Economics consequences of terrorism: Geography matters


Published on

Published in: News & Politics
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Economics consequences of terrorism: Geography matters

  1. 1. Economic consequences of terrorism: Geography matters Omer Majeed Panel :Professor Prema-chandra Athukorala Professor Robert Breunig
  2. 2. Main results and hypothesis • Terrorism can impose significant costs on an economy. This paper analyses the effect of geography on terrorism. In particular, this paper hypothesises that a terrorist attack in the financial hubs of a country will have significantly higher economic costs than a similar attack in a remote part of the country. • In particular, this paper focuses on the case study of Pakistan and Net Foreign Direct Investment (NFDI), finding 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. 2
  3. 3. Outline • • • • • • • Background and literature review Why geography matters Pakistan and Terrorism Data Methodology Results Conclusions and policy implications 3
  4. 4. Definition • This paper uses 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. • Other definitions include that of UN and state department. 4
  5. 5. Literature review • Terrorism can impose significant economic costs. Most important is the number of lives lost. • 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). 5
  6. 6. Why geography matters • This paper argues that the economic costs of a terrorist incident vary by the geography of the terrorist incident. • In particular, this paper argues 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, a financial hub is defined to be a high economic activity area of the country. 6
  7. 7. Why geography matters • Five main reasons • 1) 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. 7
  8. 8. Why geography matters • 2) An attack in a major city is also likely to have a bigger psychological impact, as this signals to various stakeholders that the state may be weak and that the terrorist may be well organised. Such kinds of signals may force stakeholders 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. 8
  9. 9. Why geography matters • 3) There are more businesses, employees and economic activity in a major city, compared to a remote area. As such, disruption in a major city is likely to cause higher economic costs than disruptions in remote areas. • 4) Financial institutions such as stockmarkets, banks and other financial intermediaries tend to gravitate towards financial hubs. A major attack near these organisations is likely to cause a bigger negative shock to the financial sector. 9
  10. 10. Why geography matters • 5) Finally, there are more economic assets in a financial hub. These include infrastructure, property, and higher human and physical capital. 10
  11. 11. Why geography matters • 5) Finally, there are more economic assets in a financial hub. These include infrastructure, property, and higher human and physical capital. 11
  12. 12. Decision making by terrorists • For the terrorist, the decision making is rational and involves weighing the costs and benefits 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 more difficult and more costly • 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) 12
  13. 13. Background and context for Pakistan • 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. • These organisations 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. 13
  14. 14. 14
  15. 15. Background and context for Pakistan • There are several terrorist organisations operating in Pakistan and Afghanistan that have declared war on the government of Pakistan. Some of these include Tehreek-e-Taliban Pakistan (TTP), LashkareJhangvi (LeJ), Sipah-e-Muhammad Pakistan (SMP) , Lashkar-e-Toiba (LeT) and the Balochistan Liberation Army (BLA). Most of these organisations are religious extremist organizations. Some of them receive internal funding, while some of them receive funding from overseas. 15
  16. 16. Background and context for Pakistan • This paper chooses 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. • Data availability. 16
  17. 17. Data • For a database of terrorist attacks this paper uses Global Terrorism Database (GTD). • For NFDI this paper uses CPEIC database, using the 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. • To capture terrorism I created a causality list which was the number of people killed plus number of people wounded in a terrorist attack. 17
  18. 18. Data 18
  19. 19. Data: structural break and stationarity • Structural break in NFDI series, using Chow test. 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. • DF and Phillip-Perrson confirm that all series are stationary. 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 19
  20. 20. Data: Regions • 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; 20
  21. 21. • ii) remote areas included Khyber Pakhtunkhwa (KP), Balochistan, the Pakistani part of Kashmir, GilgitBaltistan and tribal agencies on the Pakistan-Afghan 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. • 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. 21
  22. 22. Data 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 22
  23. 23. Methodology: Vector Autoregression • Reasons: • i) It can take into account reverse causality between variables and is widely used in the literature; • 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, i.e. look at long term and short effects; and • iii) it can be used to calculate total reductions on NFDI caused by terrorism (Enders and Sandler, 1996). 23
  24. 24. Methodology: Vector Autoregression 24
  25. 25. Methodology: Vector Autoregression • Likelihood ratio (LR) was used to get lag length. It chose 12 lags, which takes account of seasonality. • Results robust to AIC. These criteria have better small sample properties. 25
  26. 26. Results: Impulse response functions A. Terrorism in Remote Areas and NFDI order1, remote, NFDI 40 0 -40 0 1 2 3 4 5 6 7 8 step 95% CI orthogonalized irf Graphs by irfname, impulse variable, and response variable 26
  27. 27. Results: Impulse response functions B. Terrorism in Major Cities and NFDI order1, major_city, NFDI 40 0 -40 0 1 2 3 4 5 6 7 8 step 95% CI orthogonalized irf Graphs by irfname, impulse variable, and response variable 27
  28. 28. Results: Impulse response functions • A standardised 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 level. • All other impacts are statistically insignificant at 95 per cent level. 28
  29. 29. Results: Impulse response functions C. Terrorism in Medium Zone areas and NFDI order1, medium_zone, NFDI 40 0 -40 0 1 2 3 4 5 6 7 8 step 95% CI orthogonalized irf Graphs by irfname, impulse variable, and response variable 29
  30. 30. Results: Impulse response functions Terrorism reaction to NFDI order1, NFDI, major_city 40 20 0 -20 0 1 2 3 4 5 6 7 8 step 95% CI orthogonalized irf Graphs by irfname, impulse variable, and response variable 30
  31. 31. Results: Impulse response functions • 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. 31
  32. 32. Results: 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. • Our results remain robust to these two tests. 32
  33. 33. Results: Accumulated effect 33
  34. 34. Results: Accumulated effect 34
  35. 35. Results: Accumulated effect • 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. 35
  36. 36. Conclusion and policy implications • 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 this paper demonstrates that terrorists had the tendency to increase attacks in major cities due to increased FDI in Pakistan. 36
  37. 37. Conclusion and policy implications • 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. 37
  38. 38. Questions? 38