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Economic consequences of terrorism: Geography

matters

Abstract

Terrorism can impose significant costs on an economy. This paper analyses

the effect of geography on terrorism. In particular, this paper hypothesizes that

a terrorist attack in financial hubs of a country will have significantly higher

economic cost than a similar attack in a remote part of the country.

In particular, we focus on the case study of Pakistan and Net Foreign Direct

Investment (NFDI). We find that terrorism in financial hubs of Pakistan has

imposed a significant cost on NFDI, but similar attacks in remote areas have

had insignificant impacts. This heterogeneity of the geography of terrorism

has long been ignored in the literature, and as such is likely to be a significant

contribution.

Omer Majeed

Australian National University.

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1. Introduction

Terrorism1 can impose significant costs on the economy. One of the major

costs associated with terrorism is the number of lives lost and permanent

health injuries. Some of the other significant costs include reductions in

economic growth (Blomberg et al., 2004, Eckstein and Tsiddon, 2004), Net

Foreign Direct Investment (NFDI) (Enders and Sandler, 1996), trade (Nitsch

and Schumacher, 2004) and tourism (Enders et al., 1992).

This paper argues that the economic costs of a terrorist incident vary by the

geography of the terrorist incident. In particular, we argue that a terrorist

attack in one of the financial hubs of a country will have a significantly higher

impact on the economy than a similar attack in a remote area. For the purpose

of this paper we define a financial hub to be a high economic activity area of

the country.

To the best of our knowledge, previous studies in the literature have not

disaggregated terrorist attacks by location of where they happen within a

country. As such this is identified as a significant contribution to the literature

and is likely to have important policy implications.

Attacks in a financial hub, as opposed to remote areas, are likely to cause

higher economic costs for the following five reasons: i) attacks in a financial

hub would get more media coverage than a remote area would, and as such is

likely to affect consumer and business confidence more; ii)an attack in major

city/economic hub is likely to cause a bigger psychological impact; iii) such an

attack is also likely to disrupt activity of a larger section of the economy; iv)

the financial sector of the economy, including stock exchange and the banking

1We use the definition of Enders and Sandler for terrorism: “Terrorism is the premeditated

use or threat to use violence by individuals or subnational groups to obtain a political or social objective through the intimidation of a large audience beyond that of the immediate victim”. ENDERS, W. & SANDLER, T. 2006. The political economy of terrorism, Cambridge University Press.

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sector, are usually located in the financial hubs of an economy. Given this, an

attack in a financial hub of a country is more likely to reverberate through the

financial sector; and v) an attack inside the financial hub is likely to cause

more infrastructure and property damage. Based on this, the main hypothesis

of this paper is: economic costs of a terrorist incident vary by the geography of

the terrorist incident.

This paper investigates the heterogeneity of the terrorist attacks on economic

costs by analysing the case study of Pakistan. The choice of country for case

study is determined by two factors: i) the country had to experience terrorism

in financial hubs of the country as well as remote areas of the country; and ii)

terrorism incidents had to be prevalent for a long enough period to alter the

economic behaviour of the stakeholders in the country. Terrorism in Pakistan

has occurred all over the country, including remote areas and financial hubs.

In addition, Pakistan has experienced persistent and significant number of

terrorist attacks since 2001. Given these reasons, Pakistan provides a good

study to analyse whether economic costs vary by the location of the terrorist

attack.

To test the main hypothesis that the costs of a terrorist attack vary by

geography we employ a Vector Autoregression (VAR) approach to look at the

interaction between terrorism and NFDI in Pakistan. We choose NFDI for

three reasons. Firstly, analysing NFDI gives this paper a base point to compare

with the growing literature on terrorism and foreign direct investment (FDI)

(Muckley, 2010, Abadie and Gardeazabal, 2008, Enders and Sandler, 1996,

Enders et al., 2006, Bandyopadhyay et al., 2011, Sandler and Enders, 2004,

Mancuso et al., 2010). Secondly, FDI is likely to be sensitive to terrorism

because, ceteris paribus, foreign investors are likely to show risk aversion to

terrorism and divert their capital to safer place. Lastly, FDI is available in

monthly terms since July 2001. Other real variables such as GDP,

consumption and total investment are only available at annual level from 1973

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onwards. An econometric analysis using these variables would yield a

significant degrees of freedom problem, especially for VAR analysis.

