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Analiza zależności pomiędzy indywidualnymi cechami jednostki a poczuciem bycia dyskryminowanym

100%
Grzenda W., Dorszyńska M.
Studia Ekonomiczne
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2017
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vol. 326
55-74
PL
Dyskryminacja społeczna jest problemem złożonym i może być rozważana w bardzo wielu aspektach. Nierówne traktowanie jest skutkiem posiadania przez jednostkę lub grupę osób określonej cechy lub cech. Celem niniejszego opracowania jest ustalenie wielkości i kierunku wpływu wybranych cech demograficznych oraz społeczno- ekonomicznych na poczucie bycia dyskryminowanym. Analizę tych cech przeprowadzono z wykorzystaniem modelu regresji logistycznej. Zgodnie z jej wynikami, istotny wpływ na poczucie bycia dyskryminowanym mają następujące cechy: wiek, religijność, płeć, pochodzenie, bycie w związku, poziom zadowolenia z życia, ocena sytuacji społeczno- ekonomicznej w kraju zamieszkania oraz kraj zamieszkania. Ponadto, z wykorzystaniem modelu klas ukrytych (LCA), zbadano wpływ poczucia bycia dyskryminowanym na posiadanie określonych opinii i poglądów.
EN
Social discrimination is a complex problem and can be considered in many aspects. Some features of the individuals or the group of people may cause unequal treatment. The primary objective of this study is the assessment of the scale and direction of the impact of selected demographic and socio-economic characteristics on the feeling of being discriminated. Logistic regression model have been used in this analysis. The paper reveals that the characteristics like age, religion, national origin, being in a relationship, the level of life satisfaction, the evaluation of socio-economic situation in the country and country of residence have a significant impact on the feeling of being discriminated. Moreover, latent class model (LCA) makes it possible to examine the impact of the feeling of being discriminated on the opinions and views expressed by the person.
2
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Artificial intelligence in predicting the bankruptcy of non-financial corporations

100%
Gavurova B., Jencova S., Bacik R., Miskufova M., Letkovsky S.
Oeconomia Copernicana
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2022
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vol. 13
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issue 4
1215-1251
EN
Research background: In a modern economy, full of complexities, ensuring a business' financial stability, and increasing its financial performance and competitiveness, has become especially difficult. Then, monitoring the company's financial situation and predicting its future development becomes important. Assessing the financial health of business entities using various models is an important area in not only scientific research, but also business practice. Purpose of the article: This study aims to predict the bankruptcy of companies in the engineering and automotive industries of the Slovak Republic using a multilayer neural network and logistic regression. Importantly, we develop a novel an early warning model for the Slovak engineering and automotive industries, which can be applied in countries with undeveloped capital markets. Methods: Data on the financial ratios of 2,384 companies were used. We used a logistic regression to analyse the data for the year 2019 and designed a logistic model. Meanwhile, the data for the years 2018 and 2019 were analysed using the neural network. In the prediction model, we analysed the predictive performance of several combinations of factors based on the industry sector, use of the scaling technique, activation function, and ratio of the sample distribution to the test and training parts. Findings & value added: The financial indicators ROS, QR, NWC/A, and PC/S reduce the likelihood of bankruptcy. Regarding the value of this work, we constructed an optimal network for the automotive and engineering industries using nine financial indicators on the input layer in combination with one hidden layer. Moreover, we developed a novel prediction model for bankruptcy using six of these indicators. Almost all sampled industries are privatised, and most companies are foreign owned. Hence, international companies as well as researchers can apply our models to understand their financial health and sustainability. Moreover, they can conduct comparative analyses of their own model with ours to reveal areas of model improvements.
3

Adoption and Benefits of Statistical Methods in Enterprises: Differences between Croatian Regions

100%
Žmuk B.
South East European Journal of Economics and Business
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2015
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vol. 10
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issue 1
55-65
EN
This paper aims to address differences in the use of statistical methods by enterprises as one of the factors leading to the uneven level of economic development between different regions. For research purposes, a web survey was conducted on a sample of 667 Croatian enterprises in 2013. In order to better distinguish between Croatian regions, a complex sample survey design was used. The results show that the highest rates of statistical methods use among enterprises are in the Central and East region (36.96%). The conducted logistic regression analysis showed that the enterprises that use statistical methods have 63.5% greater odds of achieving positive net income than enterprises that do not. The research results point out the need for the adoption of statistical methods as a tool for achieving higher net income and for reducing economic dissimilarities between regions.
4
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Determining the Impact of Residential Neighbourhood Crime on Housing Investment Using Logistic Regression

84%
Olajide S. E., Lizam M.
Path of Science
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2016
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vol. 2
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issue 12(17)
6.9-6.18
EN
This paper discusses the impact of criminal activities on residential property value. With regard to criminal activities, the paper emphasizes on the contribution of each component of property crime. One thousand (1000) sets of structured questionnaire were administered on the residents of residential estates within the South Western States of Nigeria out of which 467 were considered useable after the data screening. Purposive and systematic sampling techniques were used while logistic regression was used to determine the impact of each of the components of residential property crime on housing investment. The results showed the P-Values of 0.000, 0.322, 0.335, 0.545 and 0.992 for violent crime, incivilities and street crime, burglary and theft, vandalism and robbery respectively. However, the R2 which represents the generalisation of the impact of neighbourhood crime on housing investment was 44 % and aggregate P-value was 0.000. Using the Hosmer and Lemeshow (H-L) test of goodness of fit, the model had approximately 89% predictive probability which is considered excellent. This indicates that the alternative hypothesis is upheld that residential neighbourhood crime is capable of impacting on residential property value. The policy implication of this result is that no effort should be spared in combating residential neighbourhood crime in order to boost and encourage housing investment.
RU
В данной статье рассматривается влияние преступной деятельности на стоимость жилой недвижимости. Что касается преступной деятельности, в работе подчеркивается вклад каждого компонента имущественных преступлений. Одна тысяча (1000) комплектов структурированного вопросника были предложены жителям жилых комплексов в южно-западных штатах Нигерии, из которых 467 были признаны полезными после скрининга данных. Использовались целенаправленные и систематические методы выборки, в то время как логистическая регрессия была использована для определения влияния каждого из компонентов преступной деятельности, связанной с жилой недвижимостью на инвестиции в жилье. Результаты показали, P-Values 0,000, 0,322, 0,335, 0,545 и 0,992 для насильственных преступлений, неучтивости и уличной преступности, взломов и краж, вандализма и грабежа соответственно. Тем не менее, R2, который представляет собой обобщение влияния преступности на инвестиции в жилищное строительство, составил 44 %, а совокупное P-значение составило 0,000. С помощью теста Хосмера и Лемешова (H-L), теста на эффективность и достоверность, модель имела примерно 89% прогностической вероятности, что считается отличным результатом. Это указывает на то, что поддерживается альтернативная гипотеза, о том, что преступность жилого района способна воздействовать на стоимость жилой недвижимости. Политическое значение этого результата состоит в том, что никаких усилий не следует жалеть в борьбе с преступностью жилого микрорайона с целью увеличения и поощрения инвестиций в жилищное строительство.
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