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Sex Ratio and Terrorist Group Survival

  • Emin Gahramanov , Khusrav Gaibulloev EMAIL logo and Javed Younas
Published/Copyright: April 2, 2024
The B.E. Journal of Economic Analysis & Policy
From the journal The B.E. Journal of Economic Analysis & Policy Volume 24 Issue 2

Abstract

Using data of 638 terrorist groups operating in 92 countries for the period 1970–2016, we examine the relationship between gender imbalance and a resident terrorist group survival. Our empirical design employs alternative models and controls for terrorist group characteristics, base-country influences, and unobservable regional fixed effects. An increase in male-to-female ratio is associated with lower probability of terrorist groups demise, suggesting that countries with a skewed male population are less able to combat terrorism.

Keywords: terrorist group survival; gender imbalance; terrorism; discrete-time hazard models
JEL Classification: D74; C41; H56
Corresponding author: Khusrav Gaibulloev, Department of Economics, American University of Sharjah, Sharjah, 26666, United Arab Emirates, E-mail:
Appendix
Table A1:

Cross-sectional analysis of terrorist group survival.

Variables Lognormal model Cox model
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Sex Ratio 17.201*** 12.469*** 13.535* −13.953*** −10.374** −12.305*
(4.771) (4.827) (7.040) (3.365) (4.107) (7.186)
Left wing −0.692*** −0.572* 0.889*** 0.889***
(0.247) (0.298) (0.240) (0.284)
Nationalist −0.218 −0.380 0.225 0.435
(0.257) (0.267) (0.267) (0.267)
Right wing −1.432*** −1.337*** 1.474*** 1.527***
(0.383) (0.411) (0.355) (0.366)
Share trans. attacks −3.274*** −2.868*** 3.476*** 3.104***
(0.307) (0.335) (0.398) (0.418)
Attack diversity 1.242 1.161 −1.560** −1.554**
(0.834) (0.823) (0.624) (0.647)
EAP 1.024** −1.032***
(0.439) (0.383)
ECA 0.076 −0.305
(0.296) (0.257)
LAC 0.090 −0.196
(0.321) (0.250)
NA 0.331 −0.463
(0.428) (0.386)
SAS 0.575* −0.653*
(0.327) (0.340)
SSA 1.016*** −1.064***
(0.355) (0.360)
N 556 507 507 556 507 507

  1. Significance levels: ***p < 0.01, **p < 0.05, and *p < 0.10. Robust standard errors are in parentheses. Constant is suppressed. For the Cox regressions, coefficients (not hazard ratios) are reported.

Table A2:

Terrorist group failure: including additional controls.

Variables Logit RE CLL RE CLL LPM
Sex Ratio −18.608* −26.460* −17.809* −22.482* −1.154*
(10.769) (13.919) (10.131) (12.29) (0.589)
Left wing 1.335*** 1.511*** 1.290*** 1.340*** 0.067***
(0.434) (0.533) (0.417) (0.466) (0.023)
Nationalist 0.971** 1.226** 0.937** 1.097** 0.041***
(0.399) (0.497) (0.383) (0.439) (0.014)
Right wing 1.840*** 2.212*** 1.734*** 1.942*** 0.132**
(0.587) (0.769) (0.565) (0.648) (0.06)
Share trans. attacks 2.240*** 2.472*** 1.910*** 2.026*** 0.294***
(0.494) (0.662) (0.380) (0.523) (0.070)
Attack diversity −0.941 −0.854 −0.836 −0.761 −0.100**
(0.685) (0.883) (0.627) (0.768) (0.044)
log(GDP/POP) 0.164 0.221 0.126 0.171 0.016
(0.184) (0.214) (0.168) (0.186) (0.012)
log(POP) 0.03 0.089 0.032 0.069 0.008
(0.137) (0.178) (0.126) (0.153) (0.011)
Polity2 −0.018 −0.018 −0.017 −0.017 −0.001
(0.029) (0.034) (0.027) (0.03) (0.002)
Ethnic fractionalization 0.193 0.360 0.151 0.225 0.021
(0.729) (0.865) (0.663) (0.746) (0.051)
EAP −1.645** −1.900** −1.614** −1.760** −0.069**
(0.672) (0.860) (0.643) (0.751) (0.032)
ECA −0.761 −0.602 −0.755* −0.602 −0.045
(0.469) (0.682) (0.441) (0.599) (0.035)
LAC −0.358 −0.028 −0.355 −0.116 0.021
(0.574) (0.772) (0.523) (0.660) (0.049)
NA −0.487 −0.129 −0.521 −0.182 0.023
(0.69) (1.049) (0.638) (0.888) (0.071)
SAS −0.731 −0.830 −0.783 −0.810 0.030
(0.616) (0.792) (0.573) (0.692) (0.038)
SSA −1.711** −2.177** −1.632** −1.894** −0.092*
(0.746) (0.970) (0.693) (0.835) (0.048)
log(Elevation) −0.15 −0.165 −0.146 −0.142 −0.010
(0.181) (0.243) (0.167) (0.206) (0.014)
Tropics 0.232 −0.239 −0.253 −0.210 −0.020
(0.482) (0.56) (0.443) (0.485) (0.035)
Landlock 0.857* 1.016* 0.768* 0.856 0.065*
(0.478) (0.615) (0.424) (0.527) (0.037)
NT 1504 1504 1504 1504 1504