The main results of this paper confirm the main hypothesis that the location of

terrorist attack matters. In particular the impulse response functions (IRF)

from VAR show that terrorist attacks in major cities have high and statistically

significant reduction in FDI, but at the same time a similar attack in a remote

area does not affect FDI. According to our estimates, terrorism in economic

hubs of Pakistan reduced NFDI by around 10.7 per cent or around 3.0 billion

US dollars (2009 prices). These results are similar to Enders and Sandler’s

(1996) results on the effect of terrorism in Spain and Greece.

The rest of the paper is organized as follows. Section 2 talks about the

economic reasoning of why geography matters for a terrorist attack, section

gives the background of terrorism in Pakistan, section 3 describes data,

whereas section 4 gives the econometric methodology, section 5 gives the

results, section 7 analyses the accumulated effect of terrorism on NFDI and

section 7 concludes and evaluates policy implications.

2. Economic reasoning

As mentioned before, there are five main reasons why a terrorist attack in an

economic hub, like a major city, would have a higher economic cost than a

similar terrorist attack in a remote area. First, a terrorist attack in a major city

is likely to attract bigger media coverage. This extra media coverage is likely

to dampen both consumer and business confidence. This can be particularly

relevant for foreign investors as it can be hypothesized that FDI has a high

elasticity to terrorism.

Second, an attack in a major city is also likely to have a bigger psychological

impact, as this signals to various stake holders that the state may be weak and

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that the terrorist may be well organized. Such kinds of a signal may force

stake holders to rationally expect future terrorist attacks and as such they

would be forced to alter their behaviour. This may be particularly bad for

foreign investors.

Third, there are more businesses, employees and economic activity in a major

city as opposed to a remote area. As such, disruption in a major city is likely to

cause higher economic costs than disruptions in remote areas.

Fourth, Financial institutions such as stocks markets, banks and other financial

intermediaries tend to gravitate towards financial hubs. A major attack near

these organizations is likely to cause a bigger negative shock to the financial

sector than compared to a similar attack in a distant remote area.

Finally, there are more economic assets in a financial hub. These include

infrastructure, property, and higher human and physical capital. Given this, an

attack in a financial hub is likely to cause higher damage to these assets of an

economy.

For the terrorist, the decision making is rational and involves weighing the

cost and benefit of attacking a financial hub versus a remote area. Attacking a

financial hub gets terrorists more political leverage but at the same time it is

harder and more costly. Hence the terrorists will attack a financial hub if

equation (2.1) holds and attack a remote area if equation (2.2) holds.

Expected (benefit from attacking a financial hub – cost of attack) > 0 (2.1)

Expected (benefit from attacking a remote area – cost of attack) > 0 (2.2)

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3. Background and context

After the terrorist attacks on September 11, 2001, Pakistani military bases and

land routes were used by the US and NATO to attack the Taliban in

Afghanistan. As a consequence, the Taliban saw the government of Pakistan

as a puppet of the US and started retaliating against the people and the state of

Pakistan.

There are several terrorist organizations operating in Pakistan and Afghanistan

that have declared war on the government of Pakistan. Some of these include

Tehreek-e-Taliban Pakistan (TTP), Lashkar-eJhangvi (LeJ), Sipah-e-

Muhammad Pakistan (SMP) , Lashkar-e-Toiba (LeT) and Balochistan

Liberation Army (BLA). These organizations are all religious extremist

organizations. Some of them get funded internally, while some of them get

funding from overseas.

These organizations have successfully attacked across all over Pakistan. These

attacks include major cities like Karachi, Lahore, Islamabad and Rawalpindi.

In addition remote areas of Pakistan and minor cities have also been targeted.

4. Data

For NFDI we use CPEIC data base. We use US GDP deflator to convert

nominal NFDI into real values with 2009 used as the base year. This is

monthly data and the sample period is between July 2001 and December 2011.

This time range was chosen because this was the only time range in which

NFDI data overlapped with terrorism data.

For data base of terrorist attacks we use Global Terrorism Database (GTD).

Data here is available on daily basis, which we aggregated to get to monthly

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figures. To capture terrorism we created a causality list which was the number

of people killed plus number of people wounded in a terrorist attack.

Terrorist attacks were disaggregated by geography into three categories as

following: i) major cities which included Karachi, Lahore, Islamabad and

Rawalpindi. These are the main cities of Pakistan, as well as the financial hubs

and government centres; ii) remote areas included Khyber Pakhtunkhwa (KP),

Balochistan, Pakistani part of Kashmir, Gilgit-Baltistan and tribal agencies on

the Pak-Afg border in the North-West of Pakistan. Based on contribution by

GDP, these regions add very little to the Pakistan’s economy and are

considered remote; and iii) medium zones consisted of the remainder of i and

ii.

