Population, Institutions, and Violent Conflicts – How Important is Population Pressure in Violent Resource-Based Conflicts?

Kwami Adanu

doi:10.1515/peps-2023-0004

Article

Population, Institutions, and Violent Conflicts – How Important is Population Pressure in Violent Resource-Based Conflicts?

Kwami Adanu

Published/Copyright: August 1, 2023

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal Peace Economics, Peace Science and Public Policy Volume 29 Issue 3

Abstract

This paper examines the moderating role of institutions in explaining the effects of population density, income, and high-valued natural resources (oil) on violent conflict events. Panel-Corrected Standard Errors and Poisson Pseudo Maximum Likelihood estimators were applied. Results show that population density beyond 2293 persons per square kilometre increases violent conflict events. Further, institutional quality has a moderating effect on violent conflict events – stronger corruption control reduces the positive effect of significant oil production on violent conflict events and weakens the negative effect of per capita income on such events. The results suggest that reducing violent conflict events requires at least three things; (1) keeping population density below 2293 persons per square kilometre, (2) investing in institutional quality improvements, and (3) raising incomes.

Keywords: common factors; institutions; panel; population; violent conflict

JEL Classification: D74; Q01; Q34

Corresponding author: Kwami Adanu, Department of Economics, Ghana Institute of Management and Public Administration (GIMPA), Green Hill Drive, Accra, Ghana, Email: kadanu@gimpa.edu.gh

Appendix A

Table 1:

Summary data on variables.

Variable	Observation	Mean	Standard deviation	Min	Max
Annual events	1509	93.665	662.228	0	13,223
Population density	1509	128.520	219.679	2.103	2017.274
GDP per capita	1509	5665.107	9002.309	187.517	63,251.52
Oil	1509	0.402	0.490	0	1
Oil price	1509	76.269	32.159	20.190	128.010
Regulatory quality	1509	36.284	21.351	0	90.430
Corruption control	1509	33.975	22.050	0	87.204
Rule of law	1509	34.490	21.334	0.469	87.5
Corruption control – Vdem	1504	65.600	21.8	11.3	96.6

Table 2:

Pesaran (2004) test for cross-sectional dependence null: no cross-sectional dependence exists.

Tests	Statistic	p-Value
Annual events	13.394	0.000

Table 3:

Pesaran (2007) panel unit root test null: series is I(1).

Variables	Levels				First difference
	Without trend		With trend		Without trend		With trend
	Statistic	p-Value	Statistic	p-Value	Statistic	p-Value	Statistic	p-Value
Annual events	9.857	1.000	9.747	1.000	−14.091	0.000	−11.250	0.000
GDP per capita	9.627	1.000	11.324	1.000	−8.823	0.000	−7.995	0.000
Population density	16.486	1.000	15.925	1.000	−0.099	0.461	7.887	0.000
Regulatory quality	2.581	0.995	1.812	0.965	−23.542	0.000	−20.557	0.000
Corruption control	4.331	1.000	7.139	1.000	−18.232	0.000	−14.971	0.000
Rule of law	3.263	0.999	4.732	1.000	356.681	0.000	301.311	0.000

Table 4:

Kao and Westerlund panel cointegration tests.

	Kao test for cointegration	Westerlund cointegration test	Westerlund cointegration test
	H_o: no cointegration	H_o: no cointegration	H_o: no cointegration
	Ha: all panels cointegrated	Ha: all panels cointegrated	Ha: some panels are cointegrated
Unadjusted modified Dickey–Fuller t	−1.7577 (0.0394)
Unadjusted Dickey-Fuller t	3.7194 (0.0001)
Variance rank		−2.9865 (0.0014)	−1.8137 (0.0349)

Table 5a:

Results of PCSE and PPML Estimates for annual events of Violent Conflicts (Uses oil production and WGI measure of corruption).

