diversification patterns across countries. In addition the use of fixed effects mitigates the potential impact of having a non-random sample. Some countries are more likely to have gaps in their data due to an un-observable effect, and this is captured to some extent by the fixed effect (Wooldridge, 2009, p. 488). The dynamic panel nature of the data rules out the use of pooled OLS or within fixed-effects estimations as these methodologies would produce biased estimates. The presence of the lagged dependent variable among the explanatory variables causes correlation between the error term and the lagged variable in the within transformation used to obtain fixed effects estimates. Since the other regressors are likely to be correlated to some extent with the lagged dependent variable, their coefficients would also be biased.

For estimation purposes, the period 1980-2010 is divided into six sub-periods of five years each. The average of all variables included in the estimation is computed for each period t. This allows us to eliminate the influence of business cycles on the results as well as to minimize the potential for attenuation bias stemming from measurement errors in the data.

Equation (1) is estimated with the “GMM system estimator” of Arellano and Bond to solve the econometric problems caused by the presence of the lagged dependent variable in the set of the right hand-side variables. The GMM system estimator has been demonstrated to be more efficient and less biased than the Arellano and Bond’s difference estimator (Kpodar, 2007). The system estimator combines lagged levels as well as lagged differences as instruments. The period dummies are treated as strictly exogenous, while all the other explanatory variables are considered non-endogenous. The size of the matrix of instruments is reduced so as to avoid overinstrumentation bias. We estimate our autoregressive model with robust standard errors and the t- statistics obtained are presented in what follows.

# _<u>3.3 Empirical Findings</u>_

The GMM findings are presented in Table 3.2. In all cases, Hansen tests p-values do not reject the null hypothesis that the lagged instruments in levels and differences are valid. Moreover, Arellano and Bond’s auto-correlation of second order tests fail to reject the hypothesis of absence of second order auto-correlation.

In all specifications, access to finance, terms of trade shocks and FDI inflows have a significant effect on diversification. Ceteris paribus, countries receiving more direct investments tend to be more diversified. This suggests that foreign investors tend to develop new industries instead of pooling their resources in sectors already oriented towards international markets. Terms of trade shocks appear to be associated with a concentration of the export base, hereby suggesting that relative prices variations are accompanied by a reallocation of factors of production towards the sectors where profitability has increased. Moreover, the interaction term between terms of trade and human capital shown in column (4) is significant and negative. Agosin et al. (2012) find a similar result, which suggests that the concentrating effect of terms of trade shocks is less important for those countries with higher levels of human capital. Financial development has a 12