Researcher:
Angın, Merih

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Merih

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Angın

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Angın, Merih

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2021 - 20236

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Researcher Of Publications: search.filters.isAuthorOfPublication.5231d084-1633-446f-a3e3-ff75cc537080

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Now showing 1 - 6 of 6
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    IMF: international migration fund
    (Taylor & Francis, 2023) Shehaj, Albana; Shin, Adrian J. J.; Department of International Relations; Angın, Merih; Faculty Member; Department of International Relations; College of Administrative Sciences and Economics; 308500
    Existing models of international organizations focus on the strategic and commercial interests of major shareholders to explain why some countries secure better deals from international organizations. Focusing on the International Monetary Fund (IMF), we argue that the Fund's major shareholders pressure the IMF to minimize short-term adjustment costs in the borrowing country when they host a large number of the country's nationals. Stringent loan packages often exacerbate short-term economic distress in the borrowing country, which in turn causes more people to migrate to countries where their co-ethnics reside. Analyzing all IMF programs from 1978 to 2014, we assess our hypothesis that IMF borrowers with larger diasporas in the major IMF shareholder countries tend to secure better arrangements from the IMF. Our findings show that when migration pressures on the G5 countries increase, borrowing countries receive larger loan disbursements and fewer conditions.Los modelos existentes de organizaciones internacionales se centran en los intereses estrategicos y comerciales de los principales accionistas para explicar por que algunos paises obtienen mejores tratos por parte de las organizaciones internacionales. Centrandonos en el Fondo Monetario Internacional (FMI), argumentamos que los principales accionistas del Fondo presionan al FMI para que minimice los costes de ajuste a corto plazo en el pais prestatario cuando acogen a un gran numero de ciudadanos de ese pais. Los severos paquetes de prestamos suelen exacerbar las dificultades economicas a corto plazo en el pais prestatario, lo que a su vez provoca que mas personas emigren a paises donde residen otros de sus compatriotas. Analizando todos los programas del FMI desde 1978 hasta 2014, evaluamos nuestra hipotesis de que los prestatarios del FMI con mayores diasporas en los principales paises accionistas del FMI tienden a obtener mejores acuerdos por parte del FMI. Nuestras conclusiones muestran que cuando aumentan las presiones migratorias en los paises del G5, los paises prestatarios reciben mayores desembolsos de prestamos y con menos condiciones.Les modeles actuels d'organisations internationales se focalisent sur les interets strategiques et commerciaux des actionnaires majoritaires pour expliquer pourquoi certains pays obtiennent de meilleurs accords aupres des organisations internationales. En nous concentrant sur le Fonds monetaire international (FMI), nous affirmons que ses actionnaires majoritaires appliquent une certaine pression pour reduire les couts d'ajustement a court terme du pays emprunteur, quand un grand nombre de ressortissants de ce pays vit chez eux. Les prets aux conditions strictes aggravent souvent la detresse economique a court terme dans le pays emprunteur. Cette situation renforce ensuite frequemment l'immigration vers les pays ou des compatriotes resident. Apres l'analyse de tous les programmes du FMI de 1978 a 2014, nous evaluons notre hypothese : quand les emprunteurs disposent d'une diaspora plus importante dans les pays actionnaires majoritaires du fonds, ils obtiennent de meilleurs accords aupres du FMI. Nos resultats montrent que lorsque la pression migratoire sur les pays du G5 s'accroit, les pays emprunteurs recoivent des versements de pret plus importants et sont soumis a moins de conditions.
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    Inside job: migration and distributive politics in the European Union
    (Wiley, 2021) Shehaj, Albana; Shin, Adrian J.; Department of International Relations; Angın, Merih; Faculty Member; Department of International Relations; College of Administrative Sciences and Economics; 308500
    Migration has become a top policy priority of the European Union (EU) in the wake of the 2015 migrant crisis. Given the significant ramifications of non-European immigration for its member states, the EU has implemented a variety of policies to minimize popular backlashes within the borders of its wealthiest member states, which are also popular final destinations for migrants. In this article, we show that the EU offers financial incentives to its migrant-transit mem-ber countries in exchange for holding migrants traveling from the Middle East and North Africa region within their territories. We use a subnational dataset on Southern Italy to examine the effects of migrant arrivals by boat on the amount of the European Regional Development Fund and the European Social Fund received by each autonomous region between 2006 and 2018. In addition, we provide a cross-national analysis of EU expenditures using data on unauthorized border crossings into the EU between 2009 and 2018. We find robust empirical support for the argu-ment that the EU channels more funds to jurisdictions lo-cated on the major migrant-transit routes.