Variables	PCSE		PPML
	(1)	(2)	(1)	(2)
	Annual events	Annual events	Annual events	Annual events
Population density	0.452	0.210	−0.0660***	−0.0555***
	(0.395)	(0.281)	(0.0126)	(0.0112)
Population density square	−0.00104***	−0.00121***	1.53e−05***	1.21e−05***
	(0.000101)	(0.000153)	(3.66e−06)	(3.96e−06)
Population density*GDP per capita	−8.92e−05***	−8.26e−05***	5.29e−06***	5.83e−06***
	(1.16e−05)	(1.83e−05)	(1.49e−06)	(1.47e−06)
Population density*oil	5.157***	4.927***	0.0178**	0.0186**
	(0.545)	(0.851)	(0.00879)	(0.00865)
Population density*reg. quality	−0.0146***		0.000183*
	(0.00462)		(0.000109)
Population density*corrupt.		−0.00405		4.57e−05
control		(0.00285)		(6.19e−05)
GDP per capita	−0.0361***	−0.0368***	−0.00102***	−0.00103***
	(0.00889)	(0.00695)	(0.000325)	(0.000269)
GDP per capita*oil	0.0180***	0.0152***	0.000535	0.000717**
	(0.00692)	(0.00579)	(0.000353)	(0.000364)
GDP per capita*reg. quality	0.000541***		1.17e−05*
	(9.07e−05)		(6.82e−06)
GDP per capita*corrupt. control		0.000637***		9.56e−06***
		(0.000108)		(3.39e−06)
Oil	1729***	1475***	6.574***	6.460***
	(367.5)	(378.0)	(1.338)	(0.967)
Oil*reg. quality	−18.67***		0.00715
	(2.646)		(0.0479)
Oil*corrupt. control		−18.88***		−0.0910**
		(3.051)		(0.0383)
Reg. quality	4.550***		−0.0341
	(0.929)		(0.0452)
Corrupt. control.		1.190***		−0.0224
		(0.445)		(0.0268)
Average_Events	0.956***	0.966***	−0.000578	0.00368
	(0.0255)	(0.00663)	(0.00838)	(0.00461)
Average_GDP per capita	−0.0156	−0.0180	−0.000356	0.00434
	(0.0248)	(0.0127)	(0.00900)	(0.00524)
Average_Reg. quality	−10.77*		−0.789
	(5.594)		(2.196)
Average_Corrupt. control.		1.952		0.492
		(4.254)		(1.651)
Average_Population density	1.038	1.619***	0.0889	−0.0295
	(0.730)	(0.376)	(0.247)	(0.121)
Constant	286.9	−50.93	22.20	−35.29
	(217.6)	(202.6)	(98.57)	(72.12)
Observations	1509	1509	1460	1460

Standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1. Year and Country fixed effects included.

Table 5b:

Results of PCSE and PPML estimates for annual events of violent onflicts (uses oil price and VDEM measure of corruption).

Variables	PCSE		PPML
	(1)	(2)	(1)	(2)
	Annual events	Annual events	Annual events	Annual events
Population density	−1.376***	−3.060***	−0.0524***	−0.0527***
	(0.362)	(0.254)	(0.0110)	(0.0138)
Population density	0.000480***	0.000334***	8.64e−06**	9.07e−06*
	(6.34e−05)	(6.87e−05)	(3.97e−06)	(4.77e−06)
Population density*GDP per capita	−3.60e−05***	1.50e−05***	4.62e−06***	5.32e−06***
	(8.17e−06)	(5.27e−06)	(1.41e−06)	(1.36e−06)
Population density*oil price	0.00274***	0.00282***	3.01e−05***	3.57e−05***
	(0.000676)	(0.000614)	(8.43e−06)	(9.15e−06)
Population density*reg. quality	−0.000454		0.000143*
	(0.00379)		(8.10e−05)
Population density*corrupt. control – vdem		1.865***		−0.00132
		(0.336)		(0.0108)
GDP per capita	−0.0141***	−0.00483***	−0.000480*	−0.000485
	(0.00530)	(0.00112)	(0.000286)	(0.000372)
GDP per capita*oil price	1.45e−06	−1.96e−05*	−1.68e−06*	−1.78e−06**
	(1.06e−05)	(1.05e−05)	(9.24e−07)	(8.71e−07)
GDP per capita*reg. quality	0.000212***		8.30e−06
	(6.68e−05)		(5.71e−06)
GDP per capita*corrupt. control – vdem		0.00651		0.000204
		(0.00532)		(0.000465)
Oil price	−1.502***	2.109**	−0.0191	0.0212
	(0.447)	(1.011)	(0.0134)	(0.0189)
Oil price*reg. quality	0.0294**		0.000412
	(0.0130)		(0.000264)
Corrupt. control – vdem*oil price		−3.815***		−0.0393**
		(1.532)		(0.0188)
Reg. quality	−5.837***		−0.0507*
	(1.494)		(0.0269)
Corrupt. control – vdem		292.0***		2.463
		(106.9)		(3.284)
Average_Reg. quality	0.658		−0.0203
	(6.048)		(2.175)
Average_Events	0.979***	0.995***	0.00151	0.00200
	(0.0254)	(0.00564)	(0.00797)	(0.00382)
Average_GDP per cap.	0.0219	0.0374***	0.000437	0.00116
	(0.0288)	(0.151)	(0.00866)	(0.00466)
Average_Population density	0.891	0.499	0.0705	0.0591
	(0.827)	(0.333)	(0.240)	(0.143)
Average_Corrupt. control – vdem		−446.3***		5.938
		(100.0)		(38.93)
Constant	175.2	73.00	−8.254	−18.66
	(243.3)	(117.3)	(96.45)	(31.06)
Observations	1509	1504	1460	1455

Standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1. Year and Country fixed effects included.

Table 6:

Results of PCSE and PPML estimates for annual events of violent conflicts (uses oil production, &WGI measure of corruption and rule of law).

Variables	PCSE		PPML
	(1)	(2)	(3)	(4)
	Annual events	Annual events	Annual events	Annual events
Population density	0.00341	0.210	−0.0555***	−0.0555***
	(0.315)	(0.281)	(0.0116)	(0.0112)
Population density square	−0.000722***	−0.00121***	2.03e−05***	1.21e−05***
	(0.000170)	(0.000153)	(5.68e−06)	(3.96e−06)
Population density*GDP per capita	−5.71e−05***	−8.26e−05***	4.56e−06***	5.83e−06***
	(1.45e−05)	(1.83e−05)	(1.60e−06)	(1.47e−06)
Population density*oil	4.542***	4.927***	0.0217**	0.0186**
	(0.822)	(0.851)	(0.00904)	(0.00865)
Population density*reg. quality	−0.0238***		−0.000233**
	(0.00463)		(0.000110)
Population density*corrupt. control		−0.00405		4.57e−05
		(0.00285)		(6.19e−05)
GDP per capita	−0.0380***	−0.0368***	−0.000528**	−0.00103***
	(0.00893)	(0.00695)	(0.000230)	(0.000269)
GDP per capita*oil	0.0291***	0.0152***	0.000429	0.000717**
	(0.00693)	(0.00579)	(0.000342)	(0.000364)
GDP per capita*reg. quality	0.000412***		4.03e−08
	(7.49e−05)		(4.26e−06)
GDP per capita*corrupt. control		0.000637***		9.56e−06***
		(0.000108)		(3.39e−06)
Oil	1390***	1475***	5.968***	6.460***
	(369.9)	(378.0)	(1.219)	(0.967)
Oil*reg. quality	−22.15***		−0.0563
	(2.275)		(0.0426)
	(366.4)	(372.1)	(1.137)	(1.024)
Oil*corrupt. control		−18.88***		−0.0910**
		(3.051)		(0.0383)
Reg. quality	3.807***		−0.0253
	(0.817)		(0.0296)
Corrupt. control		1.190***		−0.0224
		(0.445)		(0.0268)
Average_Events	1.014***	0.966***	0.00717	0.00368
	(0.0288)	(0.00663)	(0.0132)	(0.00461)
Average_GDP per capita	0.0495*	−0.0180	0.00728	0.00434
	(0.0287)	(0.0127)	(0.0126)	(0.00524)
Average_Reg. quality	0.202		0.707
	(4.872)		(1.776)
Average_Corrupt. control		1.952		0.492
		(4.254)		(1.651)
Average_Population density	0.218	1.619***	−0.139	−0.0295
	(0.827)	(0.376)	(0.391)	(0.121)
Constant	−119.4	−50.93	−45.55	−35.29
	(253.8)	(202.6)	(83.50)	(72.12)
Observations	1509	1509	1460	1460

Standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1. Year and Country fixed effects included.

