Suchergebnisse

Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- List of figures -- List of tables -- Foreword -- Preface and Acknowledgments -- Chapter 1 Media Linguistics -- Chapter 2 Linguistics of News -- Chapter 3 News of Conflict: A South Asian Matrix -- Chapter 4 Lexico: Semantic Interpretation of the Cricket Headlines -- Chapter 5 Conclusion -- Bibliography -- Index.

Existing research on the incorporation of immigrants generally celebrates immigrant organizations (IOs) as essential conduits for political mobilization, civic integration, and transnational engagement. Less attention, however, has been given to the external contexts or conditions that can constrain IOs. In this article, I introduce the concept of ascriptive organizational stigma (AOS) and examine how domestic and geopolitical contexts contribute to the stigmatization and constraining of Pakistani immigrant organizational capacities. Data come from 59 in-depth interviews conducted with leaders and members of Pakistani IOs in New York City and London. Findings suggest Pakistani IOs in both cities experienced AOS, and that external pressures to prioritize stigma management over core missions, impeded efforts to serve domestic and homeland constituents. Findings also indicate the stigmatization of ascriptive status markers can contribute to the conflation of immigrants' group and organizational identities. This article contributes to existing scholarship by revealing how external contexts can lead to the constraining of immigrants' domestic and homeland-oriented organizational capacities.

This dissertation presents two chapters on empirical models of macroeconomics and finance, and one chapter on a theoretical model for conducting monetary policy. The first chapter applies machine learning algorithms to construct non-parametric, nonlinear predictions of mortgage loan default. I compile a large dataset with over 20 million loan observations from Fannie Mae and Freddie Mac, for the period 2001-2016 at the quarterly frequency. Different machine learning algorithms are applied to predict in sample (training sample), and to forecast out-of-sample (testing data). I find that the forecast performance of nonlinear and non-parametric algorithms are substantially better than the traditional logit model. Additionally, machine learning algorithms allow identification of the predictive power of specific variables. The results indicate that loan age is the most important predictor of loan default before and after the 2008 financial crisis. However, I find that market loan- to-value is the most effective predictor of mortgage loan default during the recent financial crisis. Finally, I use machine learning to formulate risk-based capital stress tests for Fannie Mae and Freddie Mac under different scenarios. I forecast their mortgage credit losses and associated capital needs during the financial crises. The results obtained are more accurate than those from the Federal Housing Enterprise Oversights (OFHEO), and other existing stress test studies. In the second, and third chapters, I tested the effectiveness of Monetary policy by empirical, and theoretical models. With the severity of the 2008 financial crisis, and apparent inefficacy of traditional monetary and fiscal policies, the Federal Reserve together with the U.S. government introduced unconventional policy measures. The Large Scale Asset Purchase (LSAP) and Troubled Asset Relief Program (TARP) are some of these policies introduced by the Federal Reserve and Department of Treasury. While these policies may have been important in preventing a deepening of the financial crisis and laying the foundation for the economic recovery, there were collateral effects on bank profitability. In this chapter, I study the impact of both the LSAP and TARP programs on banks? profit and risk taking using a large panel. The results indicate that these programs had a positive effect on banks? profit (Chapter 2). In chapter three ,I use a small-scale DSGE model for the economy of Iran to analyze monetary policy. The model is extended to include housing and oil sectors. The model is adapted for the peculiarities of Iran?s Central Bank, which uses money supply as a function of oil income and production growth. I study the reaction function of the model to technology, oil, and monetary shocks in this specific Iranian monetary policy framework. The results show that monetary shocks has only nominal effect on inflation but not on the real sector such as investment, consumption, or production. Also, positive oil income shocks lead to an increase in inflation instead of an increase in production.

AbstractIn this article, I examine voting patterns in origin and receiving country national elections among immigrants in Europe. The existing scholarship on transnational political engagement offers two competing interpretations of the relationship between immigrant integration and transnational engagement, which I classify as theresocializationandcomplementarityperspectives. The resocialization perspective assumes that transnational political engagement gradually declines as immigrants become socialized into the new receiving society. Conversely, the complementarity perspective assumes that immigrant integration increases transnational political engagement. I test these competing perspectives with survey data collected between 2004 and 2008 for 12 different immigrant groups residing in seven European cities. The analysis examines how immigrant political and civic participation in receiving countries affect their proclivities to vote in homeland elections. I also analyse the effects of receiving and origin country contexts on immigrant voting behaviour in homeland elections. While my findings support both the resocialization and complementarity perspectives, they also highlight the ways in which a set of origin‐country contexts shape immigrant propensities to engage in transnational electoral politics. I observe a degree of complementarity among immigrants with resources who are motivated and eligible to participate in both receiving and origin‐country elections.

This study examines how race and generational status shape self-employment propensities and industry-sector prestige among the self-employed in the U.S. It draws on theories of assimilation, racialization, and a combined framework, racialized incorporation, to guide the analysis and interpret the results. It uses data from the U.S. March Current Population Survey (2000–2010) offering the first nationally representative examination of second-generation self-employment in the U.S. This study investigates three questions. First, do the odds of being self-employed decline in the second and third generations? Second, do generational patterns in self-employment propensities vary by race? And finally, do race and generational status affect the odds of being self-employed in low-, medium-, and high-prestige industry sectors? Results offer some support for the assimilation perspective: Immigrants are generally more likely than third-generation groups to be self-employed with the exception of Asians, where second-generation Asians have the greatest odds of being self-employed. However, results also reveal that generational patterns in self-employment propensities vary by race and industry-sector prestige. Accordingly, first- and second-generation whites have the greatest odds of being self-employed (across all levels of industry-sector prestige), and third-generation whites are more likely than all generations of blacks and Hispanics to be engaged in high-prestige self-employment. These findings suggest that immigrants, their offspring, and native-born groups undergo a racialized incorporation in which self-employment is organized along hierarchical and racial lines associated with uneven levels of prestige.