Table 4.1 gives summary statistics and figure 4.1 looks at the association

between terrorism in Pakistan and NFDI. There were a total of 14199

casualties due to terrorism between July 2001 and December 2011 for

Pakistan. Of which 9213 were in major cities, 3431 were in remote areas and

the remainder in the medium zone. In addition, we can see that the surge in

terrorism since 2008 has had a significant impact on NFDI, which started to

decline thereafter.

Table 4.1

Variable Obs Mean Std. Dev. Min Max

NFDI 126 234.54 249.75 -15.12 1371.33

Remote Areas 126 27.23 44.69 0.00 243.00

Major City 126 73.12 114.19 0.00 643.00

Medium Zones 126 7.85 26.17 0.00 223.00

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Figure 4.1

5. Methodology

5.1 Unit root and structural break

This section discusses unit root tests and structural break. To test for

stationarity we use the augmented Dicky Fuller test for all of our series. We

use equation (5.1) to test for unit root (Wooldridge, 2012). Our null hypothesis

is that Ho: and Ha: , where yt is the series being tested, and lags

of are put in to clean up serial correlation.

∑ (5.1)

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800

Terrorism Total Pakistan Real NFDI

Terrorism and NFDI US Million Casualties

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Table 5.1 shows the results. All of our terrorism related variables as stationary.

From figure 4.1, we can see that NFDI had an upward trend till the middle of

2008, and there after it started declining. Given this, there appears to be

structural break in the data. This break is taken into account by splitting the

data into two samples, one pre-July 2008 and one post this period. This date

was chosen because it coincided with the decline in NFDI. Table 5.1 shows

that NFDI has I(1) for the whole sample, but once we take into account of the

structural break, it appears to be stationary in our two subsamples.

As a robustness measure, this paper also uses the Phillip-Perrson (Phillips and

Perron, 1988) unit root test on NFDI. The Phillip-Perron test has higher power

than Dicky Fuller test and it takes account of structural breaks, so we don’t

have to split the sample. The Phillip-Perron test also confirms that NFDI is

stationary after we take the structural break into account.

Table 5.1

Dicky Fuller Unit Root Test

Variable Test Statistic

(P value)

No. of lags

Integrated order Sample period Model

Terrorism casualties

Major Cities -5.055 0.000 1 I(0) July 2001 to Dec 2011 Intercept

Remote areas -10.888 0.000 1 I(0) July 2001 to Dec 2011 Intercept

Medium Zone -7.919 0.000 1 I(0) July 2001 to Dec 2011 Intercept

Total Pakistan -4.910 0.000 1 I(0) July 2001 to Dec 2011 Intercept

NFDI -1.951 0.308 6 I(1) July 2001 to Dec 2011 Intercept

NFDI -6.314 0.000 1 I(0) July 2001 to Jun 2008 Intercept + Trend

NFDI -7.800 0.000 1 I(0) July 2008 to Dec 2011 Intercept + Trend

Phillip-Perron Unit Root Test

NFDI -8.056 0.000 1 I(0) July 2001 to Dec 2011 Intercept

A formal test of the structural break using a Chow test shows that there indeed

is a structural break in the NFDI series. The null hypothesis is that there are no

structural breaks in the data. The F-statistic is based on the comparison of the

restricted and unrestricted sum of squared residuals. The date of potential

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break is chosen to coincide with decline of NFDI starting from 2008M08.

Table 5.2 gives the results. F statistics of 13.67 exceeds the critical F value of

3.15 at the 95 per cent level, so the null hypothesis of no structural change can

be rejected.

Table 5.2

Chow Break Point Test

Null Hypothesis: No breaks at specified breakpoints

F-statistic 13.67 Prob. F(3,119) 0.00

Chow Breakpoint Test: 2008M08

5.2 Vector Autoregression

This section talks about the econometric strategy used in identifying the

impact of terrorism on NFDI. This paper employs a VAR to look at the

association between terrorism and NFDI. There are 3 main advantages of

using a VAR analysis: i) It can take into account reverse causality between

variables; ii) by using impulse response functions (IRF) we can evaluate

how a shock in one variable impacts other variables, and whether this impact

is long lasting; and iii) it can be used to calculate total reductions on NFDI

caused by terrorism (Enders and Sandler, 1996). Equations (5.2) and (5.3) lay

out the basic VAR model used.

