The Impacts of Oil Price Shocks on Tourism Receipts for Selected Middle East and North Africa (MENA) Countries: Do Structural Breaks Matter?

Abstract

One of the short comings in the tourism literature is that research on the oil price-tourism receipts nexus is limited. However, the available studies, to the best of our knowledge, provide limited evidence on the negative effect of oil prices on tourism receipts. Nevertheless, the related literature did not consider the structural breaks in the analysis, which proven to be important in the empirical work. As such, in this paper, we study the oil price-tourism receipts nexus for selected MENA countries in the presence of structural breaks. This is done by adopting the autoregressive distributed lags (ARDL) bounds test and incorporating the structural breaks. The findings show that the bounds test provide evidence of a long-run relationship between tourism receipts and oil prices after integrating structural breaks into the ARDL model for most countries.

The Impact of Corruption on Human Development in Egypt

“…gives strong results even if the sample is small and some of the independent variables are endogenous (Iskandar & Saragih, 2018;Kisswani et al, 2019;Pesaran et al, 2001).…”

“…Where ∆ is the first difference; m, n, q, r and s are the lag length and is the residual term. And where , / , , represent the long run coefficients, and , , , , represent the short run coefficients (Kisswani et al, 2019).…”

“…To investigate the existence of a long-run relationship (cointegration), we continue to use the ARDL bounds test, which is the F-test, with the null hypothesis: there is no long-run relationship among the variables, in opposition to the alternative hypothesis that there is a long run relationship (Iskandar & Saragih, 2018;Kisswani et al, 2019).…”

“…If the computed F is larger than the upper bound I(1), we reject the null hypothesis therefore, there is a long run relationship (cointegration); but if the F-statistic is less than the lower bound I(0), we cannot reject the null hypothesis, then there is no long run relationship (cointegration) among the variables. Lastly, if the computed F-statistic lies between the two bounds, the result is inconclusive (Iskandar & Saragih, 2018;Kisswani et al, 2019;Türsoy, 2017) .…”

“…Table 3 demonstrates the error correction coefficient CointEq(-1), which determines the speed of adjustment of the variables to the long-run equilibrium. Therefore, this coefficient should be negative and significant; and this is indicated in Table 3 as CointEq(-1) equals -0.502011, which means that 50% of the deviation from equilibrium in the short run will be adjusted annually until reaching the long run equilibrium (Kisswani et al, 2019;Türsoy, 2017). As for the long run effects, the results are reported in Table 4 which shows that the effect of corruption remains significant and negative in the long run, as expected, and also its coefficient value (0.014) is greater than the short run (0.003).…”