Table 7:

Results of negative binomial regression for annual events of violent conflicts.

Variables	(1)	(2)
	Annual events	Annual events
Population density	−0.00231**	−0.00405***
	(0.000997)	(0.000952)
Population density square	2.17e−06***	2.59e−06***
	(6.96e−07)	(7.47e−07)
Population density*GDP per capita	2.43e−07***	1.61e−07**
	(8.02e−08)	(7.90e−08)
Population density*oil	−0.00266**	−0.00369***
	(0.00128)	(0.00117)
Population density *reg. quality	−5.39e−05**
	(2.52e−05)
Population density*corrupt. control		4.24e−06
		(1.81e−05)
GDP per capita	−7.31e−05	−7.08e−05
	(5.54e−05)	(5.55e−05)
GDP per capita*oil	−9.06e−05**	−0.000102**
	(4.28e−05)	(4.43e−05)
GDP per capita*Reg. quality	−8.40e−07
	(7.70e−07)
GDP per capita* corrupt. control		−9.24e−07
		(7.44e−07)
Oil	−0.237	−0.163
	(0.198)	(0.200)
Oil*Reg. quality	0.00668
	(0.00667)
Oil*Corrupt. control		0.00990
		(0.00683)
Reg. Quality	0.00921**
	(0.00436)
Corrupt. control		0.00960**
		(0.00416)
Average_Events	0.00166***	0.00135***
	(0.000398)	(0.000380)
Average_GDP per capita	−0.000300	−0.000352
	(0.000284)	(0.000269)
Average_Reg. quality	0.0577
	(0.0626)
Average_Corrupt. control		0.0561
		(0.0789)
Average_Population density	0.0262***	0.0324***
	(0.00886)	(0.00700)
Constant	−4.826**	−5.225
	(2.420)	(3.425)
Observations	1460	1460

Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. Year and country fixed effects excluded. Regression with Year and country fixed effects is non-convergent.

A. 1 List of countries included in the study

1	Afghanistan	41	Lebanon
2	Albania	42	Lesotho
3	Algeria	43	Liberia
4	Angola	44	Libya
5	Bahrain	45	Madagascar
6	Bangladesh	46	Malawi
7	Benin	47	Malaysia
8	Bosnia and Herzegovina	48	Mali
9	Botswana	49	Mauritania
10	Burkina Faso	50	Montenegro
11	Burundi	51	Morocco
12	Cambodia	52	Mozambique
13	Cameroon	53	Myanmar
14	Central African Republic	54	Namibia
15	Chad	55	Nepal
16	Croatia	56	Niger
17	Cyprus	57	Nigeria
18	Democratic rep of Congo	58	North Macedonia
19	Egypt	59	Pakistan
20	Equatorial Guinea	60	Philippines
21	Eritrea	61	Republic of Congo
22	Ethiopia	62	Russia
23	Eswatini	63	Rwanda
24	Gabon	64	Saudi Arabia
25	Gambia	65	Senegal
26	Ghana	66	Serbia
27	Greece	67	Sierra Leone
28	Guinea	68	South Africa
29	Guinea-Bissau	69	Sri Lanka
30	India	70	Tanzania
31	Indonesia	71	Thailand
32	Iran	72	Togo
33	Iraq	73	Tunisia
34	Israel	74	Turkey
35	Ivory Coast	75	Uganda
36	Jordan	76	Ukraine
37	Kenya	77	United Arab Emirates
38	Kosovo	78	Vietnam
39	Kuwait	79	Yemen
40	Laos	80	Zambia
		81	Zimbabwe

References

Abreo, C., R. Bustillo, and C. Rodriguez. 2021. “The Role of Institutional Quality in the International Trade of a Latin American Country: Evidence from Colombian Export Performance.” Journal of Economic Structures 10 (1): 1–21. https://doi.org/10.1186/s40008-021-00253-5.Search in Google Scholar

