Global Journal of Human Social Science, E: Economics, Volume 23 Issue 3

oil-rich countries whose exports are almost exclusively energy products, and non-oil-rich countries whose exports are varied. The results of the econometric tests lead to converging conclusions and argue in favour of the existence of longterm cointegration relationships between economic growth, FDI, exports, the active population and capital investment. III. M ethodological F ramework of the S tudy a) Analysis tool In this article, we use a model based on an augmented neoclassical production function whose general form is : = ( ; ; ) (1). With the following assumptions: Where Y is aggregate output, K is capital, L is labour and X is exports. Exports (X) are not in principle an argument in the neoclassical production function, but their incorporation allows for international factors that affect output, but are not captured by K and L factors. b) Data Sources The data used for the estimation of equation (1) are annual. They come mainly from the World Bank’s databases (World Development Indicators). The period covered is from 1960 to 2022. Global output or GDP is real gross domestic product, capital is the real capital formation, exports are represented by total real exports. All these variables are in constant CFAF. L, labour, represents the total population. All variables are in natural logarithms. c) Methodology In this article, we use time series econometrics, which is based on three steps and consists of determining the degree of integration of each variable. In econometrics, several statistical tests are used to determine the degree of integration of a variable. The tests that will be used in this study are the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. Once the order of integration of the series is known, the next step is to examine the possible presence of cointegration relationships that may exist in the long term between the variables. This analysis will follow the Johansen (1988) cointegration test procedure, which is more efficient than the Engle and Granger (1987) two- step strategy when the sample size is small and the number of variables is large. The third step involves testing for causality between the variables in the model. The so-called sequential test procedure and the non- sequential procedure of Toda and Yamamoto (1995) will be applied. d) Empirical results The implementation of the different stationarity tests for each series led to the results summarised in Table 1 below Table 1: Results of the stationarity tests Variables Differences in level Differences in the first year Conclusions ADF PP ADF PP Ln(Y) 6.432 5.321 -8.542 -8.672** I(1) Ln(K) 2.764 2.531 -9.543** -8.022** I(1) Ln(L) 1.032 17.432 -1.210 -4.327** I(1) Ln(X) 3.658 3.210 -9.512** -9.598** I(1) Source: Author’s results 2022, Note: ** denotes rejection of the null hypothesis at the 5% level. The results of the level stationarity tests indicate that the series Ln(Y), ln(K), Ln(L) and Ln(X) are not stationary at the 5% threshold. In fact, for these series, the ADF and PP test statistics have probabilities greater than 5% and therefore allow us not to reject the null hypothesis of unit root (non-stationarity). The tests carried out on the first difference series allow the null hypothesis of non-stationarity to be rejected for all the series at the 5% threshold. However, for the series ln(L), the ADF test accepts the hypothesis of the presence of a unit root (nonstationarity) whereas the PP test rejects the null hypothesis of non-stationarity; given the effectiveness of the PP test compared to the ADF test, it is appropriate to accept the hypothesis of stationarity for this series in first difference. The presence of at least two non-stationary series leads to the search for the presence of a long-term equilibrium relationship between the variables of the model by the Johansen procedure based on the estimation of a vector autoregressive model by the maximum likelihood method. However, some work has shown that the Johansen test statistic is biased in small samples in the © 2023 Global Journals Volume XXIII Issue III Version I 4 Global Journal of Human Social Science - Year 2023 ( )E Analysis of Agricultural Exports and Economic Growth in Benin