Społeczno-ekonomiczne i środowiskowe determinanty zdrowia: przypadek gospodarek w okresie przejściowym

This study examines the effects of economic and socio-demographic factors on the health status of men and women separately. The annual data of 16 selected transition countries for the period 2000-2016 were used. Life expectancy at birth was used as an indicator of health status in the study. Economic and environmental variables such as GDP per capita, health expenditures, unemployment, carbon emissions, access to safe water, and urbanization are considered as factors affecting life expectancy at birth. In the study, the Autoregressive Distributed Lags (ARDL) model was used. The findings show that the effects of socioeconomic and environmental factors on life expectancy differ according to men and women. It has been found that above-mentioned factors are more effective on life expectancy of men than women in selected transition economies. Therefore, it can be recommended to prioritize economic and environmental targets in improving the health outcomes of countries.W artykule przeanalizowano wpływ czynników ekonomicznych i społeczno-demograficznych na stan zdrowia kobiet i mężczyzn. Wykorzystano dane z 16 wybranych krajów reprezentujących gospodarki w okresie przejściowym za lata 2000-2016. W badaniu jako wskaźnik stanu zdrowia wykorzystano oczekiwaną długość życia w chwili urodzenia. Za czynniki wpływające na oczekiwaną długość życia w chwili urodzenia są uważane zmienne gospodarcze i środowiskowe, takie jak PKB na mieszkańca, wydatki na zdrowie, bezrobocie, emisje dwutlenku węgla, dostęp do czystej wody i urbanizacja. W badaniu wykorzystano model Autoregressive Distributed Lags (ARDL). Okazuje się, że wpływ czynników społeczno-ekonomicznych i środowiskowych na oczekiwaną długość życia różni się w zależności od płci. Stwierdzono, że wyżej wymienione czynniki wpływają bardziej na długość życia mężczyzn niż kobiet w wybranych gospodarkach w okresie przejściowym. Dlatego należy zalecić priorytetowe potraktowanie celów ekonomicznych i środowiskowych w poprawie wyników zdrowotnych krajów

An Econometric Analysis of Determinants of House Rents in Istanbul

In this study, the hedonic pricing model, demonstrating the relationship between residential properties and housing rentals and based on revealed preference, has been estimated by the spatial quantile regression method. The data set collected for each housing rental consists of cross-sectional data of the variables such as housing size, number of bathrooms, housing age, distance dummies etc. Additionally, spatial factor providing to measure the direction and intensity of the externality and interaction between locations, have also been added to the model. The findings show that the spatial effect is a statistically significant and it has a positive impact on housing rental prices. Proximity to the Trans European Motorway has a diminishing effect on housing rents while housing size, multiple bathrooms, appliances and security presence, ease of access to an important means of transport such as metro increase them. Although housing and heating type are the factors that could affect housing prices, a remarkable point is that these two factors is not effective on housing rents. The results of the study provide important clues about the functioning of the real estate market in Istanbul. Keywords: housing rents, hedonic pricing model, quantile regressio

The Perceptual Maps of the Regions in Turkey Where Mass Tourism is Implemented in Terms of Sustainable Tourism

Tourism is an important fact for developed and developing countries due to its economic, cultural and environmental effects. The developments in the tourism sector have positive effects such as increasing the employment and national income; but also some negative effects when we consider from social, cultural and environmental dimensions. Such negative effects are more frequently seen in regions where mass tourism is implemented. This study used the data of the years between 2001 and 2012 and the multi-dimension scaling method for Antalya, Mugla, Izmir, Aydin and Nevsehir where mass tourism is implemented was used to draw perceptual maps and the changes within 10 years was examined. The analysis has used data such as tourism incomes, number of tourists, number of facilities, number of beds, tourism investments, total employment, green areas, forestry, buildings, solid waste and water consumption. According to the date of eleven variables used in two-dimensional perceptual maps, the dimensions are set as economic and environmental dimensions.  It has been seen that in terms of economy, Antalya was in a better position than other cities in 2011 and Izmir was in a positive location in terms of environment.  In 2012, it has been determined that Aydin has shown a progress at environmental dimension. Keywords: Tourism, Sustainable Tourism, Mass Tourism, Multidimensional Scaling Jel Codes: C39, L8

The effects of education and experience on youth employee wages: the case of Turkey

The aim of this study is to reduce the disadvantages experienced by young Turkish employees, such as age discrimination, by analysing their wage structure and the factors that could affect their earnings. This study could fill the gaps in the literature on youth employee wages in the Turkish labour force. Using the 2018 Household Budget Survey data, this study addresses five research questions by estimating the extended Mincer wage equation with robust estimators to respond to the research questions. The findings show that postgraduate and bachelor\"s degrees have a high incremental effect on wages and the wage gaps between the degrees are wide. Each added year of experience impacts wages because employers prefer more experienced employees to avoid the cost of training them. Young female employees earn less than young male employees because of occupational segregation, motherhood penalty, and gender norms. Due to the lack of opportunities for part-time jobs in the Turkish labour force, there is a wide gap between the wages for full-time and part-time jobs. This study contributes to a better understanding of young employees\" wage structure with robust-to-outliers econometric analysis and may guide to develop techniques to reduce the disadvantages for young Turkish individuals in the labour market

The impact of Stock index futures on the Turkish spot market

The aim of this work is to investigate the impact of the introduction of index futures on the volatility of the underlying Turkish spot market. For this purpose, symmetric and asymmetric conditional-volatility models have been employed by using the Istanbul Stock Exchange 30 Index (ISE30) daily returns. The evidences indicate that there have been significant changes in the structure of volatility in the ISE30 spot market, following the onset of futures trading. It has also been found that the asymmetric effect is relevant in the post-futures period

