Migration,Ethnicity and Solidarity: ‘Multinational Workers’ in the Former Soviet Union

We investigate migrant construction workers’ experiences in the Former Soviet Union, examining their attitudes to other ethno-national groups, unions and collective action. Industrial relations and migration studies view migrant workers’ hypermobility and diversity, under conditions of low union coverage and rising nationalism, as potentially obstructing consciousness-raising and mobilizing. Workers in our study faced union indifference, ethno-national segregation and discrimination. However, managerial abuses, informality and contestation from below led to spontaneous mobilization. Lack of institutional channels to solve these disputes drove workers’ further mobility. Complex mobility trajectories and collective action translated into increased awareness of collective interests and rejection of nationalist ideologies. The outcome is ‘multinational workers’ potentially resistant to nation-state politics and capital's logics but also aware of the value and usefulness of collective solidarities. Thus, previous arguments solely associating exit with individualistic attitudes, and post-socialist legacies with workers’ quiescence present only partial pictures.     

Information flows and crashes in dynamic social networks

We develop a dynamic model of information transmission and aggregation in social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity, in which agents’ opinions are widely dispersed.

     

PITFALLS IN CROSS‐SECTION STUDIES WITH INTEGRATED REGRESSORS: A SURVEY AND NEW DEVELOPMENTS

In cross‐section studies, if the dependent variable is I(0) but the regressor is I(1), the true slope must be zero in the resulting “unbalanced regression.” A spuriously significant relationship may be found in large cross‐sections, however, if the integrated regressor is related to a stationary variable that enters the DGP but is omitted from the regression. The solution is to search for the related stationary variable, in some cases the first difference of the integrated regressor, in other cases, a categorical variable that can take on limited number of values which depend on the integrated variable. We present an extensive survey, new developments, and applications particularly in finance.     

Thought leadership in investment banking: The beginning of a new era

The paper argues that although considerations such as industry consolidation, the role of lending and the effects of technology are still important in shaping the future of investment banking, sustainable competitive advantage necessitates that emphasis is also placed on thought leadership. This entails quite a radical change in terms of the way in which the industry approaches the problem of competition and requires emphasis on value creation for all the constituent parts of the industry: corporations, investors, the banks themselves and research departments. Accordingly, the paper provides a rationale for this change and provides inter alia a range of examples to illustrate how thought leadership could lead to a fundamental change in the future of the investment banking industry.     

Effect Sizes in Qualitative Research: A Prolegomenon

The American Psychological Association Task Force recommended that researchers always report and interpret effect sizes for quantitative data. However, no such recommendation was made for qualitative data. Thus, the first objective of the present paper is to provide a rationale for reporting and interpreting effect sizes in qualitative research. Arguments are presented that effect sizes enhance the process of verstehen/hermeneutics advocated by interpretive researchers. The second objective of this paper is to provide a typology of effect sizes in qualitative research. Examples are given illustrating various applications of effect sizes. For instance, when conducting typological analyses, qualitative analysts only identify emergent themes; yet, these themes can be quantitized to ascertain the hierarchical structure of emergent themes. The final objective is to illustrate how inferential statistics can be utilized in qualitative data analyses. This can be accomplished by treating words arising from individuals, or observations emerging from a particular setting, as sample units of data that represent the total number of words/observations existing from that sample member/context. Heuristic examples are provided to demonstrate how inferential statistics can be used to provide more complex levels of verstehen than is presently undertaken in qualitative research.