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Food Security

, Volume 8, Issue 5, pp 999–1010 | Cite as

From global to local, food insecurity is associated with contemporary armed conflicts

  • Ore KorenEmail author
  • Benjamin E. Bagozzi
Original Paper

Abstract

Food security has attracted widespread attention in recent years. Yet, scientists and practitioners have predominately understood food security in terms of dietary energy availability and nutrient deficiencies, rather than in terms of food security’s consequential implications for social and political violence. The present study offers the first global evaluation of the effects of food insecurity on local conflict dynamics. An economic approach is adopted to empirically evaluate the degree to which food insecurity concerns produce an independent effect on armed conflict using comprehensive geographic data. Specifically, two agricultural output measures – a geographic area’s extent of cropland and a given agricultural location’s amount of cropland per capita – are used to respectively measure the access to and availability of (i.e., the demand and supply of) food in a given region. Findings show that food insecurity measures are robustly associated with the occurrence of contemporary armed conflict.

Keywords

Food security Civil war Resource scarcity Agriculture Social 

Notes

Acknowledgements

Compliance with ethical standards

Author contributions

Ore Koren developed the theory for the paper and conducted primary analysis. Benjamin Bagozzi constructed datasets for the paper and conducted secondary analysis. Ore Koren and Benjamin Benjamin wrote the paper.

Data and materials availability

All replication data and files can be found on Harvard’s Dataverse.

Competing interest

The authors declare no competing interests.

Supplementary material

12571_2016_610_MOESM1_ESM.doc (108 kb)
These supplemental materials proceed in three parts. First, a selection of alternative robustness specifications, sequentially addressing concerns over (i) the global sample used in the primary analysis, (ii) the effects of food and agricultural imports, and (iii) excessive control variables and their effects on missing data are presented and discussed below. This is followed by a table of summary statistics for all independent, dependent, and control variables (Table S6). Finally, a map of the global PRIO-Grid structure, which is described in the main paper and which underpins our analysis, is presented in Figure S1. Note that due to the specificity of the data and the small size of the cells analyzed here (0.5 decimal degree x 0.5 decimal degree), this map appears opaque with latitude and longitude lines crossing it. To see the specificity of the cells one must therefore substantially increase focus when viewing this map. Turning to the robustness specifications, one can first note that many of the cell-years within the global grid sample analyzed in the primary paper correspond to advanced developed democracies such as the United States, Sweden, and Japan. These countries generally exhibit a mix of political and economic traits that preclude them from experiencing most forms of large-scale political violence such as civil war. Though some advanced industrialized democracies might nevertheless occasionally experience civil or international conflict on their soil, it is reasonable to view these countries, and cropland areas therein, as being nearly “immune” to the food security-conflict dynamics discussed above. The primary logit models control for several sources of this immunity through the inclusion of variables such as democracy, and cell-level economic output per capita. Furthermore, the presence of conflict-immune grid cells, if anything, should bias any findings for the aforementioned food security measures towards zero, rather than increase any potential for false positives. Even so, Table S1 seeks to more fully account for these potential conflict-immune grid-cells within the analysis through the use of a split population (i.e., zero-inflated) logit model. Using a single system of two estimating equations, this model accounts for “inflated” (i.e., conflict-immune) cells by explicitly estimating each cell’s propensity