Public Support for UN Missions and Attacks on Peacekeepers: Evidence From the Democratic Republic of the Congo
-
Burak Giray
Abstract
UN peacekeepers are often targeted by rebel groups. The violence they experience hinders the functionality of peacekeeping operations, constituting a major problem for the UN. What conditions make these attacks more likely? How does the local support for UN peacekeeping operations affect the violence against peacekeepers? Using an original data set that includes local trust sentiments towards MONUSCO’s peacekeepers in the Democratic Republic of the Congo from October 2014 to December 2020, this study finds that peacekeepers are attacked more often when they lack local support. In the absence of local support, peacekeepers become vulnerable as they can no longer gather intelligence about rebel activities from the locals and the increased distrust for peacekeepers gives propagandist benefits to rebel groups. This study suggests to the UN that it needs to employ strategies in peacekeeping operations that would legitimize the presence of peacekeepers and cultivate more strengthened civil-military relations.
Appendix A: Graph Appendix
This section provides additional descriptive information from the data. Table A1 presents the descriptive statistics, in which the number of observation, mean, standard deviation as well as minimum and maximum values of each variable is provided.
Descriptive statistics.
| Statistic | N | Mean | St. dev. | Min | Pctl (25) | Pctl (75) | Max |
|---|---|---|---|---|---|---|---|
| Peacekeeper fatality | 75 | 1.653 | 2.424 | 0 | 0.5 | 2 | 17 |
| Peacekeeper fatality t−1 | 75 | 1.640 | 2.425 | 0 | 0.5 | 2 | 17 |
| Trust sentiments (as %) | 75 | 0.141 | 0.088 | 0.000 | 0.092 | 0.165 | 0.500 |
| Peacekeeper size (logged) | 75 | 9.774 | 0.104 | 9.547 | 9.726 | 9.838 | 9.956 |
| Troop size (logged) | 75 | 9.679 | 0.108 | 9.439 | 9.641 | 9.740 | 9.877 |
| Police size (logged) | 75 | 7.133 | 0.091 | 6.984 | 7.069 | 7.216 | 7.256 |
| Observers (logged) | 75 | 5.733 | 0.435 | 5.075 | 5.269 | 6.157 | 6.229 |
| Troop-providers | 75 | 55.267 | 1.388 | 53 | 54 | 56 | 59 |
| Total violence t−1 | 75 | 246.600 | 250.253 | 5 | 93 | 312.5 | 1240 |
| Time since mission’s onset | 75 | 89.000 | 21.794 | 52 | 70.5 | 107.5 | 126 |
Figure A1 demonstrates the frequency of tweets on MONUSCO peacekeepers in the DRC from October 2014 to February 2022 (the main data is right censored on December 2020 due to data missingness on other variables). The first ever tweet on MONUSCO peacekeepers is posted on October 2, 2014. In the first month, there are only 26 tweets while the highest number of tweets (239) is posted in April 2021.
Count of tweets on MONUSCO peacekeepers.
Figures A2 and A3 are the word clouds that show the most frequent words in English and French tweets.
Word cloud of the tweets in English.
Word cloud of the tweets in French.
Figure A4 presents the distribution of trust sentiments in tweets for MONUSCO peacekeepers. As can be seen in the figure, the trust for peacekeepers could show a big difference from one time period to another.
Trust sentiments for MONUSCO peacekeepers.
Similarly, Figure A5 displays the distributions of positive and negative sentiments in tweets for MONUSCO peacekeepers. The figure shows that there is no significant difference between positive and negative sentiments. It, therefore, wipes out the concerns related to whether Twitter users tweet more when they are disappointed by MONUSCO peacekeepers.
Negative and positive sentiments for MONUSCO peacekeepers.
Next, Figure A6 compares the ratio of trust sentiments that is calculated from Twitter Data to the percentages of confidence in MONUSCO, acquired from a survey data entitled “Peacebuilding and Reconstruction Surveys” (Vinck et al. 2019). The survey has been conducted twice a year since 2013. It provides information about how the respondents in the DRC perceive MONUSCO as a security actor. The blue line in the plot represents the respondents’ confidence in MONUSCO (as of %) to ensure security, and the red line represents the trust sentiments calculated using the tweets on MONUSCO peacekeepers. While the two lines show difference in magnitude, the trends are generally similar in terms of direction. The figure, therefore, is helpful in addressing concerns related to the representativeness of Twitter data.
