Determination of Criteria Affecting the Growth Performance of Manufacturing Industry Firms in Türkiye Using Quantile Regression
Aycan Kulaksız Hacıbebekoğlu, Seda Bağdatlı KalkanThis study aims to examine the growth performance of firms in Türkiye’s manufacturing industry, taking into account firm characteristics, firm capacity, and human capital indicators. Data from the “Business Environment and Enterprise Performance Survey” for the 2018–2020 period, conducted jointly by the European Bank for Reconstruction and Development and the World Bank, are used. The quantile regression method was used to analyze 395 companies engaged in manufacturing activities in Türkiye that experienced increased sales revenues between 2016 and 2018. This method yields different coefficient results for different quantile points and provides various regression models (Chen, 2005; Koenker, 2005). It allows for the interpretation of growth performance at various levels. This study examined the 0.10th, 0.20th, ... 0.90th quantile results for growth performance. The study’s findings indicate that the firm’s weekly working hours have no statistically significant effect on growth performance across all quantile levels. The capacity utilization rate of firms have a positive and statistically significant impact on the growth performance of firms in the 0.70th, 0.80th, and 0.90th quantiles. Regardless of the level of growth performance, an increase in firm age resulted in a decrease in growth performance. Furthermore, an increase in nonproduction employee rates for the 0.10th, 0.20th, 0.30th , and 0.40th quantiles leads to increased growth performance. Similarly, the rate of employees with university degrees at the 0.10th quantile has a positive and statistically significant impact on firm growth performance.
Türkiye’deki İmalat Sanayi Firmalarının Büyüme Performanslarını Etkileyen Kriterlerin Kantil Regresyon ile Belirlenmesi
Aycan Kulaksız Hacıbebekoğlu, Seda Bağdatlı KalkanBu çalışmanın amacı Türkiye’de imalat sanayinde faaliyet gösteren firmaların büyüme performanslarını firma özellikleri, firma kapasitesi ve insan sermayesi göstergelerini ele alarak incelemektir. Bu amaçla Avrupa İmar ve Kalkınma Bankası (EBRD), Dünya Bankası (WB) ile ortaklaşa yapılan “İş Ortamı ve İşletme Performansı Araştırması” (BEEPS) 2018-2020 dönemi 2019 yılı sonuçları kullanılmıştır. Türkiye’de imalat sanayinde faaliyet gösteren, 2016-2018 yılları arasında satış gelirleri artmış olan 395 firma kantil regresyon yöntemi ile incelenmiştir. Bu yöntem, farklı kantil noktaları için farklı katsayı sonucu vermekte olup, farklı regresyon modelleri sunmaktadır (Chen, 2005; Koenker, 2005). Dolayısıyla farklı büyüme performans düzeyleri için yorum yapılmasına olanak sağlamaktadır. Bu çalışmada büyüme performansı için 0.10, 0.20, . . . 0.90’ıncı kantil sonuçları incelenmiştir. Çalışmanın sonucunda, Türkiye’de imalat sanayinde faaliyet gösteren firmaların haftalık çalışma saatinin tüm kantil düzeylerinde büyüme performansı üzerinde istatistiksel olarak anlamlı bir etkisi olmadığı görülmüştür. Buna karşın firmaların kapasite kullanım oranının, firma büyüme performansı üzerinde 0.70, 0.80 ve 0.90’ıncı kantiller için pozitif ve istatistiksel olarak anlamlı bir etkisi bulunmaktadır. Firma yaşı arttığında, firmanın büyüme performans düzeyi fark etmeksizin büyüme performansında azalış görülmüştür. 0.10, 0.20, 0.30, ve 0.40’ıncı kantiller için üretim dışı faaliyetlerde çalışan oranı arttıkça büyüme performansının da artış gösterdiği ortaya çıkmıştır. Benzer şekilde 0.10’uncu kantilde üniversite mezunu çalışan oranının, firmaların büyüme performansı üzerinde pozitif ve istatistiksel olarak anlamlı etkisi olduğu sonucuna varılmıştır.
The purpose of this study is to examine the growth performance of firms in Türkiye’s manufacturing industry, taking into account firm characteristics, firm capacity, and human capital indicators. Data from the “Business Environment and Enterprise Performance Survey” conducted jointly by the European Bank for Reconstruction and Development and the World Bank for the 2018–2020 period are used. The study included only surveys conducted in 2019. A total of 395 Turkish manufacturing firms with increased sales revenues between 2016 and 2018 were examined. Firms’ growth performance can be measured in various ways. In this study, it is measured by calculating the change in sales revenues, which is a widely used method in the literature. Firms’ growth performance is calculated using the formula [(sales revenue (sales revenue-2)) / sales revenue-2 × 100]. After calculating the change in sales revenues, firms whose sales revenues increased are included in the study. We focus on some firm characteristics, firm capacity, and human capital that can have an impact on growth performance. Firm age is used from firm characteristics. Meanwhile, firm capacity is measured using the capacity utilization rate and the weekly working hours. For the human capital indicator, the variables of how many years of experience the manager has in this sector, the rate of personnel working in nonproduction activities (the rate of employees working in units such as management, sales, etc.), and the rate of employees with university degrees are used.
The quantile regression method is used in the study to examine the growth performance of firms in the Turkish manufacturing industry. Koenker and Basset (1978) proposed the quantile regression method as an alternative to the classical one (Koenker and Halloc, 2001). The quantile regression method ignores the assumptions that must be satisfied in the classical regression method. When the error terms are not normally distributed, quantile regression parameter estimates are more effective than classical regression parameter estimates (Ünvan ve Demirel, 2020, s. 200). Furthermore, the quantile regression method solves the outlier problem and can be used when the data set contains outliers (Yavuz ve Aşık, 2017, Cameron ve Trivedi, 2005, s. 85). This study has outliers in the dependent variable. Except for the manager experience variables, all independent variables have outlier values. The quantile regression method produces more accurate results in the presence of outliers (Çınar, 2019). This method produces different coefficient results for different quantile points and provides various regression models (Chen, 2005; Koenker, 2005). Allows for the interpretation of growth performance at various levels.
As a result of the study, it is seen that the increase in the firm age negatively affected the growth performance of firms in the manufacturing industry in Türkiye. An increase in firm age leads to a decrease in growth performance, regardless of the level of growth performance. The capacity utilization rate of a firm has a positive and statistically significant impact on the growth performance of firms in the 0.70th, 0.80th, and 0.90th quantiles. It has been observed that increasing the capacity utilization rate increases growth performance. Another study finding indicates that the firm’s weekly working hours have no statistically significant effect on growth performance at all quantile levels. Furthermore, it has been estimated that senior management experience in the sector has a negative and statistically significant effect on firm growth performance in the 0.20th, 0.30th, 0.40th, and 0.50th quantiles. Increasing the experience of senior managers in the sector reduces growth performance. When the level of growth performance is high, senior management experience in the sector has no statistically significant effect on growth performance. Furthermore, an increase in the rate of nonproduction employees in the 0.10th, 0.20th, 0.30th, and 0.40th quantiles resulted in improved growth performance. The rate of employees working in management and sales have a statistically significant and positive effect on the growth performance of the companies in the manufacturing industry sector. Therefore, by strengthening their management and sales units, these firms will increase their productivity and thus their growth performance. Similarly, it is concluded that the rate of employees with university degrees at the 0.10th quantile positively and significantly impact growth performance.