Acemoglu, D., L. Fergusson, and S. Johnson. 2020. “Population and Conflict.” The Review of Economic Studies 87 (4): 1565–604. https://doi.org/10.1093/restud/rdz042.Search in Google Scholar

Akinbode, S. O., P. A. Okuneye, and C. O. Onyeukwu. 2022. “Inequality, Population Growth, and Hunger in Sub-saharan Africa.” SN Social Sciences 2 (11): 250. https://doi.org/10.1007/s43545-022-00554-x.Search in Google Scholar

Baltagi, B. H., Q. Feng, and C. Kao. 2012. “A Lagrange Multiplier Test for Cross-Sectional Dependence in a Fixed Effects Panel Data Model.” Journal of Econometrics 170 (1): 164–77. https://doi.org/10.1016/j.jeconom.2012.04.004.Search in Google Scholar

Bas, M. A., and R. Schub. 2017. “The Theoretical and Empirical Approaches to Uncertainty and Conflict in International Relations.” In Oxford Research Encyclopedia of Politics, edited by W. R. Thompson, 177–94. Oxford: Oxford University Press.10.1093/acrefore/9780190228637.013.537Search in Google Scholar

Bazzi, S., R. A. Blair, C. Blattman, O. Dube, M. Gudgeon, and R. Peck. 2022. “The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia.” The Review of Economics and Statistics 104 (4): 764–79. https://doi.org/10.1162/rest_a_01016.Search in Google Scholar

Beck, N., and J. Katz. 1995. “What to Do (And Not to Do) with Time-Series Cross-Section Data.” American Political Science Review 89 (3): 634–47. https://doi.org/10.2307/2082979.Search in Google Scholar

Berman, N., M. Couttenier, D. Rohner, and M. Thoenig. 2017. “This Mine Is Mine! How Minerals Fuel Conflicts in Africa.” The American Economic Review 107 (6): 1564–610. https://doi.org/10.1257/aer.20150774.Search in Google Scholar

Blackwell, M., and M. P. Olson. 2022. “Reducing Model Misspecification and Bias in the Estimation of Interactions.” Political Analysis 30 (4): 495–514. https://doi.org/10.1017/pan.2021.19.Search in Google Scholar

Bleaney, M., and A. Dimico. 2017. “Ethnic Diversity and Conflict.” Journal of Institutional Economics 13 (2): 357–78. https://doi.org/10.1017/s1744137416000369.Search in Google Scholar

Bodea, C., M. Higashijima, and R. J. Singh. 2016. “Oil and Civil Conflict: Can Public Spending Have a Mitigation Effect?” World Development 78: 1–12. https://doi.org/10.1016/j.worlddev.2015.09.004.Search in Google Scholar

Boserup, E. 1965. The Conditions of Agricultural Growth. London: George Allen and Urwin.Search in Google Scholar

Breusch, T. S., and A. R. Pagan. 1980. “The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics.” The Review of Economic Studies 47 (1): 239–53. https://doi.org/10.2307/2297111.Search in Google Scholar

Brückner, M. 2010. “Population Size and Civil Conflict Risk: Is There a Causal Link?” The Economic Journal 120 (544): 535–50. https://doi.org/10.1111/j.1468-0297.2010.02352.x.Search in Google Scholar

Buhaug, H., and J. K. Rød. 2006. “Local Determinants of African Civil Wars 1970–2001.” Political Geography 25 (3): 315–35. https://doi.org/10.1016/j.polgeo.2006.02.005.Search in Google Scholar

Burret, H. T., L. P. Feld, and E. A. Köhler. 2016. “(Un-)Sustainability of Public Finances in ` German Laender: A Panel Time Series Approach.” Economic Modelling 53: 254–65. https://doi.org/10.1016/j.econmod.2015.12.001.Search in Google Scholar

Cavalcanti, T., and Á. Novo. 2005. “Institutions and Economic Development: How Strong is the Relation?” Empirical Economics 30: 263–76. https://doi.org/10.1007/s00181-004-0217-5.Search in Google Scholar

Collier, P., and A. Hoeffler. 2004. “Greed and Grievance in Civil War.” Oxford Economic Papers 56 (4): 563–95. https://doi.org/10.1093/oep/gpf064.Search in Google Scholar

