Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 1Citation - Scopus: 1Unhappiness Among the Unemployed: The Roles of Descriptive Norms, Injunctive Norms and Personal Beliefs(Wiley, 2025-05-19) Ugur, Zeynep B.; Durak, AysenurThis study explores the influence of social norms and individual beliefs on the well-being of unemployed individuals in Turkey, a context marked by both chronic unemployment and a high societal valuation of employment. Using province-level representative data from the 2013 Life Satisfaction Survey, encompassing 196,203 observations, we analyse how descriptive norms (prevalence of unemployment) and injunctive norms (social pressures due to unemployment) at the province level affect the happiness of the unemployed. We utilized people's perception of employment for being respected in social life and personally feeling social pressure as a measure of individual beliefs. Multilevel regression results reveal that descriptive norms can modestly alleviate the adverse impact of unemployment, particularly for the short-term unemployed, while injunctive norms slightly intensify the unhappiness of being unemployed, especially in the short term. The unemployed's personal beliefs about the value of employment matter for their happiness. These findings underscore the theoretical implications of social norms in shaping the well-being of the unemployed and highlight the importance of individual beliefs in moderating these effects.Article Citation - WoS: 5Citation - Scopus: 14Forecasting of the Unemployment Rate in Turkey: Comparison of the Machine Learning Models(MDPI, 2024-07-30) Guler, Mehmet; Kabakci, Aysil; Koc, Omer; Eraslan, Ersin; Derin, K. Hakan; Guler, Mustafa; Namli, ErsinUnemployment is the most important problem that countries need to solve in their economic development plans. The uncontrolled growth and unpredictability of unemployment are some of the biggest obstacles to economic development. Considering the benefits of technology to human life, the use of artificial intelligence is extremely important for a stable economic policy. This study aims to use machine learning methods to forecast unemployment rates in Turkey on a monthly basis. For this purpose, two different models are created. In the first model, monthly unemployment data obtained from TURKSTAT for the period between 2005 and 2023 are trained with Artificial Neural Networks (ANN) and Support Vector Machine (SVM) algorithms. The second model, which includes additional economic parameters such as inflation, exchange rate, and labor force data, is modeled with the XGBoost algorithm in addition to ANN and SVM models. The forecasting performance of both models is evaluated using various performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings of the study show how successful artificial intelligence methods are in forecasting economic developments and that these methods can be used in macroeconomic studies. They also highlight the effects of economic parameters such as exchange rates, inflation, and labor force on unemployment and reveal the potential of these methods to support economic decisions. As a result, this study shows that modeling and forecasting different parameter values during periods of economic uncertainty are possible with artificial intelligence technology.