  1. Significance levels: ***p < 0.01, **p < 0.05, and *p < 0.10. Robust standard errors are in parentheses. Constant and duration variables are suppressed. The duration dependence pattern is specified using decade dummies. NT is the sample size. RE, CLL, RE CLL, LPM denote random-effects logit, complementary log–log, random-effects complementary log-log, and linear probability model, respectively.

Table A3:

Terrorist group failure: replacing sex ratio at birth with the ratio of male residents to female residents.

Variables Logit RE CLL RE CLL LPM
Sex Ratio −5.808** −6.269** −4.822** −5.098** −0.372*
(2.600) (2.832) (2.297) (2.434) (0.199)
Left Wing 1.363*** 1.395*** 1.229*** 1.238*** 0.094***
(0.354) (0.371) (0.319) (0.328) (0.024)
Nationalist 0.866*** 0.930*** 0.775*** 0.821*** 0.047***
(0.328) (0.353) (0.299) (0.316) (0.016)
Right Wing 1.464*** 1.515*** 1.116*** 1.133*** 0.123**
(0.488) (0.497) (0.426) (0.423) (0.057)
Share Trans. Attacks 2.859*** 2.957*** 2.173*** 2.246*** 0.476***
(0.339) (0.395) (0.249) (0.299) (0.051)
Attack Diversity −1.249** −1.205* −0.977* −0.928 −0.166***
(0.603) (0.682) (0.531) (0.587) (0.047)
log(GDP/POP) 0.184 0.214 0.131 0.157 0.019
(0.162) (0.162) (0.139) (0.140) (0.013)
log(POP) −0.049 −0.037 −0.032 −0.026 0.001
(0.097) (0.102) (0.086) (0.088) (0.009)
Polity2 −0.021 −0.021 −0.016 −0.016 −0.001
(0.025) (0.026) (0.022) (0.023) (0.002)
Ethnic Fractionalization 0.585 0.660 0.704 0.723 0.044
(0.627) (0.647) (0.538) (0.543) (0.052)
EAP −1.126** −1.173** −0.952** −0.985** −0.062*
(0.496) (0.533) (0.452) (0.467) (0.034)
ECA −0.797** −0.776 −0.724** −0.702* −0.046
(0.390) (0.489) (0.336) (0.406) (0.038)
LAC −0.372 −0.304 −0.286 −0.253 0.008
(0.460) (0.484) (0.386) (0.390) (0.049)
NA −0.364 −0.283 −0.282 −0.241 −0.013
(0.517) (0.697) (0.437) (0.546) (0.067)
SAS −0.651 −0.708 −0.713 −0.725 −0.034
(0.519) (0.553) (0.469) (0.484) (0.039)
SSA −1.316** −1.418** −1.149** −1.205** −0.087*
(0.558) (0.585) (0.507) (0.507) (0.046)
log(Elevation) −0.178 −0.176 −0.194 −0.186 −0.014
(0.163) (0.176) (0.144) (0.149) (0.015)
Tropics −0.321 −0.308 −0.410 −0.378 −0.024
(0.455) (0.421) (0.398) (0.357) (0.037)
Landlock 0.377 0.414 0.336 0.345 0.038
(0.436) (0.455) (0.347) (0.368) (0.037)
NT 1678 1678 1678 1678 1678