Survey data versus Twitter data.
The verified Twitter account of MONUSCO (@MONUSCO) provides detailed information on the daily activities of MONUSCO peacekeepers, and clears up any concern regarding the language barrier between UN peacekeepers and the locals. Figure A7, for instance, demonstrates that non-French speaking Nepalese peacekeepers gather with local women to increase awareness among them about gender-based violence and feminine health. The examples of military-civil interactions can be expanded. The tweets of @MONUSCO further inform us that MONUSCO peacekeepers visit schools and villages regularly to distribute food and clean water, inform the locals about their mandates, and even organize football matches with villagers.
Nepalese peacekeepers interact with locals.
Figure A8 demonstrates four examples of intelligence-gathering from villagers and local actors at various time points (2013, 2016, 2021, and 2022). One can come across more of such examples with a closer examination of the verified Twitter account of MONUSCO. It can be interpreted from these tweets that MONUSCO peacekeepers can gather intelligence from the local population or local actors such as village chiefs and doctors either directly or with the help of interpreters. The frequency of such intelligence-gathering patrols might wipe out any concerns that villagers are not knowledgeable about the rebel groups.
Examples of intelligence-gathering from villagers and local actors.
Appendix B: Supplementary Empirical Results
This section provides additional empirical analyses. To begin with, I first check whether the relationship between trust sentiments and peacekeeper fatalities is affected by the outlier, December-2017. After the outlier is omitted from the sample, no evidence of overdispersion is detected. For this reason, I used Poisson Regression in four of the models reported in Table B1.
Omitting outliers – poisson regression.
| DV: peacekeeper fatality | ||||
|---|---|---|---|---|
| No clustered SEs | Clustered SEs | |||
| Peacekeeper fatality t−1 | −0.007 | −0.007 | ||
| (0.044) | (0.034) | |||
| Trust sentiments (as %) | −2.925* | −2.918* | −2.925 | −2.918* |
| (1.605) | (1.602) | (1.779) | (1.773) | |
| Troop size | 1.697 | 1.671 | 1.697 | 1.671 |
| (2.685) | (2.688) | (3.266) | (3.208) | |
| Police size | −1.188 | −1.168 | −1.188 | −1.168 |
| (1.193) | (1.201) | (1.654) | (1.696) | |
| Observers | 1.079 | 1.069 | 1.079 | 1.069 |
| (0.740) | (0.743) | (0.742) | (0.752) | |
| Troop-providers | −0.110 | −0.113 | −0.110 | −0.113 |
| (0.080) | (0.083) | (0.079) | (0.086) | |
| Total violence t−1 | −0.114 | −0.111 | −0.114 | −0.111 |
| (0.119) | (0.120) | (0.139) | (0.141) | |
| Time since mission’s onset | 0.039* | 0.039* | 0.039* | 0.039 |
| (0.022) | (0.022) | (0.025) | (0.025) | |
| Constant | −10.273 | −9.914 | −10.273 | −9.914 |
| (30.929) | (31.010) | (42.587) | (41.854) | |
|
|
||||
| Observations | 74 | 74 | 74 | 74 |
| Akaike Inf. Crit. | 252.017 | 253.994 | 252.017 | 253.994 |
-
* p < 0.1; ** p < 0.05; *** p < 0.01.
The table presents the findings from Poisson Regression with and without clustered standard errors. The findings are robust in three of the four models, and trust sentiments loses its statistical significance only in the third model.
In Table B2, I check for the issue of overfitting. Excluding Troop size, Police size and Observers from the models, I instead include Peacekeeper size, which is the sum of all types of UN personnel. As seen in the table, trust sentiments is found to have statistical significance in the expected direction in the models with clustered standard errors.