∑ ∑

(5.2)

∑ ∑

(5.3)

where Terror is the number of casualties in a terrorist incident, NFDI is net

foreign direct investment, D is the dummy variable to take into account the

structural break (taking the value zero before July 2008 and one after wards)

and are the standard VAR error terms. To see if the impact of terrorism

varies by location, we run the above system of equations separately for major

cities, medium zones and remote areas. One advantage of running the above

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regression separately for each type of location is that the system does not run

out of degrees of freedom as quickly and hence with higher number of

observations available gives a more precise estimate of the confidence

intervals around the IRFs. A Cholesky decomposition is used to analyse the

IRFs of the VAR system. Since both equations have identical right-hand-side

variables, ordinary least squares will yield efficient estimates.

We use the likelihood ratio (LR) test to ascertain the number of lags required

for the above system of equations. The LR test selected 12 lags for all systems,

except for the system involving medium zones where it selected 13 lags. As

the NFDI data is not seasonally adjusted, using 12 or more lags also serves the

purpose of controlling for seasonality. For major cities VAR AIC choose 12

lags as well. For other specifications, AIC and BIC always choose lags less

than 12. Given that NFDI data is not seasonally adjusted, AIC and BIC chosen

models maybe mis-specified because they don’t account for seasonality.

The identification assumption is that terrorism does not contemporaneously

react to NFDI. This assumption is based on the fact that terrorists may require

some time to logistically organize themselves. In contrast, NFDI can

contemporaneously react to terrorism. As a robustness measure we test to see

if the ordering of our variables matter.

For robustness measure we also tests to see if the results are sensitive to

specification by putting terrorism in all regions and NFDI in one VAR system.

Our sensitivity analysis shows that the main results of the paper remain valid

after the robustness tests.

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6. Results

6.1 Impulse response functions

By analysing the moving average representation of a VAR2 we can trace out

the dynamic responses of the variables in the VAR system (Enders, 2008).

Figure 6.1 traces out the impacts of terrorist incidents by location on NFDI.

Figure 6.1 confirms our hypothesis that terrorism attacks in major cities have a

higher cost than terrorism in remote area and medium zones. According to

panel A and C in figure 6.1, a shock to terrorism in a remote area and medium

zones has no statistically significant impact on NFDI at the 95 per cent

confidence level. However, a shock in terrorism in a major city has both

economically and statically a negative impact on NFDI. A standardized attack

in a major city decreases NFDI by around $40.94 million 2009 US dollars in

one months’ time. This result is statistically significant at the 95 per cent

level3.

2 VAR results are not produced here for saving space, as there is not much interpretation we

can do with reduced forms. However, these results can be produced on request. 3 All other impacts are statistically insignificant at 95 per cent level.

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Figure 6.1

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order1, remote, NFDI

A. Terrorism in Remote Areas and NFDI

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

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order1, major_city, NFDI

B. Terrorism in Major Cities and NFDI

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

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Another interesting dynamic between terrorism and NFDI is that terrorism

reacts to increased NFDI in major cities. A one standard deviation increase in

NFDI in major cities results in terrorist attacks in one, four and five months on

average after the increase. This is in line with the terrorists’ declared intention

of attacking foreign interests in Pakistan. Some prominent examples of this

include attacks on the Sri Lankan cricket team and the killing of the American

journalist Daniel Pearl.

However, terrorism does not increase in remote areas and medium zones due

to increased NFDI. Part of this may reflect that NFDI is usually concentred in

the main economic hubs of Pakistan and as such, terrorists do not react to

them outside the main financial hubs.

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C. Terrorism in Medium Zone areas and NFDI

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

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Figure 6.2 4

6.2 Granger Causality

Granger causality technique analyses whether the lags of one variable help

predict the other variables in a VAR system. Terrorism in various regions does

not Granger causes NFDI if equation (6.1) holds, and NFDI does not cause

terrorism in various regions if equation (6.2) holds (where p is the total

number of lags in the equation and a2i and b1i are the relevant coefficients in

equations (5.2) and (5.3) ).

a21=a22=…=a2p=0 (6.1)

b11=b12=…=b1p=0 (6.2)

4 Other IRFs were not interesting and as anticipated the IRFs reached zero in a short period of

time. Given this, they were not produced to save space.

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Terrorism reaction to NFDI

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

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From table 6.1 we can see that Terrorism in major cities Granger causes NFDI

and vice versa at the 95 per cent level. It is interesting to note that, at the same

level of significance, NFDI Granger causes terrorism in in remote areas.

However, Terrorism in remote areas only Granger causes NFDI at the 10 per

cent significance level.

