How early should labor market exposure start? Internships become an essential element of numerous school and university curricula, justified with an anticipated improvement in later occupational choices and job placements. For high school students there is, however, little evidence that short-term impacts are positive. This study evaluates a unique youth labor market pilot initiative. Over 50 companies have been mobilized to host high-school students who were randomly selected for firm visits. During then received on-the-job introductory trainings on work and hiring practices. Eventually, students’ subjective beliefs were updated. Expectations of entry level wages dropped by over 12% as compared to the control group. The company- student match was random within geographical proximity, allowing to rule out further self-selection into firm choices and estimating sector-specific effects. Students adjusted their occupational preferences, self- perception, and grades in line with the labor market requirements. Role models and parental background play an important role for female students’ occupational preferences, who are also less likely to dropout of school. The study adds to a literature stressing that when information interventions are implemented at an early yet very decisive age, the average impacts on beliefs and educational performance can be considerable.
On average, firstborns complete more education than their laterborn siblings. We study whether this effect is amplified by genetic endowments. Our family-fixed effects approach allows us to exploit exogenous variation in birth order and genetic endowments among 15,019 siblings in the UK Biobank. We find that those with higher genetic endowments benefit disproportionally more from being firstborn compared to those with lower genetic endowments, providing a clean example of how nature and nurture interact in producing skills. Moreover, since parental investments are a dominant channel driving birth order effects, our results are consistent with dynamic complementarity in skill formation.
I investigate whether differences in macroeconomic experiences during formative years of individuals affect their entrepreneurial behavior throughout their careers. I find that living in an area with high unemployment during an individual’s formative years decreases the propensity of an individual to start a business throughout their entire career history. I distinguish between two types of businesses: incorporated) and unincorporated. Using panel data, I find that those individuals who reside in a region that experience a larger shock to unemployment during their formative years significantly decrease the propensity to start an incorporated enterprise throughout their entire career. Consistent with the prior literature, I find that experiencing a macroeconomic shock during adolescence also significantly increases the risk-aversion of an individual. However, the observed decrease in entrepreneurial proclivity of young adults who experienced an economic shock during adolescence is not fully explained by increased risk-aversion.
In this paper, we analyzed whether the level of credit information sharing among lenders, the strength of creditor rights protection and efficiency of court enforcement stimulates bank lending with particular interest to the recent global financial crisis. Using data for the period 2004-2013, our results indicate that the Depth of credit information and Private credit bureau have a positive and significant impact on bank lending rates. Compared to the information sharing indicators, Creditor rights protection does not significantly impact to bank lending. However, the results for the efficiency of the judicial system indicate that Court enforcement has a significant impact on bank lending policy in both normal and crisis periods.
The role of misallocation in the relationship between trade and income inequality
Abstract: This paper introduces a new factor that mediates the impact of trade on income inequality within countries. In a sample of 18 European countries over the period 1999-2016, I find that the effect of trade openness on income distribution is conditional on the existing patterns of resource allocation. In case of an efficient allocation of resources within a country, more trade reduces income inequality. Under conditions of misallocation, however, the inequality-reducing effect of trade is weakened—and may even be reversed when misallocation is sufficiently high—albeit such countries tend to have lower income inequality, other things being equal.
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May God Give Sons to Everyone: Gender-Biased Fertility Patterns in Pakistan.
Abstract: We examine the prevalence and strength of the phenomenon of son preference and its effects on Pakistani women’s fertility. Using data from two representative Demographic and Health Surveys, we come up with strong evidence for both the revealed and stated preference for male offspring. While the likelihood of second birth does not vary with the sex of the first-born, women with one or more sons are upto 17% less likely to pursue higher-order births compared with women with no sons. Besides, parents with one or more sons are upto 35% less likely to state the desire to have another child.
Abbos Utkirov, Learning Resource Centre, Westminster International University in Tashkent
November 16, 2020
Abstract: This paper investigates the impact of the library activities (LA) on the labor market outcome (LMO) of WIUT graduates. WIUT library provides a learning environment that helps students to create a practical team and individual projects to support the agenda of employability. LRC activities such as Guest lectures, Reading Clubs, Embedded sessions, Information, and digital Literacy training, group and individual projects, team works were promoted as an employability attribute. The research highlights the benefits of LRC activities and working in collaboration and with students and effect services beyond the Library in the employability arena.
Background materials:
https://www.libraryassessment.org/program/2020-posters/
https://www.libraryassessment.org/wp-content/uploads/2020/10/66-Utkirov-Library-Activities.pdf
Join our 5th Virtual Research Seminar on Monday, November 9, 2020 at 16:15 Tashkent time.
Subair Ali, Computing Department, Westminster International University in Tashkent, will present “A Comparative Analysis of Various Machine Learning Algorithms on Student Performance”.
