Türkiye’de Sanayi ve Tarım Sektörü Faaliyetleri ile İktisadi Büyüme Arasındaki İlişkiler: Kaldor Büyüme Yasasının Analizi
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2020
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info:eu-repo/semantics/openAccess
Özet
İktisadi büyüme gelişmiş ve gelişmekte olan ülkelerin politika yapıcılarınınen temel uğraş konularının başında yer almaktadır. Bu çalışmanın amacı da1961-2017 yılları arası Türkiye’de tarım ve sanayi üretimi ile iktisadi büyümearasındaki ilişkileri analiz etmektir. Değişkenler arasındaki ilişkiler önceJohansen eşbütünleşme, sonrasında ise Granger nedensellik testleri ile analizedilmiştir. Analizlerde değişkenlerde tespit edilen yapısal kırılmalar dadikkate alınmıştır. Yapılan analizler neticesinde değişkenler arasındaeşbütünleşme ilişkisi gözlemlenmiştir. Ayrıca çalışmada, Türkiye’deKaldor’un birinci ve üçüncü yasasını destekler sonuçlara ulaşılamamıştır.Test sonuçlarına göre iktisadi büyüme sanayileşmeyi pozitif yönde,sanayileşme ise tarım sektörünü negatif yönde etkilemektedir. Son olarakiktisadi büyüme tarım sektörünü negatif, tarım sektörü iktisadi büyümeyipozitif yönde etkilemektedir. Amaç: Çalışmada Türkiye için Nicholas Kaldor’un (1966; 1968) öne sürdüğüsanayileşme sürecinin iktisadi büyümeye ve pozitif dışsallığı ile diğersektörlere olumlu katkısının olup olmadığını sınanması amaçlanmaktadır.Bu amaç doğrultusunda Türkiye’nin gayri safi yurt içi hasıla, sanayi vetarım üretimine ait 1961-2017 dönemi verileri kullanılarak Kaldor’unbüyüme yasası analiz edilmektedir.Yöntem: Çalışmada değişkenler arasındaki ilişkiler altı aşamadaincelenmiştir. Birinci aşamada değişkenlerde var olan anlamlı yapısalkırılmalar Bai ve Perron (1998, 2003) tarafından geliştirilen testlebelirlenmiştir. İkinci aşamada değişkenlerin durağanlık düzeyleri gelenekselbirim kök testlerinden olan Genelleştirilmiş Dickey-Fuller (ADF), PhillipsPerron (PP) ve Kwiatkowski-Phillips-Schmidt-Shin (KPSS) birim kök testleriile Zivot ve Andrews (1992) tarafından geliştirilen yapısal kırılmalı birimkök testleri (ZA) yardımıyla belirlenmiştir. Üçüncü aşamada değişkenlerarasında uzun dönemli ilişkileri saptayabilmek amacıyla JohansenEşbütünleşme Analizi kullanılmıştır. Dördüncü aşamada değişkenlerarasındaki nedensellik ilişkileri Vektör Hata Düzeltme Modeline dayalıGranger nedensellik analizi kullanılarak tespit edilmiştir. Beşinci aşamadatespit edilen nedenselliklerin yönünü belirlemek amacıyla Etki-Tepkianalizleri kullanılmıştır. Son olarak, altıncı aşamada nedensellik testiylebulunan nedensellik ilişkisinin inceleme dönemi dışında da geçerli olupolmadığı belirlemek amacıyla Varyans Ayrıştırma Analizindenyararlanılmıştır. Üçüncü aşamadan son aşamaya kadar olan süreç zaman serisi analizlerinde sıklıkla kullanılan ve Sims (1980) tarafından geliştirilenVektör Otoregresif (VAR) modellerine dayanmaktadır. Birinci aşamada eldeedilen yapısal kırılma tarihleri VAR modeline dahil edilerek diğer aşamalaruygulanmıştır.Bulgular: Yapılan kırılmalı analizlerde değişkenlerde yapısal kırılmalarınolduğu belirlenmiştir. Elde edilen kırılma tarihleri dikkate alınarakdeğişkenler arasındaki eşbütünleşme ve nedensellik ilişkileri test edilmiştir.Johansen eşbütünleşme test sonuçlarına bakıldığında, değişkenler arasındahem iz hem de maksimum olabilirlik test istatistiklerine göre anlamlıeşbütünleşme ilişkisi olduğu bulgusuna ulaşılmıştır. Bunun anlamı LGDP,LAGR ve LIND arasında uzun dönemli anlamlı ilişkiler vardır. Buaşamadan sonra yapılan Granger nedensellik testi sonuçlarına göre kısadönemde LAGR’dan LGDP’ye doğru tek yönlü, LIND’den LAGR’a tekyönlü, LGDP’den LIND’e ise tek yönlü nedensellik ilişkisi olduğugörülmektedir. Uzun dönemde ise LGDP’den ve LIND’den LAGR’a doğrutek yönlü nedensellik ilişkisi olduğu tespit edilmiştir. Elde edilennedensellik sonuçlarının yönünü belirlemek amacıyla etki-tepkifonksiyonları incelenmiştir. Etki-tepki fonksiyonlarına göre LAGR’dameydana gelen bir şok LGDP’yi yaklaşık 5 dönem pozitif yönde etkilemektesonrasında ise etki kaybolmaktadır. LGDP’de meydana gelen bir şokunLAGR’ı genellikle negatif etki ettiği görülmektedir. LIND, LAGR’i 7 dönemnegatif yönde etkilemekte sonrasında ise etki kaybolmaktadır. LGDP iseLIND’i tüm periyod boyunca pozitif yönde etkilemektedir. Son olarakyapılan Varyans ayrıştırma analiz sonuçlarına göre iktisadi büyümedekideğişmenin %1.5’i LIND’den, %8.5’i LAGR’dan, %90’ı ise kendi şoklarındankaynaklanmaktadır. LAGR’daki değişmenin %5’i LIND’den, %50’si kendişokundan, %45’i ise LGDP den kaynaklanmaktadır. LIND’deki