Coxe, S., S. G. West, and L. S. Aiken. 2009. “The Analysis of Count Data: A Gentle Introduction to Poisson Regression and its Alternatives.” Journal of Personality Assessment 91 (2): 121–36. https://doi.org/10.1080/00223890802634175.Search in Google Scholar

Cruz, J. 2011. “Criminal Violence and Democratization in Central America: The Survival of the Violent State.” Latin American Politics and Society 53 (4): 1–33. https://doi.org/10.1111/j.1548-2456.2011.00132.x.Search in Google Scholar

Eberhardt, M. 2012. “Estimating Panel Time-Series Models with Heterogeneous Slopes.” STATA Journal 12 (1): 61–71. https://doi.org/10.1177/1536867x1201200105.Search in Google Scholar

Esty, D. C., J. A. Goldstone, T. R. Gurr, B. Harff, M. Levy, G. D. Dabelko, P. Surko, and A. N. Unger. 1998. State Failure Task Force Report: Phase II Findings. McLean, VA: Science Applications International, for State Failure Task Force.Search in Google Scholar

Farzanegan, M. R., C. Lessmann, and G. Markwardt. 2018. “Natural Resource Rents and Internal Conflicts: Can Decentralization Lift the Curse?” Economic Systems 42 (2): 186–205. https://doi.org/10.1016/j.ecosys.2017.05.009.Search in Google Scholar

Fearon, J., and D. Laitin. 2003. “Ethnicity, Insurgency, and Civil War.” American Political Science Review 97 (1): 75–90. https://doi.org/10.1017/s0003055403000534.Search in Google Scholar

Ferrara, F. 2003. “Why Regimes Create Disorder: Hobbes’s Dilemma during a Rangoon Summer.” Journal of Conflict Resolution 47 (3): 302–25. https://doi.org/10.1177/0022002703047003003.Search in Google Scholar

Gardner, W., E. P. Mulvey, and E. C. Shaw. 1995. “Regression Analyses of Counts and Rates: Poisson, Overdispersed Poisson, and Negative Binomial Models.” Psychological Bulletin 118: 392–404. https://doi.org/10.1037/0033-2909.118.3.392.Search in Google Scholar

Gartzke, E. 1999. “War Is in the Error Term.” International Organization 53 (3): 567–87. https://doi.org/10.1162/002081899550995.Search in Google Scholar

Gartzke, E., and J. J. Zhang. 2015. “Trade and War.” In The Oxford Handbook of the Political Economy of International Trade, edited by L. L. Martin, 419–38. New York: Oxford University Press.Search in Google Scholar

Gupta, S., and N. Kumar. 2021. “Three Decades of Narrow Globalization: Evaluating India’s Exports between 1991 and 2017.” Managerial and Decision Economics 42 (2): 343–59. https://doi.org/10.1002/mde.3239.Search in Google Scholar

Hegre, H., and H. M. Nygård. 2015. “Governance and Conflict Relapse.” Journal of Conflict Resolution 59 (6): 984–1016. https://doi.org/10.1177/0022002713520591.Search in Google Scholar

Hegre, H., and N. Sambanis. 2006. “Sensitivity Analysis of Empirical Results on Civil War Onset.” Journal of Conflict Resolution 50 (4): 508–35. https://doi.org/10.1177/0022002706289303.Search in Google Scholar

Hegre, H., N. W. Metternich, H. M. Nygård, and J. Wucherpfennig. 2017. “Introduction: Forecasting in Peace Research.” Journal of Peace Research 54 (2): 113–24. https://doi.org/10.1177/0022343317691330.Search in Google Scholar

Hegre, H., M. Bernhard, and J. Teorell. 2020. “Civil Society and the Democratic Peace.” Journal of Conflict Resolution 64 (1): 32–62. https://doi.org/10.1177/0022002719850620.Search in Google Scholar

Hobbes, T., and M. Missner. 2016. Thomas Hobbes: Leviathan (Longman library of primary sources in philosophy). New York: Routledge.10.4324/9781315507613Search in Google Scholar

