Economic Diversification and Growth in Euro-Mediterranean Countries : A Granger Causality Approach in Panel Data

This paper develops and tests the relationship between economic diversification and growth in Euro-Mediterranean countries. The analysis covers the period 1975-2013 in a panel data of 35 Euro-Med countries, and introduces a bootstrapped Granger-causality approach in order to analyze the unidirectional causality, and likely the bidirectional causality between economic diversification and growth. Overall, results provide robust empirical evidence of a positive impact of growth on economic diversification in most of developing countries benefiting from diversifying their economies in contrast to developed countries that perform with more economic specialization. © 2016 Elixir All rights reserved. Elixir Inter. Busi. Mgmt. 92 (2016) 38953-38962 International Business Management Available online at www.elixirpublishers. com (Elixir International Journal) Djennas Mustapha et al./ Elixir Inter. Busi. Mgmt. 92 (2016) 38953-38962 38954 ability to partially offset fluctuations in some economic sectors with other ones characterized which a relative stability. Hence, diversification is a key element to reduce instability and enhance growth (Gutiérrez-de-Piñeres & Ferrantino, 2000; Balaguer & Cantavella-Jordá, 2004; Hammouda et al., 2006; Love, 1986; Greenaway, Morgan & Wright, 1999; Feenstra & Kee, 2004). However, the positive relationship between economic diversification and growth is not always revealed in the literature. For instance, others works stand up for the fact that this relationship is influenced by other supporting factors such as finance, openness, human capital, investment and savings policies, or even the time dimension, as long as it is also relevant to analyze both the short and the long run relationship between diversification and growth. This implies that the impact of economic diversification on growth is weaker in the short run than in the long run. The adjustment will, however, not take long, notably because economic growth is also found to contribute to increased diversification. In a more developed stage, the relationship between economic diversification and growth is confirmed only among developed economies, but this was not the case among developing countries. Therefore, a certain minimum level of development is crucial for economic diversification to impact on growth (Michaely, 1977). Moreover, no evidence supporting diversification-induced growth was found in the works of Ferrantino (2000) and Chang et al. (2000). However, the conflicting results could be potentially traced back to different levels of disaggregation and types of regression models used (cross-sectional versus time series), so more work needs to be done for a better understanding of the relationship between economic diversification and growth. 3. Data and Methodology A large body of research on economic diversification and growth studies the relationship between them by including cross-country regressions or panel studies. 3.1. Data and Variables Description Annual data involving 35 Euro-Mediterranean countries, including developed and developing countries from 1975 to 2013 was used in the analysis. The used variables are the logarithm of the GDP and the logarithm of the HerfindahlHirschman Index for measuring diversification (HHI). The data are obtained from World Bank and International Monetary Fund . In what follow, the natural logarithms of these two variables are denoted as lnGDP and lnHHI respectively. To investigate panel causal links between economic diversification and growth, it is recommended to use appropriate metrics of these variables. Measuring growth is a relative easy process compared to economic diversification. Researchers have constructed various scalar measures of regional economic diversity using different economic theories, but these measures are sometimes difficult to obtain consistently across countries for an adequate time period for a causality study. For example, the most used indexes for measuring diversification in the empirical literature are the Ogive index of economic diversity, the Entropy index, the Location Quotient and the Herfindahl-Hirschman Index. Because of its simplicity, the HHI index, is a widely-used measure of diversification/concentration in an economy, industry or a market (Scherer, 1980; Tauer, 1992). The HHI index indicates the extent to which a particular economy is dominated by a few sectors or not. Accordingly, this paper considers the HHI which can be expressed by squaring the employment shares of all sectors in an economy and then summing the squares, as follows (Hirschman, 1989): (1) Where ESi represents the share of employment in the i th sector and n the number of sectors in an economy. The HHI varies from zero, meaning that the economy has a large number of sectors with small and relatively equal employment shares (high diversification) to one, where an economy is based on a one single prevailing sector with a full-employment (full specialization). Thus, a situation with a less concentration (or high diversification) in the dominant sector leads to low values of the HHI, against the situation where an increase indicates high concentration (or a less diversification) in the dominant sector. Therefore, according to the HHI, more equal distribution of employment among a large number of sectors means higher level of economic diversity. In this paper, three main economic sectors are included in the empirical analysis, namely agricultural, industrial and services sectors (n = 3). 3.2. Econometric framework From the previous literature review, the causal relationship between economic diversity and growth could be in either one or both directions. It is also possible to even have no interdependency. This study uses the approach of Bootstrapping Panel Granger-causality developed by Kónya (2006) to empirically test the causal relationship between economic diversification and growth by considering contemporaneous correlation across countries and cross-country heterogeneity. In this case, causality tests are considered as an extension of the standard time series Granger-causality tests, but in a cross-sectional dimension. For each individual or entity, a variable x Granger-cause a variable y if y can be better predict using all available information than in the case where the information set used does not include x (Granger, 1969). Hence, economic diversification is said to Granger-cause a growth if the lagged values of economic diversification helps to forecast more efficiently the growth levels. Even if the Granger-causality test for time series data has been well developed, a better way of testing for causality is to combine both the cross-sectional and time series data, and to perform the so-called panel Grangercausality test (Hurlin & Venet, 2001; Hurlin, 2004; Hurlin, 2007). Consequently, applying Granger-causality test in panel data is more efficient than using only the time series data (Hurlin & Venet, 2001). Overall, the two main used approaches in testing Grangercausality in panel data are the works developed by Hurlin (2001), and the one of Kónya (2006). However, the Hurlin’s (2008) approach controls for the heterogeneity, but it is not able to take into account the cross-sectional dependence. The Hurlin work’s drawback does not exist in the Kónya’s (2006) approach, where both the cross-sectional dependence and the heterogeneity are taken into account. Moreover, the Kónya’s approach does not require any pre-testing for panel unit root and co-integration. In a country-by-country analysis, the possibility of Granger-causality between lnGDP and lnHHI can be modeled using the following Seemingly Unrelated Regressions (SUR) systems (Kónya, 2006): Djennas Mustapha et al./ Elixir Inter. Busi. Mgmt. 92 (2016) 38953-38962 38955