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    A RoBERTa approach for automated processing of sustainability reports
    (Mdpi, 2022) Tasdemir, Beyza; Yilmaz, Cenk Arda; Demiralp, Goekcan; Atay, Mert; Angin, Pelin; Dikmener, Gokhan; Department of International Relations; Angın, Merih; Faculty Member; Department of International Relations; College of Administrative Sciences and Economics; 308500
    There is a strong need and demand from the United Nations, public institutions, and the private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by community volunteers. Results demonstrate that especially fine-tuned RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.
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    Sentiment and context-refined word embeddings for sentiment analysis
    (IEEE, 2021) Deniz, Ayca; Angin, Pelin; Department of International Relations; Angın, Merih; Faculty Member; Department of International Relations; College of Administrative Sciences and Economics; 308500
    Word embeddings have become the de-facto tool for representing text in natural language processing (NLP) tasks, as they can capture semantic and syntactic relations, unlike their precedents such as Bag-of-Words. Although word embeddings have been employed in various studies in recent years and proven to be effective in many NLP tasks, they are still immature for sentiment analysis, as they suffer from insufficient sentiment information. General word embedding models pre-trained on large corpora with methods such as Word2Vec or GloVe achieve limited success in domain-specific NLP tasks. On the other hand, training domain-specific word embeddings from scratch requires a high amount of data and computation power. In this work, we target both shortcomings of pre-trained word embeddings to boost the performance of domain-specific sentiment analysis tasks. We propose a model that refines pre-trained word embeddings with context information and leverages the sentiment scores of sentences obtained from a lexicon-based method to further improve performance. Experiment results on two benchmark datasets show that the proposed method significantly increases the accuracy of sentiment classification.
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    Understanding IMF decision-making with sentiment analysis
    (Institute of Electrical and Electronics Engineers Inc., 2022) Deniz, Ayça; Angın, Pelin; Department of International Relations; Angın, Merih; Faculty Member; Department of International Relations; College of Administrative Sciences and Economics; 308500
    With the advances in information technologies, the amount of available data on web sources where people express their opinions increases continually. Sentiment analysis is one of the effective tools for decision-makers to gain insights from massive heaps of data. The field of International Organizations, which produces big data in the form of large documents, has significant potential to benefit from sentiment analysis in decision-making. In this paper, we evaluate the effectiveness of different sentiment analysis tools in classifying the sentiments of the International Monetary Fund's (IMF) Executive Board members regarding the design of IMF programs. We introduce a novel dataset, Executive Board meeting minutes of the IMF, in which the sentences are labelled as positive, neutral, or negative. The experimental results demonstrate that sentiment classification with state-of-the-art language models yields high performance on this dataset when trained with domain-specific data.
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    PublicationOpen Access
    Evolutionary multiobjective feature selection for sentiment analysis
    (Institute of Electrical and Electronics Engineers (IEEE), 2021) Pelin Angın; Deniz, Ayça; Department of International Relations; Angın, Merih; Faculty Member; Department of International Relations; College of Administrative Sciences and Economics; 308500
    Sentiment analysis is one of the prominent research areas in data mining and knowledge discovery, which has proven to be an effective technique for monitoring public opinion. The big data era with a high volume of data generated by a variety of sources has provided enhanced opportunities for utilizing sentiment analysis in various domains. In order to take best advantage of the high volume of data for accurate sentiment analysis, it is essential to clean the data before the analysis, as irrelevant or redundant data will hinder extracting valuable information. In this paper, we propose a hybrid feature selection algorithm to improve the performance of sentiment analysis tasks. Our proposed sentiment analysis approach builds a binary classification model based on two feature selection techniques: an entropy-based metric and an evolutionary algorithm. We have performed comprehensive experiments in two different domains using a benchmark dataset, Stanford Sentiment Treebank, and a real-world dataset we have created based on World Health Organization (WHO) public speeches regarding COVID-19. The proposed feature selection model is shown to achieve significant performance improvements in both datasets, increasing classification accuracy for all utilized machine learning and text representation technique combinations. Moreover, it achieves over 70% reduction in feature size, which provides efficiency in computation time and space.