How does terrorism measurement matter of state-level of a country? Evidence from Islamic countries

Abstract

This study attempts to measure the intensity of terrorism in the provinces level of the Islamic economies. The main idea of this study is to quantify the terrorism score at the state level of Islamic countries to gauge the terrorism intensity of that particular state in a particular period of time for that economy. The intuition is based on the logic that the economic cost and intensity terrorism is not uniform within a country. Some areas may have more intensity of terrorism than others in the same territory of a country. The application of the TEIE index (Khan and Yusof in Qual Quant, 2016b. doi:10.1007/s11135-016-0336-z) to Islamic countries (Afghanistan, Iraq and Syria) of the provinces/states confirms that terrorism intensity to measure economic cost is not uniform in the provinces of Islamic countries and it also confirms that geographical dimensions of terrorism are heterogeneous with in one country. Terrorism intensity fluctuates across the provinces of the Islamic countries. The idea of this research study gives a more realistic approach to the policy makers for the curb of terrorism. Anti-terrorism one-fit policy within one country is not appropriate and should be decentralized according to the magnitude of terrorism score at the state level. Furthermore, this study is not only useful for the information of terrorism score of a particular state, but at the same time, it is very important from economic perspective to utilize the scare resource for terrorism measures according to the intensity of terrorism of a state of an economy.

Appendices

Appendix

The following steps are involved in TEIE model (Khan and Yusof 2016b).

Step 1: measurement of terrorist attack intensity

The intensity of any terrorist action in any area can be measured through the number of terrorism incidents, number of people killed, number of injuries, and property damage in a particular locality. Weights are assigned to the mentioned four dimensions of a terrorist attack actions. The GTI methodology approach is used to assign weights to the number of incidents, number of deaths, number of injuries, and property damage in particular state. These four factors of terrorist action have different intensities and magnitudes. The weights are assigned on the basis of the individual intensity of the four factors of a terrorist attack actions.

Step 2: terrorist attack intensity growth rate

At this part of the index, the terrorist attack intensity growth rates of the four dimensions of terrorist attack actions are measured. This growth rate has four sub-steps.

3.1 Terrorist attack incident growth rate

The procedure for obtaining the terrorist attack incident growth rate is

$$\varepsilon = \left( {\frac{{\varepsilon_{i} - \varepsilon_{0} }}{{\varepsilon_{f} - \varepsilon_{0} }}} \right)\quad 0 \le \varepsilon \le 1$$

where (ε _i ) = the actual number of terrorist incidents that occurred in a particular state in a particular year, (ε _f ) = the highest number of incidents in a particular state during the study period, and (ε ₀) = the lowest number of terrorist incidents that occurred in a state during the study period.

3.2 Terrorist attack death rate

The mathematical formula for obtaining the terrorist attack death rate is

$$\kappa = \left( {\frac{{\kappa_{i} - \kappa_{0} }}{{\kappa_{f} - \kappa_{0} }}} \right)\quad 0 \le \kappa \le 1$$

where ($\kappa$) = the terrorist attacks death rate, ($\kappa_{i}$) = the actual number of deaths in a terrorist attack in a particular state in a particular year, ($\kappa_{f}$) = the highest number of deaths in a particular state during the study period, and ($\kappa_{0}$) = the lowest number of deaths in a state during the study period.

3.3 Terrorist attack injury growth rate

$$\varPsi = \left( {\frac{{\varPsi_{i} - \varPsi_{0} }}{{\varPsi_{f} - \varPsi_{0} }}} \right)\quad 0 \le \varPsi \le 1$$

where ($\varPsi_{i}$) = the actual number of injured persons in a terrorist attack in a particular state in a particular year, ($\varPsi_{f}$) = the highest number of injured people in a particular state during the study period, and ($\varPsi_{0}$) = the lowest number of people injured in a terrorist attack in a state during the study period.

3.4 Terrorist attack property damage rate

The terrorist attack property damage rate is expressed as

$$\mu = \left( {\frac{{\mu_{i} - \mu_{0} }}{{\mu_{f} - \mu_{0} }}} \right)\quad 0 \le \mu \le 1$$

where (μ) = the terrorist attack property damage rate, (μ _i ) = the actual number of property damage in a particular state in a particular year, (μ _f ) = the highest number of property damage in a particular state during the study period, and, (μ ₀) = the lowest number of property damage in a terrorist attack at the state level during the study period.

Step 3

At this stage, principal component analysis (PCA) is used to assign different relative weights to the indices of the four dimensions of a terrorist attack. The PCA is a technique of abstracting data from its original position into reduced form to show as much of the information from the observed data (Rao 1964; Lee et al. 2005). Three main steps are involved in PCA. First, the covariance matrix is calculated. Second, eigenvalue decomposition is performed on the same covariance matrix. The following equation is using to capture the variance from the data.

$$PC = \alpha_{1} (x_{11} ) + \alpha_{2} (x_{12} ) + \cdots + \alpha_{np} (x_{p} )$$

where the symbol $\alpha_{np}$ (principal component) is the regression coefficient of the component variable. Third, the most significant component among all components is selected. In obtaining the percentage value of the contribution of components, the eigenvalue is divided by the sum of all eigenvalues.

There are two main advantages of the PCA technique. First it overcomes the issue of outlier of data and second it measures the underlying latent information on variables in a block. The relative weights assigned on the basis of PCA explain the relative intensities among the four terrorism dimensions’ intensity rates during a particular period of time.

The final step of the methodology of TEIE index involves adding the weighted indices of the four dimensions of a terrorist attack and calculating the TEIE score for a particular state in a particular year. The TEIE indicator (Khan and Yusof 2016a, b) value for a particular year ranges from 0 to 1, where 0 represents the least terrorism vulnerability of a state of a country in measuring economic cost and 1 represents the highest vulnerability of terrorism in measuring the terrorism intensity (score) of a state of a country.