for conflict immunity within an “inflation stage” that includes a collection of the most intuitive predictors thereof (polity, log gross cell product per capita, and log population), and then probabilistically down-weighting the leverage of conflict-immune cases within the full (i.e., outcome stage) logistic analysis. This approach is consistent with related conflict studies in the literature (Bagozzi et al. 2015), and reveals that in this case, even after taking these cell-specific inflation propensities into account and down-weighting these cells accordingly, the positive and significant findings for cropland and negative and significant findings for ln cropland pc remain. An alternate approach for addressing concerns over the inclusion of theoretically irrelevant country cells within the primary analysis is to drop those cells entirely. Building on the evidence that climatic variation is more likely to affect warmer regions (e.g. Miguel et al. 2004; Burke et al. 2009; Hsiang and Meng 2014; FAO 2008), as well as the concentration of conflict presented in Figure 1, Table S2 re-estimates the four logistic regression models presented in Table 1 for a subsample consisting of only tropical regions, i.e., regions above 25th latitude south and below 25th latitude north. These models reveal that the positive and significant findings for cropland and negative and significant findings for ln cropland pc remain even when colder regions, which might have biased the sample, are removed from analysis. A list of the countries that are partly or wholly located in the Tropics is provided in Table S7. Table S3 re-estimates the four logistic regression models presented in Table 1 while controlling for (lagged) country-year measures of agricultural imports and food imports, each measured as a share of a given country’s total merchandise imports. As mentioned in the main paper, food imports have been widely shown to have political instability-inducing effects (Bellemare 2015; Hendrix and Haggard 2015; Weinberg and Bakker 2015). Additionally, the increase in ``land grabbing” for the purpose of non-crop (e.g., ethanol) or exports production since 2008 (De Schutter 2011) has potential implications for this study’s findings, although 2008 is the final year in the sample. While the theory and analyses discussed earlier focus on local food availability and access, ensuring that these findings are robust to varying levels of food and agricultural resources available from imports is critical. Table S3 evaluates these concerns, and demonstrates that the findings for cropland and ln cropland pc are robust to models that control for food and agricultural imports. The specifications presented in the main paper also include a large number of control variables. While these controls allow for the most comprehensive assessment of food security possible, they also increase missingness levels due to listwise deletion, in addition to potentially introducing biases of their own (Achen 2005). To further ensure the robustness of the findings reported in the main paper, two smaller (“Baseline” and “Medium”) model specifications are estimated for Models 1-4. The Baseline specifications (Table S4) include only basic socioeconomic controls in addition to cropland measures to show that the effect of the latter is independent of cell level socioeconomic conditions. In the Medium specification models (Table S5), climate related factors are added to show that the effect of cropland and cropland per capita is independent of both socioeconomic conditions and the effect of climatic variation. As Tables S4 and S5 clearly illustrate, the findings presented in the main paper hold in every case. Supplementary Figure 1 (DOC 108 kb)
12571_2016_610_MOESM2_ESM.doc (95 kb)
Supplementary Table 1 (DOC 95 kb)
12571_2016_610_MOESM3_ESM.doc (88 kb)
Supplementary Table 2 (DOC 87 kb)
12571_2016_610_MOESM4_ESM.doc (90 kb)
Supplementary Table 3 (DOC 89 kb)
12571_2016_610_MOESM5_ESM.doc (76 kb)
Supplementary Table 4 (DOC 75 kb)
12571_2016_610_MOESM6_ESM.doc (80 kb)
Supplementary Table 5 (DOC 80 kb)
12571_2016_610_MOESM7_ESM.doc (86 kb)
Supplementary Table 6 (DOC 86 kb)
12571_2016_610_MOESM8_ESM.doc (68 kb)
Supplementary Table 7 (DOC 68 kb)