Hoechle, D. 2007. “Robust Standard Errors for Panel Regressions with Cross-Sectional Dependence.” STATA Journal 7 (3): 281–312. https://doi.org/10.1177/1536867x0700700301.Search in Google Scholar

Holcombe, R. 2021. “Contractarian Ideology and the Legitimacy of Government.” Journal of Institutional Economics 17 (3): 379–91. https://doi.org/10.1017/s1744137420000521.Search in Google Scholar

De Hoyos, R. E., and V. Sarafidis. 2006. “Testing for Cross-Sectional Dependence in Panel-Data Models.” STATA Journal 6 (4): 482–96. https://doi.org/10.1177/1536867x0600600403.Search in Google Scholar

Kao, C. 1999. “Spurious Regression and Residual-Based Tests for Cointegration in Panel Data.” Journal of Econometrics 90: 1–44. https://doi.org/10.1016/s0304-4076(98)00023-2.Search in Google Scholar

Kapetanios, G., M. H. Pesaran, and T. Yamagata. 2011. “Panels with Non-stationary Multifactor Error Structures.” Journal of Econometrics 160 (2): 326–48. https://doi.org/10.1016/j.jeconom.2010.10.001.Search in Google Scholar

Kavka, G. S. 1983. “Hobbes’s War of All against All.” Ethics 93 (2): 291–310. https://doi.org/10.1086/292435.Search in Google Scholar

Keohane, R. O. 2019. “Hobbes’s Dilemma and Institutional Change in World Politics: Sovereignty in International Society.” In Whose World Order? Uneven Globalization and the End of the Cold War, edited by H. -H. Holme, and G. Sorensen, 165–86. New York: Routledge.10.4324/9780429267857-9Search in Google Scholar

Kilgour, D. M., and F. C. Zagare. 1991. “Credibility, Uncertainty, and Deterrence.” American Journal of Political Science 35 (2): 305–34. https://doi.org/10.2307/2111365.Search in Google Scholar

Koos, C., and J. Pierskalla. 2016. “The Effects of Oil Production and Ethnic Representation on Violent Conflict in Nigeria: A Mixed-Methods Approach.” Terrorism and Political Violence 28 (5): 888–911. https://doi.org/10.1080/09546553.2014.962021.Search in Google Scholar

Maddala, G. S., and S. Wu. 1999. “A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test.” Oxford Bulletin of Economics & Statistics 61: 631–52. https://doi.org/10.1111/1468-0084.0610s1631.Search in Google Scholar

Malthus, T. R. 1798. An Essay on the Principle of Population. London: Electronic Book.Search in Google Scholar

Mitton, K. 2022. “Natural Resources and Atrocities.” In The Oxford Handbook of Atrocity Crimes. London: Oxford University Press.10.1093/oxfordhb/9780190915629.013.7Search in Google Scholar

Newey, W., and K. West. 1994. “Automatic Lag Selection in Covariance Matrix Estimation.” The Review of Economic Studies 61 (4): 631–53. https://doi.org/10.2307/2297912.Search in Google Scholar

Nwokolo, A. 2022. “Oil Price Shocks and Civil Conflict: Evidence from Nigeria.” The World Bank Economic Review 36 (1): 171–97. https://doi.org/10.1093/wber/lhab010.Search in Google Scholar

Ogwang, T., F. Vanclay, and A. van den Assem. 2019. “Rent-seeking Practices, Local Resource Curse, and Social Conflict in Uganda’s Emerging Oil Economy.” Land 8 (4): 53. https://doi.org/10.3390/land8040053.Search in Google Scholar

Olson, M. 1993. “Dictatorship, Democracy, and Development.” American Political Science Review 87 (3): 567–76. https://doi.org/10.2307/2938736.Search in Google Scholar

Østby, G., R. Nordås, and J. K. Rød. 2009. “Regional Inequalities and Civil Conflict in Sub- Saharan Africa.” International Studies Quarterly 53 (2): 301–24. https://doi.org/10.1111/j.1468-2478.2009.00535.x.Search in Google Scholar

Pesaran, M. H. 2004. “General Diagnostic Tests for Cross Section Dependence in Panels.” In Cambridge Working Papers in Economics No: 0435. Faculty of Economics, University of Cambridge.10.2139/ssrn.572504Search in Google Scholar