  1. Significance levels: ***p < 0.01, **p < 0.05, and *p < 0.10. Robust standard errors are in parentheses. Constant and duration variables are suppressed. The duration dependence pattern is specified using decade dummies. NT is the sample size. RE, CLL, RE CLL, LPM denote random-effects logit, complementary log–log, random-effects complementary log–log, and linear probability model, respectively.

References

Alesina, A., A. Devlesschauwer, W. Easterly, S. Kurlat, and R. Wacziarg. 2003. “Fractionalization.” Journal of Economic Growth 8 (2): 155–94.10.1023/A:1024471506938Search in Google Scholar

Allison, P. D. 1982. “Discrete-Time Methods for the Analysis of Event Histories.” Sociological Methodology 13: 61–98. https://doi.org/10.2307/270718.Search in Google Scholar

Blomberg, S., K. Gaibulloev, and T. Sandler. 2011. “Terrorist Group Survival: Ideology, Tactics, and Base of Operations.” Public Choice 149 (3/4): 441–63. https://doi.org/10.1007/s11127-011-9837-4.Search in Google Scholar

Caruso, R., and E. Gavrilova. 2012. “Youth Unemployment, Terrorism and Political Violence, Evidence from the Israeli/Palestinian Conflict.” Peace Economics, Peace Science and Public Policy 18 (2). https://doi.org/10.1515/1554-8597.1254.Search in Google Scholar

Central Intelligence Agency. 2018. The World Factbook. Washington: Central Intelligence Agency. https://www.cia.gov/the-world-factbook/ (accessed April 2, 2022).Search in Google Scholar

Daxecker, U., and M. Hess. 2013. “Repression Hurts: Coercive Government Responses and the Demise of Terrorist Campaigns.” British Journal of Political Science 43 (3): 559–77. https://doi.org/10.1017/s0007123412000452.Search in Google Scholar

Gaibulloev, K., J. A. Piazza, and T. Sandler. 2024. “Do Failed or Weak States Favor Resident Terrorist Groups’ Survival?” Journal of Conflict Resolution 68 (5): 823–48.10.1177/00220027231183939Search in Google Scholar

Gaibulloev, K., and T. Sandler. 2013. “Determinants of the Demise of Terrorist Organizations.” Southern Economic Journal 79 (4): 774–92. https://doi.org/10.4284/0038-4038-2012.269.Search in Google Scholar

Gaibulloev, K., and T. Sandler. 2014. “An Empirical Analysis of Alternative Ways that Terrorist Groups End.” Public Choice 160 (4): 25–44. https://doi.org/10.1007/s11127-013-0136-0.Search in Google Scholar

Gaibulloev, K., and T. Sandler. 2019. “What We Have Learned about Terrorism Since 9/11.” Journal of Economic Literature 57 (2): 275–328. https://doi.org/10.1257/jel.20181444.Search in Google Scholar

Gaibulloev, K., and T. Sandler. 2023. “Common Myths of Terrorism.” Journal of Economic Surveys 37 (2): 271–301. https://doi.org/10.1111/joes.12494.Search in Google Scholar

Gallup, J. L., A. D. Mellinger, and J. D. Sachs. 2010. Geography Datasets. Harvard Dataverse, V1, UNF:5:SnYwMY387RxYcu3OxaSFgA== [fileUNF].Search in Google Scholar

Gallup, J. L., J. D. Sachs, and A. D. Mellinger. 1999. “Geography and Economic Development.” International Regional Science Review 22 (2): 179–232. https://doi.org/10.1177/016001799761012334.Search in Google Scholar

Hou, D., K. Gaibulloev, and T. Sandler. 2020. “Introducing the Extended Data on Terrorist Groups (EDTG), 1970–2016.” Journal of Conflict Resolution 64 (1): 199–225. https://doi.org/10.1177/0022002719857145.Search in Google Scholar