References

  1. Achen, C. H. (2005). Let’s put garbage-can regressions and garbage-can probits where they belong. Conflict Management Peace Science, 22, 327–339.CrossRefGoogle Scholar
  2. Bagozzi, B. E., Hill, D. W., Moore, W. H., & Mukherjee, B. (2015). Modeling two types of peace: the zero-inflated ordered Probit (ZiOP) model in conflict research. Journal of Conflict Resolution, 59(4), 728–752.CrossRefGoogle Scholar
  3. Bannon I & P Collier, eds. (2004) Natural resources and violent conflict: options and actions. The World Bank.Google Scholar
  4. Barrett, C. B. (2010). Measuring food insecurity. Science, 327, 825–828.CrossRefPubMedGoogle Scholar
  5. Beger, Andreas, Jacqueline H. R. DeMeritt, Wonjae Hwang, and Will H. Moore. 2011. “The split population logit (SPopLogit): modeling measurement bias in binary data.” Working Paper. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1773594 (accessed May 20, 2011).
  6. Bellemare, M. F. (2015). Rising food prices, food price volatility, and social unrest. American Journal of Agricultural Economics, 97, 1–21.Google Scholar
  7. Bontemps S, Defourny P, & Van Bogaert E (2009) Globcover 2009 Products Description and Validation Report. European Space Agency.Google Scholar
  8. Brinkman H, & Hendrix CS (2011) Food Insecurity and Violent Conflict: Causes, Consequences, and Addressing the Challenges. Occasional Paper no. 24, World Food Programme.Google Scholar
  9. Buhaug, H. (2010). Climate not to blame for African civil wars. Proceedings of the National Academy of Sciences, 107, 16477–16482.CrossRefGoogle Scholar
  10. Buhaug, H., Gates, S., & Lujala, P. (2009). Geography, rebel capability, and the duration of civil conflict. Journal of Conflict Resolution, 53, 544–569.CrossRefGoogle Scholar
  11. Burke, M., Miguel, E., Satyanath, S., Dykem, J., & Lobell, D. (2009). Warming increases the risk of war in Africa. Proceedings of the National Academy of Sciences, 106, 20670–20674.CrossRefGoogle Scholar
  12. Collier, P., & Hoeffler, A. (2005). Resource rents, governance, and conflict. Journal of Conflict Resolution, 49, 625–633.CrossRefGoogle Scholar
  13. De Schutter, O. (2011). How not to think of land-grabbing: three critiques of large-scale investments in farmland. Journal of Peasant Studies, 38, 249–279.CrossRefGoogle Scholar
  14. Detges, A. (2014). Close-up on renewable resources and armed conflict: the spatial logic of pastoralist violence in northern Kenya. Political Geography, 42, 57–65.CrossRefGoogle Scholar
  15. EarthStat (2015) Food Versus Fuel and Feed. Global Landscape Initiative, Institute on The Environment University of Minnesota and University of British Columbia. http://www.earthstat.org (accessed 10 November, 2015)
  16. Ember, C. R., Skoggarda, I., Adema, T. A., & Faas, A. J. (2014). Rain and raids revisited: disaggregating ethnic group livestock raiding in the Ethiopian-Kenyan border region. Civil Wars, 16, 300–327.CrossRefGoogle Scholar
  17. Food and Agriculture Organization of the United Nations (FAO) (2008). Climate change and food security: a framework document. Rome: Food and Agriculture Organization of the United Nations.Google Scholar
  18. Fearon, J. D., & Laitin, D. D. (2003). Ethnicity, insurgency, and civil war. The American Political Science Review, 97, 75–90.CrossRefGoogle Scholar
  19. Fjelde, H., & Hultman, L. (2014). Weakening the enemy: a disaggregated study of violence against civilians in Africa. Journal of Conflict Resolution, 58, 1230–1257.CrossRefGoogle Scholar
  20. Gleditsch, N. P., Wallensteen, P., Eriksson, M., Sollenberg, M., & Strand, H. (2002). Armed conflict 1946-2001: a new dataset. Journal of Peace Research, 39, 615–637.CrossRefGoogle Scholar
  21. Hegre, H., & Sambanis, N. (2006). Sensitivity analysis of empirical results on civil war onset. Journal of Conflict Resolution, 50, 508–535.CrossRefGoogle Scholar
  22. Hendrix, C. S., & Brinkman, H. (2013). Food security and conflict dynamics. Stability, 2, 1–18.CrossRefGoogle Scholar
  23. Hendrix, C. S., & Haggard, S. (2015). Global food prices, regime type, and urban unrest in the developing world. Journal of Peace Research, 52, 143–157.CrossRefGoogle Scholar
  24. Hendrix, C. S., & Salehyan, I. (2012). Climate change, rainfall, and social conflict in Africa. Journal of Peace Research, 49, 35–50.CrossRefGoogle Scholar
  25. Henk, D. W., & Rupiya, M. R. (2001). Funding defense: challenges of buying military capability. Carlisle, PA: Sub-Saharan Africa. U.S. Army War College Strategic Studies Institute.Google Scholar
  26. Hsiang, S. M., & Meng, K. C. (2014). Reconciling disagreement over climate-conflict results in Africa. Proceedings of the National Academy of Sciences, 111, 2100–2103.CrossRefGoogle Scholar
  27. Kalyvas, S. N. (2004). The urban bias in research on civil war. Security Studies, 13, 160–190.CrossRefGoogle Scholar