Addressing overfitting concerns – negative binomial regression.
| DV: peacekeeper fatality | ||||
|---|---|---|---|---|
| No clustered SEs | Clustered SEs | |||
| Peacekeeper fatality t−1 | −0.018 | −0.018 | ||
| (0.054) | (0.032) | |||
| Trust sentiments (as %) | −3.015 | −3.042 | −3.015* | −3.042* |
| (1.920) | (1.916) | (1.725) | (1.716) | |
| Peacekeeper size | −0.108 | −0.229 | −0.108 | −0.229 |
| (3.862) | (3.863) | (3.348) | (3.319) | |
| Troop-providers | −0.205* | −0.213* | −0.205* | −0.213* |
| (0.107) | (0.111) | (0.116) | (0.120) | |
| Total violence t−1 | −0.097 | −0.091 | −0.097 | −0.091 |
| (0.145) | (0.146) | (0.127) | (0.128) | |
| Time since mission’s onset | 0.011 | 0.011 | 0.011 | 0.011 |
| (0.019) | (0.019) | (0.017) | (0.016) | |
| Constant | 12.735 | 14.413 | 12.735 | 14.413 |
|
|
(40.107) |
(40.242) |
(36.644) |
(36.373) |
| Observations | 75 | 75 | 75 | 75 |
| Akaike Inf. Crit. | 268.133 | 270.043 | 268.133 | 270.043 |
-
* p < 0.1; ** p < 0.05; *** p < 0.01.
Next, I evaluate the existence of reverse causality in Table B3. In an OLS model, I regress trust sentiments on the lagged peacekeeper fatality. Based on the results from the linear regression analyses, no evidence of reverse causality is detected.
Reverse causality – linear regression.
| DV: Trust Sentiments (as %) | ||||
|---|---|---|---|---|
| No clustered SEs | Clustered SEs | |||
| Peacekeeper fatality t−1 | −0.001 | −0.0003 | −0.001 | −0.0003 |
| (0.004) | (0.004) | (0.002) | (0.002) | |
| Peacekeeper size | −0.274 | −0.274 | ||
| (0.290) | (0.202) | |||
| Troop size | −0.228 | −0.228 | ||
| (0.267) | (0.178) | |||
| Police size | −0.094 | −0.094 | ||
| (0.117) | (0.116) | |||
| Observers | 0.081 | 0.081 | ||
| (0.072) | (0.060) | |||
| Troop-providers | −0.004 | −0.004 | −0.004 | −0.004 |
| (0.008) | (0.008) | (0.006) | (0.006) | |
| Total violence t−1 | −0.008 | −0.013 | −0.008 | −0.013 |
| (0.011) | (0.012) | (0.014) | (0.016) | |
| Time since mission’s onset | −0.001 | 0.0004 | −0.001 | 0.0004 |
| (0.001) | (0.002) | (0.001) | (0.001) | |
| Constant | 3.179 | 2.801 | 3.179 | 2.801 |
|
|
(2.998) |
(3.050) |
(2.212) |
(2.181) |
| Observations | 75 | 75 | 75 | 75 |
| Adjusted R2 | −0.019 | −0.027 | −0.019 | −0.027 |
-
* p < 0.1; ** p < 0.05; *** p < 0.01.
Finally, I replace trust sentiments with anger sentiments to check whether I can obtain the opposite results of trust sentiments (Table B4). As expected, contrary to the previous findings, the anger sentiments is found to be positively associated with peacekeepers’ fatality in the models where I report clustered standard errors.
Anger sentiments – negative binomial regression.
| DV: peacekeeper fatality | ||||
|---|---|---|---|---|
| No clustered SEs | Clustered SEs | |||
| Peacekeeper fatality t−1 | −0.013 | −0.013 | ||
| (0.054) | (0.031) | |||
| Anger sentiments (as %) | 4.619 | 4.667 | 4.619* | 4.667* |
| (3.027) | (3.025) | (2.715) | (2.727) | |
| Troop size | 0.949 | 0.868 | 0.949 | 0.868 |
| (3.486) | (3.486) | (3.228) | (3.191) | |
| Police size | −0.235 | −0.195 | −0.235 | −0.195 |
| (1.571) | (1.583) | (1.760) | (1.802) | |
| Observers | 1.311 | 1.285 | 1.311* | 1.285* |
| (0.984) | (0.987) | (0.721) | (0.721) | |
| Troop-providers | −0.198* | −0.205* | −0.198* | −0.205* |
| (0.103) | (0.106) | (0.115) | (0.120) | |
| Total violence t−1 | −0.144 | −0.137 | −0.144 | −0.137 |
| (0.151) | (0.153) | (0.139) | (0.139) | |
| Time since mission’s onset | 0.042 | 0.041 | 0.042* | 0.041* |
| (0.028) | (0.028) | (0.024) | (0.023) | |
| Constant | −7.047 | −5.979 | −7.047 | −5.979 |
|
|
(40.019) |
(40.059) |
(40.304) |
(39.769) |
| Observations | 75 | 75 | 75 | 75 |
| Akaike Inf. Crit. | 270.955 | 272.902 | 270.955 | 272.902 |
-
* p < 0.1; ** p < 0.05; *** p < 0.01.