Huber, L. 2019. “When Civilians Are Attacked.” Journal of Conflict Resolution 63 (10): 2289–318. https://doi.org/10.1177/0022002719835601.Search in Google Scholar

Institute for Economics & Peace, and Global Terrorism Index. 2023. Measuring the Impact of Terrorism. Sydney. http://visionofhumanity.org/resources.Search in Google Scholar

Malkki, L. 2022. “Longevity of Terroris Groups.” In Contemporary Terrorism Studies, edited by D. Muro, and T. Wilson, 238–59. Oxford: Oxford University Press.10.1093/hepl/9780198829560.003.0013Search in Google Scholar

Marshall, M. G., K. Jaggers, and T. R. Gurr. 2019. Polity IV Project: Dataset and Users’ Manual. Vienna: Center for Systemic Peace. Polity IV Project http://www.systemicpeace.org/inscrdata.html (accessed November 9, 2021).Search in Google Scholar

McDermott, R. 2020. “The Role of Gender in Political Violence.” Current Opinion in Behavioral Sciences 34: 1–5. https://doi.org/10.1016/j.cobeha.2019.09.003.Search in Google Scholar

Phillips, B. 2014. “Terrorist Group Cooperation and Longevity.” International Studies Quarterly 58 (2): 336–47. https://doi.org/10.1111/isqu.12073.Search in Google Scholar

Urdal, H. 2006. “A Clash of Generations? Youth Bulges and Political Violence.” International Studies Quarterly 50 (3): 607–29. https://doi.org/10.1111/j.1468-2478.2006.00416.x.Search in Google Scholar

Van San, M. 2015. “Striving in the Way of God: Justifying Jihad by Young Belgian and Dutch Muslims.” Studies in Conflict & Terrorism 38 (5): 328–42. https://doi.org/10.1080/1057610x.2015.1013776.Search in Google Scholar

Venhaus, J. 2010. Why Youth Join al-Qaeda. United States Institute of Peace (May). https://www.usip.org/sites/default/files/resources/SR236Venhaus.pdf (accessed February 10, 2024).Search in Google Scholar

World Bank. 2021. World Development Indicators (WDI). http://databank.worldbank.org/ddp/home.do (accessed November 11, 2021).Search in Google Scholar

World Bank. 2022. World Development Indicators (WDI). http://databank.worldbank.org/ddp/home.do (accessed August 21, 2022).Search in Google Scholar

Younas, J., and T. Sandler. 2017. “Gender Imbalance and Terrorism in Developing Countries.” Journal of Conflict Resolution 37 (2): 271–301.10.1177/0022002715603102Search in Google Scholar
Received: 2023-11-19
Accepted: 2024-03-17
Published Online: 2024-04-02

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/bejeap-2023-0392
Keywords for this article
terrorist group survival; gender imbalance; terrorism; discrete-time hazard models

Articles in the same Issue

  1. Frontmatter
  2. Research Articles
  3. Tradeoffs in the Power of Regulatory Regimes
  4. Pay as You Throw Threshold Tariff: Evidence on the Incentive to Recycle
  5. Industrial Technology Boundary, Product Quality Choice, and Market Segmentation
  6. Human Capital Investments and Family Size in Italy: IV Estimates Using Twin Births as an Instrument
  7. Maternal Labour Supply and School Enrolment Laws: Empirical Evidence from Brazilian Primary School Reforms
  8. Pricing Personalised Drugs: Comparing Indication Value Based Prices with Performance Based Schemes
  9. The Impact of Migration on Productivity: Evidence from the United Kingdom
  10. In the Eye of the Storm: The Disrupted Career Paths of Young People in the Wake of COVID-19
  11. Separating the Accountability and Competence Effects of Mayors on Municipal Spending
  12. Letters
  13. Close But Not Too Close? Optimal Copycat Strategies in the Light of Negative Publicity by the Original Product
  14. Keeping Mobile Firms at Home: The Role of Public Enterprise
  15. The Trouble with Take-Up
  16. Sex Ratio and Terrorist Group Survival
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