Population, Institutions, and Violent Conflicts – Shedding New light on an Old Conundrum
Knowledge on the prevention and management of violent conflicts remains in a state of flux as understanding of the triggers, severity, and trajectory of violent conflicts remains shallow. Malthus long predicted a link between population and conflicts. According to Malthus, population expansion leads to population-resource supply imbalances, which leads to conflict. Holding the Malthusian prediction true suggests that, beyond some critical population level, conflicts and violence can only be expected to increase with population until increasing violence and mortality regulate population density figures to the relatively peaceful end of the conflict cycle. To the contrary, a good number of empirical studies have shown no linkage, or a weak linkage at best, between population and conflict. To date, there is no generally acceptable guidance on how population pressure affects violent conflicts, and how such conflicts may be managed. Gaining understanding of what delays the Malthusian population-conflict prediction from materializing helps in increasing the spread of peaceful zones across the world through the development of an environment that supports peace. Neo-malthusians argue that technological growth and income are responsible for failure of the Malthusian conflict prediction thus far. That is, population pressure elicits innovative and technological response which delays realization of the Malthusian prediction. This in fact is a restatement of the Boserup (1965) position on how pressure on resources in agricultural dependent societies leads to innovation and diversification, which increase productivity. That technological growth and income reduce the risk of violent conflicts is understandable. Institutional quality may however have even more significant impact on violent conflicts. Unfortunately, Institutional quality has been largely ignored in research efforts to explain violent conflict. Institutions, being the rules of society, must be fundamental to the maintenance of peace. In particular, they should serve as a bulkwalk against factors that promote conflict. Research on institutional quality over the past 30 – 40 years shows that institutions are critical not only for explaining economic development disparities across countries, but for explaining violent crime rate, and even happiness. Given what institutions are by definition, and what they have been shown to accomplish in various spheres of economic and social life of people, it follows, that institutional quality will be a reliable factor in explaining violent conflicts. To explore the role of institutions in violent conflicts more deeply, and throw more light on how population pressure really relates to conflicts, this study uses Panel-Corrected Standard Error (PCSE) and Poisson Pseudo Maximum Likelihood (PPML) estimators to estimate the impact of institutional quality variables, income, population pressure, and oil production on violent conflicts across 81 countries.
Steps to reproduce
Dataset for the study comprise country level data on conflicts, population density, oil production, institutional quality, as well as interactions of the key variables in the data (except country and year effect variables) with the institutional quality measures. The conflict data covers the period, 1996 – 2018 and is taken from the Armed Conflict Location and Events Data (ACLED) database. The data is panel and covers 81 countries with a sample size of 1509. The list of countries covered by the data cuts across the world. This list is determined by data availability. ACLED receives financial support from the Bureau of Conflict and Stabilization Operations at the United States Department of State, the Dutch Ministry of Foreign Affairs, the German Federal Foreign Office, the Tableau Foundation, the International Organization for Migration, and The University of Texas at Austin in compiling this data. The data is thus quite reliable for research purposes. Conflict data from this database is divided by the author into three categories namely; outright wars involving battles and explosions (grade 1), random violent attacks by armed groups causing displacement of civilians through abductions, sexual violence, looting and property destructions (grade 2), and protests and riots (grade 3). Protests and riots are largely peaceful protests with occasional violent confrontations with the police. This data for this study is focused on outright wars since outright wars have more widespread damaging effects on life and property with some of these effects spilling over across borders. Data on institutional quality is taken from the World Bank’s worldwide governance indicators database. The institutional quality variables used are regulatory quality ratio (rqr) and control of corruption ratio (ccr). These key institutional variables are expected to have direct effects on human conduct as far as creating a conflict-conducive environment is concerned - corruption for instance has been hypothesized in several studies as a leading cause of conflicts. The list of countries by oil production, as compiled from the U.S. Energy Information Administration database for 2019 is used to measure the impact of oil wealth on conflicts. The dummy variable for an oil producing country is 1 if the country is a significant oil producer (at least 100,000 barrels a day), and zero otherwise. Population density data came from world population review, USA while GDP per capita (2010 constant USD) is taken from WDI (World Bank). The data shows one dependent variable (annual incidence), and five independent variables. Data on the annual incidence variable ranges from 0 to 13223, while population density ranges from 2.103 to 2017. Population density is measured as the number of people per square kilometers, while per capita GDP is measured in 2010 constant US dollars. The institutional variables are measured on a scale of 0-100.