Identifying the systemically important banks of Turkey with the CoVaR method

The purpose of this paper is to measure the systemic risk contributions of Turkish banks and to identify the systemically important banks of Turkey during the period from 2005 to 2016. We apply the conditional value-at-risk (CoVaR) method proposed by Adrian and Brunnermeier (2009) using quantile regression. The study includes thirteen major banks of Turkey, including both public and private banks, out of a total of 52 banks. The banks are ranked in terms of their systemic risk contribution to the Turkish financial system based on their asset returns, macroeconomic variables and individual bank variables. The study reveals that Akbank, Garanti, Yapi Kredi and Isbank have the highest systemic risk contribution to the financial system when adding macroeconomic variables to the model. This ranking is changed to Yapi Kredi, Garanti, TEB, Sekerbank and Akbank when taking into account bank-specific variables. One surprising result is that risk in isolation and the spillover risks of public banks are smaller than in large private banks. Furthermore, the marginal systemic risk contributions of public banks are smaller than those of private banks. In conclusion, authorities improve the regulatory framework according to the context of CoVaR in addition to monitor the idiosyncratic risks of banks

Socioeconomic and Environmental Determinants of Health Outcomes: The Case of Transition Economies

This study examines the effects of economic and socio-demographic factors on the health status of men and women separately. The annual data of 16 selected transition countries for the period 2000-2016 were used. Life expectancy at birth was used as an indicator of health status in the study. Economic and environmental variables such as GDP per capita, health expenditures, unemployment, carbon emissions, access to safe water, and urbanization are considered as factors affecting life expectancy at birth. In the study, the Autoregressive Distributed Lags (ARDL) model was used. The findings show that the effects of socioeconomic and environmental factors on life expectancy differ according to men and women. It has been found that above-mentioned factors are more effective on life expectancy of men than women in selected transition economies. Therefore, it can be recommended to prioritize economic and environmental targets in improving the health outcomes of countries

Microeconometric Analysis of Household Consumption Expenditures: LAD-LASSO Method

Bu çalışmanın amacı, denetimli makine öğrenmesi yöntemlerinin aşırı değer ve uzun kuyruklu hatalara sahip HanehalkıBütçe Anketi Hane veri setinin ilgili değişkenlerini seçmemize nasıl yardımcı olduğunu incelemek ve Türkiye’nin HanehalkıTüketimHarcamaları’nın tahmininde en iyitahmin ve öngörü performansına sahip olanmodelin belirlenmesinisağlamaktır.Bu amaçla, 2018 yılı Türkiye’nin Hanehalkı Bütçe Anketi Hane veri seti klasik regresyon yönteminin yanı sıra En KüçükMutlak Sapma (LAD), En Küçük Mutlak Küçültme ve Seçim Operatörü (LASSO) ve LAD-LASSO yöntemleri kullanılarakincelenmiş ve yöntemlerin tahmin ve öngörü performansları karşılaştırılmıştır. Analiz sonuçlarına göre; uzun kuyrukluhataların varlığında dayanıklı tahminciler elde edilirken aynı zamanda değişken seçimine olanak sağlayan LAD-LASSOmakine öğrenmesi yönteminin tahmin performansı ve öngörü açıklığı açısından en başarılı yöntem olduğu sonucunaulaşılmıştır. Ayrıca gelir, tasarruf ve hane halkı büyüklüğü gibi bazı temel değişkenler tüm modeller için hanehalkı tüketimharcamalarını artırmaktadır. Bu değişkenlere ek olarak odanın yapısı, mutfak, banyo zeminleri, ısıtma, klima tercihleri,kullanılan enerji kaynakları, müstakil ev, apartman, yazlık, bağ sahipliği ve yatırım tercihleri, kredi kartı kullanımı, internetalışveriş alışkanlıkları gibi çeşitli değişkenler LAD-LASSO modelinde hane halkı tüketim harcamalarının belirleyicileriolarak seçilmiştir. Çalışma sonuçlarından, makine öğrenme algoritmalarının mikroekonometrik modellerin oluşturulmasısırasında gerekli değişkenlerin seçiminde kullanılabileceğine dair bulgular elde edilmiştir. Bu çalışma doktora tezindenüretilmiştir.This study examined how supervised machine learning methods help us select the relevant variables of a Household_x000D_ Budget Survey Consumption Expenditures dataset with outliers in order to achieve better performance in the predicting_x000D_ and forecasting of the Household Consumption Expenditures Model. To achieve this, the Household Budget Survey_x000D_ Consumption Expenditures dataset of Turkey for 2018 was examined using the Least Absolute Deviation (LAD), Least_x000D_ Absolute Shrinkage and Selection Operator (LASSO) and LAD-LASSO methods. In addition, the classical regression method_x000D_ and the prediction and forecasting performances of the methods were compared. According to the analyzed results, it was concluded that the LAD-LASSO machine learning method, which enables the selection of variables_x000D_ while obtaining robust predictors in the presence of long-tailed errors, was the most successful method in_x000D_ prediction performance and forecasting accuracy. Additionally, several fundamental variables such as income,_x000D_ saving, and household size increase the household consumption expenditures for all models. In addition to_x000D_ these variables, other variables including the structure of a room, the kitchen, bathroom floors, heating, air_x000D_ conditioning preferences, energy sources used, detached house, apartment, cottage, vineyard ownership,_x000D_ investment preferences, credit card usage, and internet shopping habits were selected as determinants of_x000D_ household consumption expendituresin the LAD-LASSO model. From the results of the study, it wasfound that_x000D_ machine learning algorithms can be used in the selection of the most appropriate variablesin the course of the_x000D_ construction of microeconometric models