References
Autesserre, S. 2014. Peaceland: Conflict Resolution and the Everyday Politics of International Intervention. Cambridge: Cambridge University Press.10.1017/CBO9781107280366Suche in Google Scholar
Balcells, L. 2011. “Continuation of Politics by Two Means: Direct and Indirect Violence in Civil War.” Journal of Conflict Resolution 55 (3): 397–422, https://doi.org/10.1177/0022002711400865.Suche in Google Scholar
Bashir, S., S. Bano, S. Shueb, S. Gul, A. A. Mir, R. Ashraf, and N. Noor. 2021. “Twitter chirps for Syrian people: Sentiment analysis of tweets related to Syria Chemical Attack.” International Journal of Disaster Risk Reduction 62 (102397), https://doi.org/10.1016/j.ijdrr.2021.102397.Suche in Google Scholar
Bove, V., and A. Ruggeri. 2016. “Kinds of Blue: Diversity in UN Peacekeeping Missions and Civilian Protection.” British Journal of Political Science 46 (3): 681–700, https://doi.org/10.1017/s0007123415000034.Suche in Google Scholar
David Mason, T., M. Gurses, P. T. Brandt, and J. Michael Quinn. 2011. “When Civil Wars Recur: Conditions for Durable Peace After Civil Wars.” International Studies Perspectives 12 (2): 171–89, https://doi.org/10.1111/j.1528-3585.2011.00426.x.Suche in Google Scholar
Diez, D. M., C. D. Barr, and M. Cetinkaya-Rundel. 2019. OpenIntro Statistics, 4th ed. Boston: OpenIntro.Suche in Google Scholar
Dorn, A. W. 2009. “Intelligence-led Peacekeeping: The United Nations Stabilization Mission in Haiti (MINUSTAH), 2006–07.” Intelligence and National Security 24 (6): 805–35, https://doi.org/10.1080/02684520903320410.Suche in Google Scholar
Dorussen, H., and T.-I. Gizelis. 2013. “Into the Lion’s Den: Local Responses to UN Peacekeeping.” Journal of Peace Research 50 (6): 691–706, https://doi.org/10.1177/0022343313484953.Suche in Google Scholar
Doyle, M. W., and N. Sambanis. 2000. “International Peacebuilding: A Theoretical and Quantitative Analysis.” American Political Science Review 94 (4): 779–801, https://doi.org/10.2307/2586208.Suche in Google Scholar
Duursma, A. 2018. “Information Processing Challenges in Peacekeeping Operations: A Case Study on Peacekeeping Information Collection Efforts in Mali.” International Peacekeeping 25 (3): 446–68, https://doi.org/10.1080/13533312.2018.1446757.Suche in Google Scholar
Duursma, A. 2019. “Obstruction and Intimidation of Peacekeepers: How Armed Actors Undermine Civilian Protection Efforts.” Journal of Peace Research 56 (2): 234–48, https://doi.org/10.1177/0022343318800522.Suche in Google Scholar
Fjelde, H., L. Hultman, and S. Lindberg Bromley. 2016. “Offsetting Losses: Bargaining Power and Rebel Attacks on Peacekeepers.” International Studies Quarterly 60 (4): 611–23, https://doi.org/10.1093/isq/sqw017.Suche in Google Scholar
Fortna, V. P. 2004. “Does Peacekeeping Keep Peace? International Intervention and the Duration of Peace after Civil War.” International Studies Quarterly 48 (2): 269–92, https://doi.org/10.1111/j.0020-8833.2004.00301.x.Suche in Google Scholar
Gigova, R. 2016. 4 Killed, 14 Injured in Anti-UN Protest in Central African Republic. CNN World, October 24, 2016. Also available at https://www.cnn.com/2016/10/24/world/anti-un-protests-central-african-republic.Suche in Google Scholar
Gladstone, R., and A. Cowell. 2017. At Least 15 U.N. Peacekeepers Are Killed in Congo. The New York Times, December 8. Also available at https://www.nytimes.com/2017/12/08/world/africa/congo-un-peacekeepers-killed.html.Suche in Google Scholar