birdeğişmenin ise % 68’i kendi şokundan, %20’si LAGR’den, %12’i iseLGDP’de meydana gelen şoklarla açıklanmaktadır.Sonuç: Elde edilen bulgulara göre iktisadi büyümenin sanayi üretiminidestekler nitelikte olmaması Türkiye’de sanayi sektörünün katma değeriyüksek çıktıları ortaya koyamamasından kaynaklanabilir. AyrıcaTürkiye’nin imalat sanayiinde ithal girdi bileşenlerinin yüksek olmasıiktisadi büyümeyi artırıcı etkisinin olmamasının en büyük nedenlerindenbiridir. Bununla birlikte Mamgain’a (1999) ait çalışmada da vurgulandığıgibi küreselleşme süreci neticesinde ortaya çıkan faktörlerin Kaldor’unmodeline dahil edilmesinde fayda görülmektedir. Dünya Bankası verilerinegöre Türkiye’de sanayi sektörünün 2018 yılı itibariyle milli gelir içerisindekipayı %29,5 civarındadır. Malezya’da bu oran %38.3, Güney Kore’de %35.1,Tayland’da %35, Endonezya’da %39.7 olarak gerçekleşmiştir. Sanayileşmesüreci ile iktisadi büyüme hızlarını artıran bu ülkelere nazaran Türkiye’ninsanayi üretimi daha düşük seviyelerde görülmektedir. Diğer bir bulgu isesanayi üretimindeki artışın tarım üretimini negatif yönde etkilemesidir. TheWorld Bank (2019b) verilerine göre sanayi sektörünün milli gelir içerisindekipayı arttıkça tarımsal üretimin payı devamlı surette azalmaktadır. Türkiye’nin 1990 yılında tarımsal üretimin katma değeri milli gelirin%17.5’ini oluştururken bu oran 2018 yılında %5.8 olarak gerçekleşmiştir.Sanayileşme süreci ile birlikte işgücünün diğer sektörlerden sanayisektörüne doğru kayması doğal bir süreç olarak dünya tarihi boyuncagözlemlenen bir sonuçtur. Lakin bu süreçte beklenti Kaldor’un da ifadeettiği gibi gizli işsizliğin daha yoğun olduğu tarım sektöründeki atılişgücünün sanayi sektörüne doğru kayması şeklindeydi. Hatta bu şekildegerçekleşecek sanayileşme imalat sektörü dışındaki sektörlerin debüyümesine katkı sağlayacaktı. Fakat bu yasa çalışmanın ampiriksonuçlarıyla örtüşmemekte ve sanayileşme Türkiye’de tarım sektörünüolumsuz yönde etkilemektedir. Bu bağlamda bir ekonomi tüm sektörleriylebir bütün olarak değerlendirilmelidir. Bir sektörün tek başına bir ekonomiyiyüklenmesi Türkiye’de görüldüğü gibi daha büyük sorunların ortayaçıkmasına yol açabilecektir. Örnek vermek gerekirse son yıllarda inşaatsektörü üzerinden büyüme ivmesi yakalamış olan Türkiye’de dövizkurunda ve faiz oranlarında görülen artışın bu sektöre olumsuz yansımalarıbirçok inşaat firmasının iflasına ya da işlerini yarıda bırakmalarına nedenolmuştur. Bu durum ise genel itibariyle Türkiye ekonomisine büyük bir yükoluşturmaktadır. Halbuki analiz sonuçlarında bir diğer tespit ise kısadönemde tarımsal üretimin iktisadi büyümeyi pozitif uzun dönemde iseiktisadi büyümenin tarımsal üretimi negatif yönde etkilemesidir. Türkiyeekonomisi bu anlamda tarım sektörünün sağlayacağı katma değeri göz ardıetmemelidir. Siyaset yapıcılar tarım sektörünü destekleyici politikalarla hemiç piyasaları canlandırabilmeli hem de tarım sektörünün iktisadi büyümeyisağlayıcı yönü ile diğer sektörlere de olumlu etkileri olabileceğiniunutmamalıdırlar.
Economic growth is one of the most fundamental issues of policymakers of developed and developing countries. The aim of this study is to analyze the relations between agricultural and industrial production and economic growth in Turkey for the period of 1961-2017. The relationships between variables were analyzed using Johansen cointegration and Granger causality tests. According to the test results, it was found that there was a cointegration relationship between the variables. Furthermore, the results supported the first and third laws of Kaldor in Turkey were not reached. According to the test results, economic growth affects industrialization positively and industrialization negatively affects the agricultural sector. Finally, economic growth in the positively affects the agricultural sector, while the agricultural sector positively affects economic growth. Summary Aim: The study aims to test whether the industrialization process suggested by Nicholas Kaldor (1966; 1968) for Turkey has a positive contribution to economic growth and to other sectors owing to its positive externality or not. Accordingly, Kaldor's growth law was analyzed using the data obtained during the period of 1961-2017, as to the gross domestic product, industrial production and agricultural production of Turkey. Method(s): In the study, the relationships between the variables were examined in six stages. In the first stage, significant structural breaks in the variables were determined via a test developed by Bai and Perron (1998, 