Pesaran, M. H. 2007. “A Simple Panel Unit Root Test in the Presence of Cross‐section Dependence.” Journal of Applied Economics 22: 265–312. https://doi.org/10.1002/jae.951.Search in Google Scholar

Petersen-Perlman, J. D., J. C. Veilleux, and A. T. Wolf. 2017. “International Water Conflict and Cooperation: Challenges and Opportunities.” Water International 42 (2): 105–20. https://doi.org/10.1080/02508060.2017.1276041.Search in Google Scholar

Rode, M. 2013. “Do Good Institutions Make Citizens Happy, or Do Happy Citizens Build Better Institutions?” Journal of Happiness Studies 14: 1479–505. https://doi.org/10.1007/s10902-012-9391-x.Search in Google Scholar

Ross, M. L. 2008. “Blood Barrels-Why Oil Wealth Fuels Conflict.” Foreign Affairs 87: 2.10.1080/23739770.2008.11446334Search in Google Scholar

Santos Silva, J., and S. Tenreyro. 2006. “The Log of Gravity.” The Review of Economics and Statistics 88: 641–58. https://doi.org/10.1162/rest.88.4.641.Search in Google Scholar

Sarbahi, A., and O. Koren. 2022. “The Moderating Effect of Democracy on Climate-Induced Social Conflict: Evidence from Indian Districts.” Political Research Quarterly 75 (3): 892–905. https://doi.org/10.1177/10659129211034572.Search in Google Scholar

De Sherbinin, A., D. Carr, S. Cassels, and L. Jiang. 2007. “Population and Environment.” Annual Review of Environment and Resources 32: 345–73. https://doi.org/10.1146/annurev.energy.32.041306.100243.Search in Google Scholar

Sini, S., A. S. Abdul-Rahim, and C. Sulaiman. 2021. “Does Natural Resource Influence Conflict in Africa? Evidence from Panel Nonlinear Relationship.” Resources Policy 74: 102268. https://doi.org/10.1016/j.resourpol.2021.102268.Search in Google Scholar

Taydas, Z., D. Peksen, and P. James. 2010. “Why Do Civil Wars Occur? Understanding the Importance of Institutional Quality.” Civil Wars 12 (3): 195–217. https://doi.org/10.1080/13698249.2010.509544.Search in Google Scholar

Tugcu, C. T. 2018. “Chapter 8 – Panel Data Analysis in the Energy-Growth Nexus (EGN).” In The Economics and Econometrics of the Energy-Growth Nexus, edited by A. N. Menegaki, 255–71. London: Academic Press.10.1016/B978-0-12-812746-9.00008-0Search in Google Scholar

Urdal, H. 2005. “People vs. Malthus: Population Pressure, Environmental Degradation, and Armed Conflict Revisited.” Journal of Peace Research 42 (4): 417–34. https://doi.org/10.1177/0022343305054089.Search in Google Scholar

Verpoorten, M. 2012. “Leave None to Claim the Land: A Malthusian Catastrophe in Rwanda?” Journal of Peace Research 49 (4): 547–63. https://doi.org/10.1177/0022343312437773.Search in Google Scholar

Westerlund, J. 2005. “New Simple Tests for Panel Cointegration.” Econometric Reviews 24 (3): 297–316. https://doi.org/10.1080/07474930500243019.Search in Google Scholar

Yiew, T. H., M. S. Habibullah, S. H. Law, and W. N. W. Azman-Saini. 2016. “Does Bad Governance Cause Armed Conflict.” International Journal of Applied Business and Economic Research 14 (6): 3741–55.Search in Google Scholar

Zergawu, Y., Y. Walle, and J. Giménez-Gómez. 2020. “The Joint Impact of Infrastructure and Institutions on Economic Growth.” Journal of Institutional Economics 16 (4): 481–502. https://doi.org/10.1017/s1744137420000016.Search in Google Scholar

Received: 2023-01-20

Accepted: 2023-07-21

Published Online: 2023-08-01

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/peps-2023-0004

Keywords for this article

common factors; institutions; panel; population; violent conflict