Abstract: The machine learning algorithm could then be able to predict the performance of students based on the trust parents have placed in the university. The aim of this study is to do comparative analysis of the best in machine learning algorithms. Two experiments have been conducted with nine machine learning algorithms such as RandomForestClassifier, AdaBoostClassifier, ExtraTreesClassifier, KNeighborsClassifier, DecisionTreeClassifier, ExtraTreeClassifier, LogisticRegression, GaussianNB and BernoulliNB. By using get_dummies we were able to get the best result in the second experiment more than the first experiment. The comparative analyses results demonstrated that the LogisticRegression (LR), AdaBoostClassifier (ABC) and DecisionTreeClassifier (DTC) performed 100% accuracy result than other machine learning algorithms.
Join the seminar, link for participation:
https://us02web.zoom.us/j/87292836734?pwd=K283dXFCVXJ3RzRyNkRqNGM2R0d3dz09
Meeting ID: 872 9283 6734
Passcode: 012783
BIO: Subair Ali has been in teaching industry for more than 15 years. He is currently Senior Lecturer at Computing Department at WIUT. Subair Ali is pursuing his PhD in Information Technology from India. He completed B.Sc. (Computer Science) from Jamal Mohamed College, India; M.Sc. (Information Technology) from Alagappa University, India; M.Phil. (Computer Science) from Madurai Kamaraj University, India; ETE (Education Technology and E-Learning) from Teeside University, London; PGCHE (Post Graduate Certificate in Highre Education) from Botho University, Botswana. His research interests focus on Machine Learning, IoT and Data Mining.
Research Seminars are a place where faculty and research assistants share completed research and work in progress. They receive valuable feedback to strengthen their research preparing them for conference presentations and publications. On occasion, visiting researchers from outside the University share their work. Research Seminars are open to public.
Faculty and research assistants willing to present their research at WIUT Research Seminar Series, please contact Akhtem Useinov, Senior Officer on Research Development, at
Nodira Nazyrova, Computing Department, Westminster International University in Tashkent
“A New Machine Learning Modelling Approach for Patients' Mortality Prediction in Hospital Intensive Care Unit”
Abstract: Machine Learning (ML) is a powerful tool in predictive modelling but subject to the problem of class imbalance. In this study, we tackle class imbalance with combining new features, data re-sampling, ensemble learning and an appropriate selection of evaluation metrics in a clinical setting. We built and evaluated 126 ML models to predict mortality in 48546 ICU admissions extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) repository. Our approach has a considerable impact on the classification; it resulted in an improvement in the mortality status prediction. For evaluation, we implement a comparative multi-stage evaluation filter for a binary classification to compare all models. The best models are identified. The Area Under Receiver Operator Characteristic curves of the tested models range from 0.57 to 0.94. These encouraging results can guide further development of models to allow for more reliable ICU mortality predictions.
This paper is a joint WIUT-UoW Research Collaboration project coauthored by Nodira Nazyrova, Ikboljon Sobirov and Dr. Abdumalik Djumanov (Computing Department, WIUT) and Mahmoud Aldraimli and Prof. Thierry Chaussalet (the School of Computer Science and Engineering and the Health Innovation Ecosystem (HIE) at the University of Westminster, London). Recently the paper was presented at the International Symposium on Bioinformatics and Biomedicine (BioInfoMed'2020) and received the Best Oral Presentation Award.
BIO: Nodira Nazyrova has been joining WIUT since 2015, and currently is the module leader for ‘Machine Learning and Data Analytics’ and ‘Business Intelligence’ at Computing Department. Her research interests focus on Machine Learning. Nodira Nazyrova is WIUT alumnus (BSc Business Information Systems, Class of 2012). She received her Master of Science degree in Computer Science from TH Koln, Germany.
Research Seminars are a place where faculty and research assistants share completed research and work in progress. They receive valuable feedback to strengthen their research preparing them for conference presentations and publications. On occasion, visiting researchers from outside the University share their work. Research Seminars are open to public.
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Dr. Andrey Artemenkov
“Reconstructing the income approach theory of asset valuation based on the Transactional Asset Pricing Approach (TAPA)”.
Abstract: TAPA is a novel analytical valuation methodology recasting the traditional derivations of the income approach techniques, including DCF, from a transactional perspective based on the principle of inter-temporal transactional equity, instead of the conventional investor-specific view originating from Irving Fisher or lack of arbitrate argument developed by Modigliani and Miller. Unlike CAPM, TAPA is explicitly a multi-period asset pricing model, allowing for the use of time-variable discount rates under DCF. Novel justifications for the DCF approach, the direct income capitalization and the Gordon model will be presented. For researchers of market cycles, the TAPA view can provide a powerful tool for studying the impact of market cycles on asset pricing.
