Hayat Dışı Sigorta Şirketlerinde İstihdam Üzerine Bir Çalışma: Bulanık Regresyon Örneği

Yıl 2022, Cilt: 6 Sayı: 2, 81 - 95, 12.10.2022

Yusuf Akgül Ahmet Şengönül Fuat Çamlıbel

Öz

incelenmiştir. Bu faktörler sigorta şirketlerinin mali değişkenleri olup, mali kar-zarar, net prim toplamı, toplam varlıklar ve teknik kar-zarar dır
Yöntem: Çözüm yöntemi olarak bulanık regresyon yöntemi kullanılmıştır.
Bulgular: Elde edilen sonuçlara göre bulanıklık seviyesi h=09 de mali değişkenlerin değişim aralığı anlamlı bulunmuştur. Sonuç olarak toplam varlıkların değişim aralığı %0,0906, mali kar-zarar %0,0002, prim toplamı % 0,6204ve teknik kar-zarar % 0,0392çıkmıştır. Ayrıca gerçek istihdam verileri üst regresyon sınırına yakın olduğu tespit edilmiştir.
Sonuç ve Katkılar: Bulanık regresyon yöntemiyle elde edilen sonuçlar, tahminlerin tutarlılığı açısından panel veri çözüm yönteminden oldukça iyidir. Sektördeki firmaların finansal değişkenleri ile istihdamı için tahmin modelleri oluşturulmak istendiğinde, bulanık regresyon yöntemi anlamlı modeller oluşturmada iyidir ve ilgili argümanların katsayıları hakkında daha tutarlı bilgi verir. Çalışmanın sonuçları ekonomik ve sosyal olarak yorumlanacak olursa; sigorta şirketlerinin istihdam kapasitesini etkilediği düşünülen içsel değişkenlerin sigorta şirketlerinde istihdamı nasıl etkilediği gözlemlenmiştir. Sigorta şirketlerinde prim kazancı büyüklüğündeki artışın istihdamı olumlu etkilediği görülmektedir. Bu etki, sigortacılık sektöründe yeni istihdam yaratılmasına olumlu yansıyacaktır. İstihdamdaki bu artış ülke ekonomisine de olumlu yansıyacaktır.
Sınırlılıklar: Ancak gelişen teknoloji ve birçok sigorta işleminin online olarak yapılması, prim kazanç büyüklükleri artsa bile şirketlerin istihdam kapasitelerinin azalmasına neden olacaktır. Örneğin ihtiyaç duyulan saha satış personeli ve acente sayısında azalma olacaktır. Bu durumda tam tersine sigorta sektöründe ve ülkede işsiz sayısı artacaktır. İstihdamın azalması ve işsizliğin artması hem insanları hem de ülke ekonomisini olumsuz etkileyecektir.

Anahtar Kelimeler

Sigortacılık, Bulanık Regresyon, İstihdam, Sigorta Şirketi

A Study on Employment in Non-Life Insurance Companies: Fuzzy Regression Example

Öz

Purpose: In this study, factors affecting employment in non - life insurance companies were examined. These factors are the financial variables of insurance companies, including financial profit-loss, total net premiums, total assets and technical profit-loss.
Methodology: Fuzzy regression method was used as the solution method.
Findings: According to the results, the change interval of the financial variables was found significant at h = 09. As a result, the change interval in total assets was % 0.0906, the financial profit was nearly “0”, technical profit/loss was % 0.0392 and the sum of premiums was % 62,04. Also, real employment data was found to be closer to the upper regression limit.
Implications: The results obtained by the fuzzy regression method are quite better from the panel data solution method in terms of the consistency of the estimates. When it is desired to generate prediction models for the employment with financial variables of the companies in the sector, the fuzzy regression method is good at creating meaningful models and gives more consistent information about the coefficient of the related arguments. If the results of the study will be interpreted economically and socially; it was observed how internal variables, which are thought to affect the employment capacity of insurance companies, affect employment in insurance companies. It is observed that the increase in the size of premium gain in insurance companies has a positive effect on its employment. This effect will have positive effects on creating new employment in the insurance sector. This increase in employment will also have positive effects for the country's economy.
Limitations: However, developing technology and making many insurance transactions online will cause companies to decrease their employment capacity even if their premium gain size increases. For example, there will be a decrease in needed field sales staff and number of agencies. In this case, on the contrary, it will increase the number of unemployed people in insurance sector and in the country. The decrease in employment and increasing unemployment will affect both people and the country's economy negatively. Economically, a negative outlook will occur in the domestic and foreign markets.