Gohdes, A. R. 2015. “Pulling the Plug: Network Disruptions and Violence in Civil Conflict.” Journal of Peace Research 52 (3): 352–67, https://doi.org/10.1177/0022343314551398.Suche in Google Scholar
Gökçe, O. Z., and E. Hatipoglu. 2021, April. Syrian Refugees, Public Attitudes, Policy Areas and Political Parties in Turkey: A Systematic Analysis of Twitter Data. Giza: Economic Research Forum (ERF).Suche in Google Scholar
Gordon, G. M., and L. E. Young. 2017. “Cooperation, Information, and Keeping the Peace: Civilian Engagement with Peacekeepers in Haiti.” Journal of Peace Research 54 (1): 64–79, https://doi.org/10.1177/0022343316682063.Suche in Google Scholar
Hegre, H., L. Hultman, and H. M. Nygård. 2019. “Evaluating the Conflict-Reducing Effect of UN Peacekeeping Operations.” The Journal of Politics 81 (1): 215–32, https://doi.org/10.1086/700203.Suche in Google Scholar
Howard, L. M. 2019. Power in Peacekeeping. Cambridge: Cambridge University Press.10.1017/9781108557689Suche in Google Scholar
Hultman, L. 2009. “The Power to Hurt in Civil War: The Strategic Aim of RENAMO Violence.” Journal of Southern African Studies 35 (4): 821–34, https://doi.org/10.1080/03057070903313194.Suche in Google Scholar
Hultman, L., K. Jacob, and M. Shannon. 2013. “United Nations Peacekeeping and Civilian Protection in Civil War.” American Journal of Political Science 57 (4): 875–91, https://doi.org/10.1111/ajps.12036.Suche in Google Scholar
Ilyas, S. H. W., Z. T. Soomro, A. Anwar, H. Shahzad, and U. Yaqub. 2020. “Analyzing Brexit’s Impact Using Sentiment Analysis and Topic Modeling on Twitter Discussion.” In The 21st Annual International Conference on Digital Government Research, 1–6.10.1145/3396956.3396973Suche in Google Scholar
Kalyvas, S. N. 1999. “Wanton and Senseless? the Logic of Massacres in Algeria.” Rationality and Society 11 (3): 243–85, https://doi.org/10.1177/104346399011003001.Suche in Google Scholar
Kalyvas, S. N. 2006. The Logic of Violence in Civil War. New York: Cambridge University Press.10.1017/CBO9780511818462Suche in Google Scholar
Karami, A., L. S. Bennett, and X. He. 2018. “Mining Public Opinion About Economic Issues: Twitter and the US Presidential Election.” International Journal of Strategic Decision Sciences 9 (1): 18–28, https://doi.org/10.4018/ijsds.2018010102.Suche in Google Scholar
Kathman, J. D. 2013. “United Nations Peacekeeping Personnel Commitments, 1990–2011.” Conflict Management and Peace Science 30 (5): 532–49, https://doi.org/10.1177/0738894213491180.Suche in Google Scholar
Kathman, J., and M. Benson. 2019. “Cut Short? United Nations Peacekeeping and Civil War Duration to Negotiated Settlements.” Journal of Conflict Resolution 63 (7): 1601–29, https://doi.org/10.1177/0022002718817104.Suche in Google Scholar
Kathman, J. D., and R. M. Wood. 2016. “Stopping the Killing During the “Peace:” Peacekeeping and the Severity of Postconflict Civilian Victimization.” Foreign Policy Analysis 12 (2): 149–69.10.1111/fpa.12041Suche in Google Scholar
Koops, J. A., N. MacQueen, T. Tardy, and P. D. Williams, eds. 2015. The Oxford Handbook of United Nations Peacekeeping Operations. Oxford: Oxford Handbooks.10.1093/oxfordhb/9780199686049.001.0001Suche in Google Scholar
Levine-Spound, D. 2019. Backlash in Beni: Understanding Anger Against the UN Peacekeeping Mission in the DRC. Center for Civilians in Conflict. Also available at https://civiliansinconflict.org/blog/backlash-in-beni/.Suche in Google Scholar
