending helps us see the Oklahoma City survivors’ stories in a broad, empathic, and informative context. But, I shall argue, we ought also to be careful lest we assign primacy to the survivors’ narratives over those of others in and beyond the trial process. In this Review, I assess the relationship of group memory to the study of history, the presentation and distortion of memory in the trial process, and the use of group memory as an argument for retribution. I conclude with reflections on McVeigh and his death

Missing Market in Labor Quality: The Role of Quality Markets in Transition

This paper characterizes a key feature of the classic socialist economy and state-owned enterprise, namely that of missing markets in labor quality. Under the socialist regime in which students and workers were assigned to work units, the rights of managers to monitor and reward workers were limited. The exchange of labor services was based more on measures of quantity rather than quality. Workers who performed functions broadly consistent with that of their assigned occupations for the duration of the designated workweek received the standard wage. With the reassignment of property rights, this situation has changed. Students and workers have resumed control over the accumulation of their human capital the trade of skill and effort. Managers have acquired greater authority to monitor labor - to discriminate in setting wages and bonuses and to hire and fire - as well as stronger incentives to use this authority to raise efficiency and profits. The result is an emerging market in labor quality.A 1995 cross section of enterprise data spanning 10 ownership types is used to test the hypothesis of an emerging labor quality market. The results show that certain non-state forms of ownership, in which the rights of managers to monitor and reward skill and effort are presumed to be relatively well developed, encourage labor quality, most notably training, which raises productivity. The relative inability of state enterprises to monitor and reward high quality labor is likely to create an adverse selection problem in which the most skilled and motivated workers exit from the state sector, so as to cause a "hollowing" of skilled workers and weakened enterprise performance. The theoretical contribution of this paper is to generalize Coase's analysis of the critical role of property rights in creating resource markets to the creation and exchange of quality in all goods. Analytically, the conditions for a missing market in labor quality are equivalent to those for a missing market in pollution abatement and water quality. The analysis underscores the importance of property rights in creating the conditions for the accumulation and efficient exchange of human capital.http://deepblue.lib.umich.edu/bitstream/2027.42/39645/3/wp260.pd

‘Missing Persons’? Representations of Mature Female Sexuality in British and Irish Film 1998-2011

Research has indicated that a number of recent US films have challenged hegemonic definitions of mature women as asexual by validating the sexual pleasure and sexual agency of female characters over forty five years of age. This paper seeks to ascertain whether, and to what extent, this ideological shift is being replicated in British and Irish cinema. A content analysis establishes the number of British and Irish films, either domestic productions or co-productions, in which a mature female protagonist, or central character, is sexually active or demonstrates sexual desire. A thematic, qualitative analysis of the corpus suggests that, in accordance with research on US films, the narratives of a small number of British and Irish films do indeed offer mature female sexuality conditional support, within certain parameters. With one exception, narratives implicitly identify marriage or romantic love as the appropriate forum for a mature character to express her sexuality. Further, the representation of active female sexuality is quite restrained in the vast majority of such films; it is primarily confined to an on-screen kiss and the majority of simulated sexual activity occurs pre- or post-coitus. Even if mature female characters are depicted as sexually active, the mature female body usually remains strategically concealed. Finally, mature female characters are white, middle class, slim and able-bodied women

Block-Conditional Missing at Random Models for Missing Data

Two major ideas in the analysis of missing data are (a) the EM algorithm [Dempster, Laird and Rubin, J. Roy. Statist. Soc. Ser. B 39 (1977) 1--38] for maximum likelihood (ML) estimation, and (b) the formulation of models for the joint distribution of the data ${Z}$ and missing data indicators ${M}$, and associated "missing at random"; (MAR) condition under which a model for ${M}$ is unnecessary [Rubin, Biometrika 63 (1976) 581--592]. Most previous work has treated ${Z}$ and ${M}$ as single blocks, yielding selection or pattern-mixture models depending on how their joint distribution is factorized. This paper explores "block-sequential"; models that interleave subsets of the variables and their missing data indicators, and then make parameter restrictions based on assumptions in each block. These include models that are not MAR. We examine a subclass of block-sequential models we call block-conditional MAR (BCMAR) models, and an associated block-monotone reduced likelihood strategy that typically yields consistent estimates by selectively discarding some data. Alternatively, full ML estimation can often be achieved via the EM algorithm. We examine in some detail BCMAR models for the case of two multinomially distributed categorical variables, and a two block structure where the first block is categorical and the second block arises from a (possibly multivariate) exponential family distribution.Comment: Published in at http://dx.doi.org/10.1214/10-STS344 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Identifiability of Normal and Normal Mixture Models With Nonignorable Missing Data

Missing data problems arise in many applied research studies. They may jeopardize statistical inference of the model of interest, if the missing mechanism is nonignorable, that is, the missing mechanism depends on the missing values themselves even conditional on the observed data. With a nonignorable missing mechanism, the model of interest is often not identifiable without imposing further assumptions. We find that even if the missing mechanism has a known parametric form, the model is not identifiable without specifying a parametric outcome distribution. Although it is fundamental for valid statistical inference, identifiability under nonignorable missing mechanisms is not established for many commonly-used models. In this paper, we first demonstrate identifiability of the normal distribution under monotone missing mechanisms. We then extend it to the normal mixture and $t$ mixture models with non-monotone missing mechanisms. We discover that models under the Logistic missing mechanism are less identifiable than those under the Probit missing mechanism. We give necessary and sufficient conditions for identifiability of models under the Logistic missing mechanism, which sometimes can be checked in real data analysis. We illustrate our methods using a series of simulations, and apply them to a real-life dataset