Anahtar Kelimeler

Insurance, Fuzzy Regression, Employment, İnsurance Company

Kaynakça

• Azadeh, A., Saberi, M., Asadzadeh, S. M., & Khakestani, M. (2011). A hybrid fuzzy mathematical programming-design of experiment framework for improvement of energy consumption estimation with small data sets and uncertainty: The cases of USA, Canada, Singapore, Pakistan and Iran. Energy, 36 (12), 6981-6992.
• Berry-Stölzle, T. R., Koissi, M. C., & Shapiro, A. F. (2010). Detecting fuzzy relationships in regression models: The case of insurer solvency surveillance in Germany. Insurance: Mathematics and Economics, 46 (3), 554-567.
• Chandrapala, P., & Knápková, A. (2013). Firm-specific factors and financial performance of firms in the Czech Republic. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis.
• Chang, Y. H. O., & Ayyub, B. M. (2001). Fuzzy regression methods–a comparative assessment. Fuzzy sets and systems, 119 (2), 187-203.
• Chen, L. H., Hsueh, C. C., & Chang, C. J. (2013). A two-stage approach for formulating fuzzy regression models. Knowledge-Based Systems, 52, 302-310.
• D’urso, P., Massari, R., & Santoro, A. (2011). Robust fuzzy regression analysis. Information Sciences, 181 (19), 4154-4174.
• David Cummins, J., & Derrig, R. A. (1997). Fuzzy financial pricing of property-liability insurance. North American Actuarial Journal, 1 (4), 21-40.
• De Andrés-Sánchez, J. (2007). Claim reserving with fuzzy regression and Taylor’s geometric separation method. Insurance: Mathematics and Economics, 40 (1), 145-163.
• De Andrés Sánchez, J. (2006). Calculating insurance claim reserves with fuzzy regression. Fuzzy Sets and Systems, 157 (23), 3091-3108.
• De Andrés-Sánchez, J. (2012). Claim reserving with fuzzy regression and the two ways of ANOVA. Applied Soft Computing, 12 (8), 2435-2441.
• Hunjra, A. I., Chani, D., Irfan, M., Javed, S., Naeem, S., & Shahzad Ijaz, M. (2014). Impact of micro economic variables on firms performance. International Journal of Economics and Empirical Research, 2(2), 65-73.
• Khashei, M., Hejazi, S. R., & Bijari, M. (2008). A new hybrid artificial neural networks and fuzzy regression model for time series forecasting. Fuzzy sets and systems, 159 (7), 769-786.
• Modarres, M., Nasrabadi, E., & Nasrabadi, M. M. (2005). Fuzzy linear regression models with least square errors. Applied Mathematics and Computation, 163 (2), 977-989.
• Mousavi, S. J., Ponnambalam, K., & Karray, F. (2007). Inferring operating rules for reservoir operations using fuzzy regression and ANFIS. Fuzzy Sets and Systems, 158 (10), 1064-1082.
• Nasrabadi, M. M., Nasrabadi, E., & Nasrabady, A. R. (2005). Fuzzy linear regression analysis: a multi-objective programming approach. Applied Mathematics and Computation, 163 (1), 245-251.
• Pehlivan, N. Y., Paksoy, T., & Chang, C. T. (2010). An Alternative Method for Fuzzy Regression&58; Fuzzy Radial Basis Function Network. International Journal of Lean Thinking, 1 (1), 1-15.
• Ramli, A. A., Watada, J., & Pedrycz, W. (2011). Real-time fuzzy regression analysis: A convex hull approach. European Journal of Operational Research, 210(3), 606-617.
• Rumler, F., & Waschiczek, W. (2010). The impact of economic factors on bank profits. Monetary Policy & the Economy, 4, 49-67.
• Shapiro, A. F. (2004). Fuzzy logic in insurance. Insurance: Mathematics and Economics, 35 (2), 399-424.
• Tran, L., & Duckstein, L. (2002). Multiobjective fuzzy regression with central tendency and possibilistic properties. Fuzzy Sets and Systems, 130 (1), 21-31.
• Wang, H. F., & Tsaur, R. C. (2000). Bicriteria variable selection in a fuzzy regression equation. Computers & Mathematics with Applications, 40(6-7), 877-883.
• Xue, Y., Kim, I. S., Son, J. S., Park, C. E., Kim, H. H., Sung, B. S., ... & Kang, B. Y. (2005). Fuzzy regression method for prediction and control the bead width in the robotic arc-welding process. Journal of Materials Processing Technology, 164, 1134-1139.
• Turkish Commercial Code, (2011, 13 January). Official Gazette (Issue No: 27846). Access Address: http://www.resmigazete.gov.tr/eskiler/2011/02/20110214-1-1.htm. (https://www.tsb.org.tr/resmi-istatistikler.aspx?pageID=909)sp