2003). In the second stage, stationary levels of the variables were determined using the Generalized Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) unit root tests, as well as the structural break unit root tests (ZA), developed by Zivot and Andrews (1992). In the third stage, the Johansen Cointegration Analysis was used to determine the long-term relationships between the variables. In the fourth stage, causality relationships between the variables were determined using the Granger causality analysis based on the Vector Error Correction Model. In the fifth stage, the Impulse-Response analyses were used to determine the orientation of the causalities detected. Finally in the sixth stage, the Variance Decomposition Analysis was used to determine whether the causality relationship found via the causality test was valid or not, except the examination period. The process from the third stage to the final stage is based on the Vector Autoregressive (VAR) models, which are frequently used in time series analysis and were developed by Sims (1980). The structural break dates obtained in the first stage are included in the VAR model, and other stages are applied. Results: In the structural break analysis, it was determined that there were structural breaks in the variables. Considering the break dates obtained; cointegration and causality relationships between the variables were tested. Given the Johansen co-integration test results; it was found that there was a significant co-integration relationship between the variables according to both trace and maximum likelihood test statistics. This means that there are significant long-term relationships between LGDP, LAGR and LIND. According to the results of the Granger causality test conducted following this stage, it is observed that there is a one-way causality relationship from LAGR to LGDP, from LIND to LAGR and from LGDP to LIND in the short term. In the long term, it has been determined that there is a one-way causality relationship from LGDP and LIND to LAGR. In order to determine the orientation of the causality results obtained, the impulse-response functions were examined. According to the impulse-response functions, a shock occurring in LAGR affects LGDP positively for about 5 periods, and then the impulse disappears. It is seen that a shock occurring in LGDP generally has a negative impulse on LAGR. LIND affects LAGR negatively for 7 periods, and then the impulse disappears. LGDP affects LIND positively throughout the entire period. Finally, according to the results of the variance decomposition analysis, 1.5% change in economic growth is caused by LIND, 8.5% by LAGR and 90% by their own shocks. 5% change in LAGR is caused by LIND, 50% by its own shock and 45% by LGDP. 68% change in LIND is caused by its own shock, 20% by LAGR and 12% by LGDP. Conclusion: According to the results, the fact that economic growth does not support industrial production may be caused by a failure to provide high throughput with a high added-value by the industrial sector in Turkey. The most important reason for the absence of an economic growth-increasing effect is that import entry components are higher in Turkey’s production industry. However, as is emphasized in the study by Mamgain (1999), it is useful to include the factors emerging as a result of the globalization process in Kaldor's model. According to the World Bank data, the share of Turkey’s industrial sector in the national income was around 29.5% as of 2018. This rate was 38.3% in Malaysia, 35.1% in South Korea, 35% in Thailand and 39.7% in Indonesia. Turkey's industrial production is at lower levels compared to these countries, which increase their economic growth rate with the industrialization process. Another result is that the increase in industrial production affects agricultural production negatively. According to the World Bank data (2019b), as the share of the industry sector in the national income increases, the share of agricultural production decreases continuously. The added-value of agricultural production in Turkey in 1990 accounted for 17.5% of the national income, and this rate was 5.8% in 2018. The shift of the labor force from other sectors towards the industrial sector with the industrialization process is an outcome observed throughout world history as a natural process. However, in this process, it is expected for the inert labor force in the agricultural sector, where the hidden unemployment is more intense, to shift towards the industrial sector, as stated