Table 6.1

Granger causality test

Equation Excluded Chi square DF Probability > Chi Square

Granger causality test for NFDI and terrorism in remote areas

Terrorism in remote areas NFDI 28.217 12 0.005

NFDI Terrorism in Remote areas 18.986 12 0.089

Granger causality test for NFDI and terrorism in major cities

Terrorism in major cities NFDI 41.839 12 0.000

NFDI Terrorism in major cities 23.633 12 0.023

Granger causality test for NFDI and terrorism in medium zones

Terrorism in medium zones NFDI 12.707 14 0.550

NFDI Terrorism in medium zones 5.6112 14 0.975

6.3 Robustness Test

As a robustness test this paper uses two methods. In the first method we see if

our results are sensitive to the ordering of the variables. Secondly we combine

data from terrorist attacks in all regions into one regression and examine

whether the results are sensitive to specification.

The results remain robust to changes in the ordering of the variables. IRFs

show that terrorism in remote areas and medium zones do not affect NFDI,

while terrorism in major cities decreases NFDI by around $40.9 million (in

2009 US dollars) one month later. In addition terrorism only reacts to increase

in NFDI in major cities.

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Similarly, when we add all forms of terrorism to this VAR, the results still

remain robust5. The only difference is that the impact of terrorism in major

cities on NFDI takes place both instantaneously, and with a one month’s lag.

A one shock of increase in terrorism in major cities causes NFDI to decease by

$29.4 million instantaneously and by $27.3 million with one month’s lag, in

2009 prices.

7 Accumulated effect

Using the VAR analysis we can calculate the accumulated effect of terrorism

on foreign capital in Pakistan. We follow Enders and Sandler (1996) in using

equation (7.1) to determine the law of motion for foreign capital in Pakistan.

(7.1)

Where K is foreign capital and dep is depreciation at time t. We follow Enders

and Sandler in assuming a constant rate of 5 per cent depreciation. To estimate

the total impact of terrorism, we construct a counterfactual NFDI. In

particular, we can use the results from this paper’s VAR to construct what

NFDI for each period would have been had there been no terrorism in

Pakistan. Call this counterfactual net foreign investment ̈ .

Over the entire sample, this paper finds that terrorism in major cities of

Pakistan caused a decline of $3.0 billion in 2009 US dollars, or about

10.7 per cent. These results are similar to Enders and Sandler’s (1996) results

on the effect of terrorism in Spain and Greece.

Figure 7.1 charts actual foreign capital in Pakistan as opposed to the

counterfactual foreign capital without terrorist attacks. We can see a clear

divergence between the two lines. This decline had a significant impact on

Pakistan’s economy. Reduced NFDI can affect the economy through various

5 IRFs given in appendix 1

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channels including lower levels of investment, exports, employment,

technology flows and economic growth.

Figure 7.1

8. Conclusion and policy implications

This paper tests and confirms the hypothesis that terrorism in financial hubs

has a larger impact on the economy than attacks on the remote areas of a

country. In particular, this paper finds that terrorism in major cities has caused

a significant reduction in Pakistan’s foreign capital stock, causing it to

decrease by $3.0 billion in 2009 US dollars, or about 10.7 per cent.

There are two main policy implications from this research and they are as

following: i) terrorists gain more media coverage and impose a bigger cost on

the state by attacking the financial hubs of the country. Given this, financial

hubs are more vulnerable to terrorism and should be better protected; and ii)

terrorists react to foreign presence. In particular we saw that terrorists had the

tendency to increase attacks in major cities due to increased FDI in Pakistan.

0

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10000

15000

20000

25000

30000

35000

Acutal

Counterfactual

Accumulaed Foreign Capital

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It would be incorrect to conclude from this research that security apparatus

should focus on the financial hubs only and ignore remote areas of the

country. If this happens, then the terrorists can use the vacuum to launch

attacks on the main sectors of the economy. The example of how terrorists in a

remote-land-locked country managed to use the vacuum in Afghanistan to

launch attack on the main financial hub of the world is still prominent in every

one’s memory.

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BLOMBERG, S. B., HESS, G. D. & ORPHANIDES, A. 2004. The macroeconomic consequences of terrorism. Journal of Monetary Economics, 51, 1007-1032.

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Appendix 1

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order1, major_city, NFDI

Terrorism in Major Cities and NFDI

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable

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Terrorism in Medium Zone Areas and NFDI

95% CI orthogonalized irf

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Graphs by irfname, impulse variable, and response variable

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order1, remote, NFDI

Terrorism in Remote Areas and NFDI

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable