Testing for Group-Speci…c Panel Causality

Methodology

So far, our analysis has shown that economic growth Granger-causes terrorist activity, but not for all Latin American countries. Now we want to analyze for which countries growth matters to terrorism. We divide our country sample into two groups, where one group accounts for the lower middle income (LMI ) countries and the other for the upper middle income (UMI ) countries. As noted above, we expect the causal heterogeneity to depend

on a country’s level of development, where less developed economies are anticipated to be more prone to economic change than more developed countries.14 We argued that in less developed economies a comparatively stronger need for socio-economic progress is likely, so that higher importance ought to be attached to economic variables when, e.g., support for a terrorist organization is considered. Also, given that the level of economic development usually correlates with a country’s level of institutional and state capacity, economic factors ought to matter more strongly to less developed countries. For instance, countries with a low level of institutional capacity can be expected to be more vulnerable to economic shocks as they are less likely to provide e¢ cient means of socio-economic participation (e.g., social welfare systems, education) to reduce related grievances or to introduce sound economic policies to counter recessions.

We test for group-speci…c causality by calculating the residual sum of squares (RSS2;j)

from a model which uses information on the lagged dependent variable, the …xed e¤ects and the lagged independent variable with the exception of the slope coe¢ cient of the cross- sections of interest (i.e., a subset of panel members) j to estimate present values of the dependent variable. With respect to Equation (4.1), this means to calculate this model but to constrain the slope coe¢ cients of sub-sample j to 0 (i.e., (k)_i = 0). Using RSS1 (as

calculated above), we compute our third F -statistic (F3) from:

F3 =

(RSS2;;j RSS1)=(nncp)

RSS1=[N T N (1 + p) ncp]

; (4.4)

where nnc is the number of panel members for which the slope coe¢ cient is constrained to

0 and ncis the number of panel members for which this is not the case. For this part of the

analysis the null hypothesis is that the independent variable does not cause the dependent

1 4_{We follow the most recent income classi…cation provided by the World Bank. The LMI countries in}

our sample are Bolivia, Ecuador, El Salvador, Guatemala, Honduras, Nicaragua, and Paraguay. The UMI countries in our sample are Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Mexico, Panama, Peru, Uruguay, and Venezuela.

variable for subset j. Using the same F -distribution as above, we can assess whether the exclusion of the slope terms of the subset j (indicating either the LMI or UMI countries) in‡uences the explanatory power of the model. If the F3-statistic is signi…cant, the null

hypothesis of non-causality for the respective subset j is rejected. That is, the model loses explanatory power with the exclusion of j, indicating that for this subset the independent variable Granger-causes the dependent variable. If the F3-statistic is insigni…cant, it means

that the exclusion of the respective slope terms does not a¤ect the model’s explanatory power, so that for the respective subset the independent variable does not Granger-cause the dependent variable.

Findings

The …ndings of the group-speci…c tests for non-causality are reported in Table 4.4. In short, the results indicate that economic expansions and contractions swayed terrorist activity only in the LMI but not in the UMI countries. This is consistent with our expectation that economic variables only mattered to terrorism in the less developed countries in our sample.

For the less developed countries in our sample there is evidence that growth in‡u- enced the genesis of terrorism. By comparison, the LMI experienced less economic success and stronger economic ‡uctuations.15 At the same time, they dedicated fewer resources to policy means that would have ameliorated grievances associated with poor economic conditions and performance.16 The comparatively poorer economic performance and in- stitutional framework of the LMI countries makes it plausible that (potential) terrorists and their supporters in these countries were more strongly motivated by socio-economic causes, which also relates to the ostensible goals (redistribution) and ideological motiva-

1 5_{While the average growth rate in the LMI countries is 0.91%, it is 1.91% in the UMI countries. The}

standard deviation of economic growth in the LMI countries is 5.46, but only 4.77 in the UMI countries.

1 6

For instance, between 1980 and 1999 the LMI countries spent only 1.18% of GDP on social security, whereas the UMI countries spent 4.64% of GDP during the same period. See the United Nations Eco- nomic Commission for Latin America and the Caribbean website (http://www.eclac.cl/publicaciones) for the corresponding data.

Lags F3 Test Statistics

Economic growth Granger-causes terrorist attacks for LMI countries t-1 15.399***

t-2 4.5154*** t-3 2.6988***

Economic growth Granger-causes terrorist attacks for UMI countries t-1 0.3866

t-2 0.2808 t-3 0.1134

Economic growth Granger-causes terrorism victims for LMI countries t-1 6.300***

t-2 —

t-3 5.5938***

Economic growth Granger-causes terrorism victims for UMI countries t-1 0.3855

t-2 —

t-3 0.0144

Economic growth Granger-causes terrorism index for LMI countries t-1 10.465***

t-2 5.3142*** t-3 4.5124***

Economic growth Granger-causes terrorism index for UMI countries t-1 0.3637

t-2 0.2584 t-3 0.1373

Notes: LMI = lower middle income; UMI = upper middle income. Classi…cation according to the World Bank. Critical values for F3 based on F -distribution with

Np, NT-N(1+p)-p, df (Hurlin and Venet, 2001). p < 0:01 (indicating rejection of null hypothesis, i.e., non-causality).

tions of many terrorist groups operating in these countries. As in Blomberg et al. (2004a, b), economic marginalization may have provided incentives to resort to violence. The eventual economic success of the LMI countries (even though it was slow to arrive) might have ultimately coincided with higher opportunity costs of violence that impeded terrorism recruitment and caused popular support for terrorism to dwindle. For the LMI countries, we thus concur with other empirical studies that attribute a noticeable role to economic factors in determining terrorism.

However, the group-speci…c causality analysis also o¤ers support for skeptical views towards the terrorism-economy nexus. For the UMI countries we detect no evidence of an e¤ect of growth on terrorism. Compared to the LMI countries, a higher initial level of development, stronger economic performance between 1970 and 2007 and a sounder insti- tutional framework that may have been able to better cushion economic marginalization and crisis may explain why economic performance did not matter strongly to terrorism for this group of countries. Evidently, other (non-economic) factors – which we will discuss below – were more important for the calculus of terrorists in the UMI countries, so that economic growth (or policies that augmented it) did not help to reduce terrorism. For the UMI countries, our results are in line with those empirical studies that consider economic variables to play only a minor role in terrorism.