by Kaldor. Even the industrialization process that would take place would contribute to the growth of sectors other than the manufacturing sector. However, this law does not correspond to the empirical results of the study and industrialization affects the agricultural sector in Turkey negatively. In this context, an economy should be evaluated integratedly with all its sectors. The fact that a sector shoulders the economy alone can lead to bigger problems, as is seen in Turkey. For example, the negative reflection of an increase in the exchange rate and interest rates in Turkey which has gained speed owing to the construction sector during recent years, on this sector has caused bankruptcy or discontinuation of many construction companies. This generally implements a huge burden on the Turkish economy. However, another determination in the results of the analysis is that in the short term, agricultural production affects economic growth positively and in the long term, economic growth affects agricultural production negatively. In this regard, Turkey's economy should not ignore the added-value to be provided by the agricultural sector. Politicians should able to stimulate the domestic market with policies supporting the agricultural sector and should not forget that the agricultural sector may have positive effects on other sectors with its economic growth supportive characteristic.
Economic growth is one of the most fundamental issues of policymakers of developed and developing countries. The aim of this study is to analyze the relations between agricultural and industrial production and economic growth in Turkey for the period of 1961-2017. The relationships between variables were analyzed using Johansen cointegration and Granger causality tests. According to the test results, it was found that there was a cointegration relationship between the variables. Furthermore, the results supported the first and third laws of Kaldor in Turkey were not reached. According to the test results, economic growth affects industrialization positively and industrialization negatively affects the agricultural sector. Finally, economic growth in the positively affects the agricultural sector, while the agricultural sector positively affects economic growth. Summary Aim: The study aims to test whether the industrialization process suggested by Nicholas Kaldor (1966; 1968) for Turkey has a positive contribution to economic growth and to other sectors owing to its positive externality or not. Accordingly, Kaldor's growth law was analyzed using the data obtained during the period of 1961-2017, as to the gross domestic product, industrial production and agricultural production of Turkey. Method(s): In the study, the relationships between the variables were examined in six stages. In the first stage, significant structural breaks in the variables were determined via a test developed by Bai and Perron (1998, 2003). In the second stage, stationary levels of the variables were determined using the Generalized Dickey-Fuller (ADF), Phillips-Perron (PP) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) unit root tests, as well as the structural break unit root tests (ZA), developed by Zivot and Andrews (1992). In the third stage, the Johansen Cointegration Analysis was used to determine the long-term relationships between the variables. In the fourth stage, causality relationships between the variables were determined using the Granger causality analysis based on the Vector Error Correction Model. In the fifth stage, the Impulse-Response analyses were used to determine the orientation of the causalities detected. Finally in the sixth stage, the Variance Decomposition Analysis was used to determine whether the causality relationship found via the causality test was valid or not, except the examination period. The process from the third stage to the final stage is based on the Vector Autoregressive (VAR) models, which are frequently used in time series analysis and were developed by Sims (1980). The structural break dates obtained in the first stage are included in the VAR model, and other stages are applied. Results: In the structural break analysis, it was determined that there were structural breaks in the variables. Considering the break dates obtained; cointegration and causality relationships between the variables were tested. Given the Johansen co-integration test results; it was found that there was a significant co-integration relationship between the variables according to both trace and