Mohammad, S., and P. Turney. 2010. “Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon.” In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 26–34.Suche in Google Scholar
Öztürk, N., and S. Ayvaz. 2018. “Sentiment Analysis on Twitter: A Text Mining Approach to the Syrian Refugee Crisis.” Telematics and Informatics 35 (1): 136–47, https://doi.org/10.1016/j.tele.2017.10.006.Suche in Google Scholar
Peter, M. 2015. “Between Doctrine and Practice: The UN Peacekeeping Dilemma.” Global Governance 21: 351, https://doi.org/10.1163/19426720-02103002.Suche in Google Scholar
Pettersson, T., S. Davies, A. Deniz, G. Engström, N. Hawach, S. Högbladh, and M. S. M. Öberg. 2021. “Organized Violence 1989–2020, With a Special Emphasis on Syria.” Journal of Peace Research 58 (4): 809–25, https://doi.org/10.1177/00223433211026126.Suche in Google Scholar
Pouligny, B. 2006. Peace Operations Seen From below. UN Missions and Local People. London: C. Hurst.Suche in Google Scholar
Salverda, N. 2013. “Blue Helmets as Targets: A Quantitative Analysis of Rebel Violence Against Peacekeepers, 1989–2003.” Journal of Peace Research 50 (6): 707–20, https://doi.org/10.1177/0022343313498764.Suche in Google Scholar
Sanogo, I. 2016. Four Killed in CAR Protest Against UN Peacekeepers, October 24, 2016. France 24. https://www.france24.com/en/20161024-central-africa-republic-un-peacekeepers-minusca-bangui.Suche in Google Scholar
Seet, B., and G. M. Burnham. 2000. “Fatality Trends in United Nations Peacekeeping Operations, 1948–1998.” JAMA 284 (5): 598–603, https://doi.org/10.1001/jama.284.5.598.Suche in Google Scholar
Stokes, D. E. 1962. “Popular Evaluations of Government: An Empirical Assessment.” In Ethics and Bigness: Scientific, Academic, Religious, Political, and Military, 61–72. New York: Harper and Brothers.Suche in Google Scholar
Sundberg, R., and E. Melander. 2013. “Introducing the UCDP Georeferenced Event Dataset.” Journal of Peace Research 50 (4): 523–32, https://doi.org/10.1177/0022343313484347.Suche in Google Scholar
Valentino, B. 2000. “Final Solutions: The Causes of Mass Killing and Genocide.” Security Studies 9 (3): 1–59, https://doi.org/10.1080/09636410008429405.Suche in Google Scholar
van Klingeren, M., D. Trilling, and J. Möller. 2020. “Public Opinion on Twitter? How Vote Choice and Arguments on Twitter Comply with Patterns in Survey Data, Evidence from the 2016 Ukraine Referendum in the Netherlands.” Acta Politica 56: 1–20, https://doi.org/10.1057/s41269-020-00160-w.Suche in Google Scholar
Vinck, P., P. Pham, P. Sharma, and J. P. Zibika. 2019. “Peacebuilding and Reconstruction Polls: Eastern Democratic Republic of the Congo.” Voices from Congo (Poll Report 19), http://www.peacebuildingdata.org/sites/m/pdf/DRC_Poll19_FinalEnglish.pdf.Suche in Google Scholar
Wooldridge, J. M. 2015. Introductory Econometrics: A Modern Approach. South-Western: Cengage Learning.Suche in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Is It Right to Fight? Evidence from Russia and Ukraine
- Public Support for UN Missions and Attacks on Peacekeepers: Evidence From the Democratic Republic of the Congo
- Sanctions against North Korea: A Descriptive Analysis of their Economic Impact (2000–2020)
Artikel in diesem Heft
- Frontmatter
- Research Articles
- Is It Right to Fight? Evidence from Russia and Ukraine
- Public Support for UN Missions and Attacks on Peacekeepers: Evidence From the Democratic Republic of the Congo
- Sanctions against North Korea: A Descriptive Analysis of their Economic Impact (2000–2020)
