İKTİSADİ BÜYÜME OLGUSUNA EKONOMETRİK BİR YAKLAŞIM: “BRIMCH” ÜLKELERİ VE TÜRKİYE ÖRNEĞİ

AN ECONOMETRIC APPROACH TO THE PHENOMENON OF ECONOMIC GROWTH: A CASE STUDY ON “BRIMCH” COUNTRIES AND TURKEY

Mustafa GÖMLEKSİZ,Mehmet ALAGÖZ

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Büyüme ve kalkınma, yıllardan bu yana iktisadi gelişimin açıklanmasında çoğu zaman karıştırılabilen kavramlar olmuşlardır. II. Dünya Savaşı’ndan sonra önde gelen iktisatçılar tarafından ilgi odağı olmuş kalkınma ekonomisinin, sanayi devriminden sonra ulusların zenginliğini belirlemede temel ölçüt olmuş büyüme kuramlarından daha kısa bir geçmişi vardır. Bu iki kavram arasındaki farklılığın, gelişimin niteliği veya niceliğinden kaynaklandığını vurgulayabiliriz. İktisadi ve sosyal sermayenin gelişimini ve sürdürülebilirliğini, insan kaynağını entelektüel sermayeye dönüştürme süreçleriyle alakalandıran kalkınma ekonomisi içerisinde, üretimin ve milli gelirin artması olarak tanımlanan iktisadi büyümenin de payı göz ardı edilemez. Bu kapsamda, büyüme kuramcıları sıklıkla iktisadi büyümeyi meydana getiren ekonomik faktörleri inceleme ihtiyacı içerisinde olmuşlardır. Bu çalışmada, Türkiye ve yükselen piyasalar olarak tanımlanan Brezilya, Rusya, Hindistan, Meksika, Çin ve Macaristan (BRIMCH) gibi ülkelerin 2000 - 2010 yılları arası nüfus ve belirli makroekonomik göstergelerinin büyüme üzerindeki etkilerinin tahminine yönelik ekonometrik analize tabi tutulmuştur. Tahmin sonucu nüfus ve dış ticaret değişkenlerinin büyüme üzerindeki etkileri anlamlı ve pozitif çıkmıştır. Ele alınan diğer değişkenlerden toplam kamu harcamaları ve bütçe açığının ise büyüme üzerindeki etkileri negatif ve anlamlıdır. Ayrıca analiz sonucu enflasyon ve ödemeler dengesi değişkenlerinin iktisadi büyüme ile olan ilişkileri istatistiksel olarak anlamsız bulunmuştur.

