Gibrat Orantılı Etki Yasasının Türk Bankacılık Sektörü için Geçerliliğinin Sınanması

Gibrat orantili etki yasasi olarak bilinen yasa firma büyüklüğü ile firma büyüme orani arasinda bir ilişkinin olmadiğini, firma için büyüme sürecinin pek çok faktörün ortak etkisi sonucu tesadüfi belirlendiğini ileri sürmektedir. Bu çalişmada yasanin Türk bankacilik sektörü için geçerliliği sinanmiştir. 1988 - 2008 yillari arasi bankacilik sektöründeki giriş - çikişlar ve bankalarin yaşam analizi yapilmiş ve net varlik büyüklüğü faktörü esas alinarak, büyüme performanslari iki aşamali Heckman yöntemi kullanilarak incelenmiştir. Söz konusu dönemde piyasaya giriş ve çikişlarin çok yoğun yaşandiği göz önünde bulundurularak, örneklem seçim yanliliğindan kaçinmak için Heckman iki aşamali tahmin yöntemi kullanilmiştir. Elde edilen bulgular, Gibrat yasasinin Türk Bankacilik sektöründe faaliyet gösteren bankalar için geçerli olmadiğini, küçük bankalarin büyük bankalara göre daha hizli büyüme eğiliminde olduğunu göstermektedir.

Testing Gibrat's Law of Proportionate Effect on Turkish Banking Sector

Gibrat's law of proportionate effect asserts that there exists no difference in the probability distribution of growth rates across size classes of firms. Firm's growth performance is a random process that results from combination of several factors that are both internal and external to the firm. This research aims to test whether the law of proportionate effect is valid for Turkish Banking sector. To this aim, sectoral entry and exits, and growth performances are investigated through survival anaylsis and Heckman Two Step Estimation Technique on the basis of net assets for the years 1988 - 2008. Growth and survival of banks have been tested through a stochastic model. Since the entry and exits rates of the sector has been very high for the period, in order to avoid sample selection bias Heckman two-step estimation technique is employed. Findings indicate that smaller banks grew faster than larger banks, that Gibrat's law does not hold, and these results are not subject to sample selection bias.

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