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.