Concepts of development and growth have often mistaken for each other for years.
The development economics which was spotlighted by prominent economists after the
Second World War has a shorter history than theories of growth which was the core
process of determining wealth of nations after industrial revolution. We can emphasize
that differences among the concepts arise from qualification and proportion of progress.
The share of economic growth which is identified as increase of production and national
income cannot be ignored in the development economics that progress of economic and
social capital relate with processes of transforming human resources to intellectual capital.
In this context, growth theorists have often been in need of analyzing economic factors
which may bring about growth.
The “BRIC” acronym which defines Brazil, Russia, India and China as rapidly growth
markets is first used by American economist Jim O’Neill to emphasize that these
countries’ increasing economic effects in global scale. In recent years Mexico and
Hungary are also attended this specific group by some economists who stress their
promising economic situation. Therefore this acronym can be also called as “BRIMCH”.
There have been various studies in literature about concept of economic growth after
global crisis and fluctuations in the early 2000’s. In respect to this concern experts have
concerned with investigating determinants of economic growth. In these studies some
experts have used single variable models for growth analysis when others have applied
economic or structural multivariate models. These models generally aim to determine
correlation between economic growth and economic indicators such as inflation,
government expenditures, budget deficits, imports, exports and external debts. In this
context applied econometric methods comprise of regression analysis, panel data analysis
(Kormendi and Meguire, 1985; Barro, 1996; Çakmak and Değer, 2003; Berber and Artan,
2004b; Aykut and Sayek, 2007; Lee and Kim, 2008; Adak, 2010), causality and
cointegration tests (Ulusoy and Küçükkale, 1996; Sarı, 2003; Terzi, 2004; Işık and
Alagöz, 2005; Yapraklı, 2007; Aktaş, 2009) and they focus on single or country groups.
In this study Brazil, Russia, India, Mexico, China, Hungary and Turkey which are
called emerging markets are subjected to Panel Data Analysis within the context of
structural and economic indicators which connected with economic growth processes. In
this framework these countries’ GDP growth data which is generated between years 2000
and 2010 and inflation, population, government expenditures, balance of payment, budget
deficit and the balance of trade indicators are subjected to a linear model seeing that
possible correlations. This method is important for obtaining more complex findings.
Related variables are examined with panel data analysis in order to determine possible
correlation with GDP (fixed price) within 7 countries. Panel data analysis is a method
which allows estimating parameters belonging to individuals or firms combining their
cross section specific data with time series. There are two main methods using in panel
data analysis called Fixed Effects and Random Effects. The correlation between error
terms and “X” explanatory variable is detected in order to determine which model (Fixed
Effects or Random Effects) is applicable for the study. Possible correlation between
explanatory “X” and error term is tested with Hausman specification test which is one of
the referenced methods in panel analysis. The result of the test is as below. (Chi Square (6): 45.791, p: 0,000)
Thus null hypothesis that we established “There isn’t a correlation between error term
(εi) and X explanatory variable” is rejected. Therefore Fixed Effects is chosen as applied
method. Besides Levin, Lin & Chu unit root test is applied in order to examine stationary
of dataset. As a consequence of this test it isn’t encountered unit root between variables
and coefficients of these variables are significant at significance level of 1% (see table 6.).
Hence a possible unsafe situation which may arise from stationary problem is eliminated.
The estimated linear model with applying Fixed Effects method is as below.
GSYH = β0 + β1ENF.it + β2NUF.it + β3TKHit + β4ODit + β5BAit + β6DTDit + uit
As a result of the Fixed Effects analysis coefficients of population, total government
expenditures, budget deficit and balance of trade variables are found as significant. Here,
balance of trade variable is statistically significant at significance level of 10% and other
variables are significant at level of 1%. Moreover coefficient of population variable is
positive (see table.7). Considering the model it can be argued that each unit increase in the
population resulted in an increase on the growth of a 3.87 unit. Coefficient of balance of
trade variable is also positive and accordingly each unit increase in the balance of trade
resulted in an increase on the GDP growth of a 0.017 unit. This effect is relatively much
less compared to the coefficient of the population.
On the other hand total government expenditures variable’s coefficient is found
negative. In other words a unit increase in the total government expenditures resulted in a
decrease on the GDP growth of a 1.188 unit. Similarly it is found that a unit increase in
budget deficit resulted in a decrease on the GDP growth of a 0.066 unit as a result of the
analysis. Additionally coefficient of inflation variable is found statistically insignificant
although it’s positive. Identically balance of payments variable’s coefficient is negative
and insignificant.
Looking at the coefficient of determination (R square) which is an indicator of
explanation level of independent variables on dependent one, obtained value of 0,6319
shows that approximately 63% of changes in dependent variable is explained by
explanatory variables. This value represents goodness of fit analysis. F-test which is
identifying the model that best fits the statistical population from which the data were
sampled is also found significant.
Goldman Sachs (O’Neill and Stupnytska, 2009) which is a global financial
management firm prepared a report as a part of these economies including “BRIMCH”
countries, Turkey and the next 11 emerging economies (N-11) in order to assess their
future projections. In this report it is argued that the BRIC and N-11 economies,
collectively, appear to be emerging from the global credit crisis better than the major
economies. Moreover it is emphasized that the BRICs could become as big as the G7 by
the year 2032 and China could become as big as the US by 2027 (O’Neill and Stupnytska,
2009). Hence determining factors about economic growth of these economies gain
importance because of their promising position on global scale.
Similarly, removal of a portion of these variables, or to join different types of
economic or structural factor can reveal a more different type of view. Besides working
on different or longer time series can vary the obtained results since current data set
involves a period that coincides with global fluctuations and crisis.

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