maximum likelihood test statistics. This means that there are significant long-term relationships between LGDP, LAGR and LIND. According to the results of the Granger causality test conducted following this stage, it is observed that there is a one-way causality relationship from LAGR to LGDP, from LIND to LAGR and from LGDP to LIND in the short term. In the long term, it has been determined that there is a one-way causality relationship from LGDP and LIND to LAGR. In order to determine the orientation of the causality results obtained, the impulse-response functions were examined. According to the impulse-response functions, a shock occurring in LAGR affects LGDP positively for about 5 periods, and then the impulse disappears. It is seen that a shock occurring in LGDP generally has a negative impulse on LAGR. LIND affects LAGR negatively for 7 periods, and then the impulse disappears. LGDP affects LIND positively throughout the entire period. Finally, according to the results of the variance decomposition analysis, 1.5% change in economic growth is caused by LIND, 8.5% by LAGR and 90% by their own shocks. 5% change in LAGR is caused by LIND, 50% by its own shock and 45% by LGDP. 68% change in LIND is caused by its own shock, 20% by LAGR and 12% by LGDP. Conclusion: According to the results, the fact that economic growth does not support industrial production may be caused by a failure to provide high throughput with a high added-value by the industrial sector in Turkey. The most important reason for the absence of an economic growth-increasing effect is that import entry components are higher in Turkey’s production industry. However, as is emphasized in the study by Mamgain (1999), it is useful to include the factors emerging as a result of the globalization process in Kaldor's model. According to the World Bank data, the share of Turkey’s industrial sector in the national income was around 29.5% as of 2018. This rate was 38.3% in Malaysia, 35.1% in South Korea, 35% in Thailand and 39.7% in Indonesia. Turkey's industrial production is at lower levels compared to these countries, which increase their economic growth rate with the industrialization process. Another result is that the increase in industrial production affects agricultural production negatively. According to the World Bank data (2019b), as the share of the industry sector in the national income increases, the share of agricultural production decreases continuously. The added-value of agricultural production in Turkey in 1990 accounted for 17.5% of the national income, and this rate was 5.8% in 2018. The shift of the labor force from other sectors towards the industrial sector with the industrialization process is an outcome observed throughout world history as a natural process. However, in this process, it is expected for the inert labor force in the agricultural sector, where the hidden unemployment is more intense, to shift towards the industrial sector, as stated by Kaldor. Even the industrialization process that would take place would contribute to the growth of sectors other than the manufacturing sector. However, this law does not correspond to the empirical results of the study and industrialization affects the agricultural sector in Turkey negatively. In this context, an economy should be evaluated integratedly with all its sectors. The fact that a sector shoulders the economy alone can lead to bigger problems, as is seen in Turkey. For example, the negative reflection of an increase in the exchange rate and interest rates in Turkey which has gained speed owing to the construction sector during recent years, on this sector has caused bankruptcy or discontinuation of many construction companies. This generally implements a huge burden on the Turkish economy. However, another determination in the results of the analysis is that in the short term, agricultural production affects economic growth positively and in the long term, economic growth affects agricultural production negatively. In this regard, Turkey's economy should not ignore the added-value to be provided by the agricultural sector. Politicians should able to stimulate the domestic market with policies supporting the agricultural sector and should not forget that the agricultural sector may have positive effects on other sectors with its economic growth supportive characteristic.
Açıklama
Anahtar Kelimeler
İşletme Finans, İktisat, İşletme
Kaynak
İnsan ve Toplum Bilimleri Araştırmaları Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
9
Sayı
1