  28. Kalyvas, S. N. (2006). The logic of violence in civil war. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  29. Kastner, T., Rivas, M. J. I., Koch, W., & Nonhebel, S. (2012). Global changes in diets and the consequences for land requirements for food. Proceedings of the National Academy of Sciences, 109, 6868–6872.CrossRefGoogle Scholar
  30. Koubi, V., Bernauer, T., Kalbhenn, A., & Spilker, G. (2012). Climate variability, economic growth, and civil conflict. Journal of Peace Research, 49, 113–127.CrossRefGoogle Scholar
  31. Kress, M. (2002). Operational logistic: the art and science of sustaining military operations. Norwell, MA: Kluwer Academic Publishers.CrossRefGoogle Scholar
  32. Marshall MG, Jaggers K, & Gurr TR (2013) Polity IV project: political regime characteristics and transitions, 1800–2012. Technical Report.Google Scholar
  33. Messer E, & Cohen MJ (2006) Conflict, Food Insecurity, and Globalization. FCND Discussion Paper 206, International Food Policy Research Institute.Google Scholar
  34. Miguel, E., Satyanath, S., & Sergenti, E. (2004). Economic shocks and civil conflict: an instrumental variables approach. Journal of Political Economy, 112, 725–753.CrossRefGoogle Scholar
  35. Nordhaus, W. D. (2006). Geography and macroeconomics: new data and new findings. Proceedings of the National Academy of Sciences, 103, 3150–3517.CrossRefGoogle Scholar
  36. O’Loughlin, J., Witmer, F. D. W., Linke, A. M., Laing, A., Gettelman, A., & Dudhia, J. (2012). Climate variability and conflict risk in East Africa, 1990–2009. Proceedings of the National Academy of Sciences, 109, 18344–18349.CrossRefGoogle Scholar
  37. Paarlberg, R. L. (2000). The global food fight. Foreign Affairs, 79, 24–38.Google Scholar
  38. Prunier, G. (2008). Darfur: a twenty-first century genocide (Third ed.). Ithaca, NY: Cornell University Press.Google Scholar
  39. Raleigh, C., & Kniveton, D. (2012). Come rain or shine: an analysis of conflict and climate variability in East Africa. Journal of Peace Research, 49, 51–64.CrossRefGoogle Scholar
  40. Rowhani, P., Degomme, O., Guha-Sapir, D., & Lambin, E. F. (2011). Malnutrition and conflict in East Africa: the impacts of resource variability on human security. Climate Change, 105, 207–222.CrossRefGoogle Scholar
  41. Scheffran, J., Brzoska, M., Kominek, J., Link, P. M., & Schilling, J. (2012). Climate change and violent conflict. Science, 336, 869–871.CrossRefPubMedGoogle Scholar
  42. Singer, J. D., Bremer, S., & Stucky, J. (1972). Capability distribution, uncertainty, and major power war. In B. Russett (Ed.), Peace, war, and numbers (pp. 1820–1965). Beverly Hills CA: Sage.Google Scholar
  43. Theisen, O. M. (2012). Climate clashes? Weather variability, land pressure, and organized violence in Kenya, 1989–2004. Journal of Peace Research, 49, 81–96.CrossRefGoogle Scholar
  44. Theisen, O. M., Gleditsch, N. P., & Buhaug, H. (2013). Is climate change a driver of armed conflict? Climatic Change, 117, 613–625.CrossRefGoogle Scholar
  45. Tollefsen, A. F., Strand, H., & Buhaug, H. (2012). PRIO-GRID: a unified spatial data structure. Journal of Peace Research, 49, 363–374.CrossRefGoogle Scholar
  46. Urdal, H. (2005). People vs. Malthus: population pressure, environmental degradation, and armed conflict revisited. Journal of Peace Research, 42, 417–434.CrossRefGoogle Scholar
  47. US Department of State (2010). National Security Strategy of the United States. Office of the President of the United States. DC: Washington.Google Scholar
  48. Weinberg, J., & Bakker, R. (2015). Let them eat cake: food prices, domestic policy and social unrest. Conflict Management Peace Science, 32, 309–326.CrossRefGoogle Scholar
  49. World Bank (2015) World Development Indicators. Washington, D.C.: The World Bank (producer and distributor). http://data.worldbank.org/data-catalog/world-development-indicators
  50. World Health Organization (2015) Trade, Foreign Policy, Diplomacy and Health: Food Security. http://www.who.int/trade/glossary/story028/en/ (accessed 12 June, 2015).
  51. Worldwatch Institute (2013). World Population, Agriculture, and Malnutrition. .http://www.worldwatch.org/node/554 (accessed 5 May 2015
  52. Wucherpfennig, J., Weidman, N. B., Giardin, L., Cederman, L.-E., & Andreas, W. (2011). Politically relevant ethnic groups across space and time: Introducing the GeoEPR dataset. Conflict Management Peace Science, 20(10), 1–15.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht and International Society for Plant Pathology 2016

Authors and Affiliations

  1. 1.Department of Political ScienceUniversity of MinnesotaMinneapolisUSA
  2. 2.Department of Political Science and International RelationsUniversity of DelawareNewarkUSA

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