Disguised Terrorism Versus Political and Economic Failures- Which Diagnosis Do We Need to Recognize? 205 Countries in Two Decades of Analysis

Identifying the causes of terrorism has been a goal researchers for decades. The evidences and implications are both extremely ambiguous, but also poignant. Dealing with become centerpiece political debates years. Despite that, it always followed by similar identical uncompromising intransigent security measures in different parts world, even if reasons behind acts combine many types human sides, including political, social, security, psychological, cultural, religious dimensions. There lots tremendous feelings, not only victims assailants that believe their unprejudiced continuously able to justify significance use violence. That is why paper started introducing subject reader, terms related phenomena, idea there an economic cost associated this phenomenon. A key challenge understanding defining various multidimensional theoretical practical features extremism, while, at same time trying render Political Economic impacts on societies. With effort help spheres understand roots phenomenon, we thought was necessary bring widest assorted point views roots, failures might lead violence, particular from perspectives, countries. We have added English, French, Spanish Arabic references written native languages.
 
 Empirically, chosen assess drivers Terrorism. measured “Control Corruption” (X4), “Government Effectiveness” (X5), “Regulatory Quality” (X6), “Rule Law” (X7) “Voice Accountability” (X8). determinants used as control variables robustness check they “GDP growth” (X1), per capita” (X2) “Employment Ratio” (X3). Using panel data analysis according GMM technique results indicate all these significant positive effects “Political Stability”. Analysis conducted using annual 205 countries during period 2002 2019.  Robustness checks indicates controlling factors slightly enhanced explanation power, providing R2 0.2498 instead 0.1836 (for first hypothesis), 0.8928 0.8853 second 0.2333 0.1748 third 0.8941 0.8869 fourth hypothesis) 0.9920 0.9821 fifth hypothesis). concludes mainly caused drivers. had slight impact very much power model. Nevertheless, mixing considerations shown predominantly due lack lacunas, most part needs.