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IOs’ selective adoption of NGO information: Evidence from the Universal Periodic Review

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Abstract

Nongovernmental organizations (NGOs) have actively participated in the policymaking process within international organizations (IOs) by providing policy information. But due to limited policy attention and agenda space, IOs are capable of accommodating some but not all NGO information. How do IOs decide which NGO information to be accepted on the international agenda? Leveraging a unique information-filtering mechanism in the Office of the United Nations High Commissioner for Human Rights’ (OHCHR) preparation of summary reports that selectively incorporate information from NGO shadow reports during the United Nations Universal Periodic Review between 2008 and 2016, this article looks inside the “black box” of agenda setting in global governance. Using an original corpus of documents from OHCHR and more than 7000 advocacy organizations or coalitions, and a new method that quantifies OHCHR’s information gatekeeping behavior, I provide empirical support for the general claim that IOs are more likely to adopt NGO information provided by more reputable informants, expressed in neutral language, and supported by a greater number of organizations. These findings highlight the importance of IO agency in shaping the transnational advocacy agenda.

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Notes

  1. For a more detailed review of the UPR process, see Milewicz and Goodin (2018).

  2. The first UPR cycle was held between 2008 and 2011, and the second UPR cycle between 2012 and 2016. The third cycle of the UPR began in 2017 and will end in 2022.

  3. Adopting a more benign view of NGOs’ lack of accountability, Koppell (2005) argues that NGOs are confronted with different audiences who make divergent demands on them. Due to this “multiple accountabilities disorder,” NGOs are incapable of satisfying any audience consistently.

  4. I perform exact string matching of organization names for each sentence from the text of an OHCHR summary report. The list of organization names is obtained from the endnote section of the summary report. I also manually check possible name variants and mistyped names in the summary report and include them in my matching procedure. When computing the length of a string of textual characters, I remove space, punctuation marks and organization names.

  5. In some instances, a sentence does not mention any organizations. I then adopt the following rule to infer information sources. I first search for organization names in the endnote associated with the sentence. If my search of the endnote yields no results, I then assign the sentence to organization(s) mentioned in the preceding sentence within the same paragraph. My own reading of numerous summary reports reveals that OHCHR often omits to reference information source(s) in a sentence if it has already done so immediately before.

  6. The summary reports of OHCHR, which draw information from NGO reports written in any UN official language, contain mainly English text and sometimes Spanish and French text. I am hesitant to translate non-English text in the summary reports because the dependent variable is measured by the amount of agenda space instead of substantive textual meaning.

  7. https://esango.un.org/civilsociety/login.do.

  8. I used the FuzzyWuzzy package in Python to perform the fuzzy string matching. Four different approaches of string matching—simple ratio, partial ratio, token sort, and token set—are used. For each approach, a matched string with a similarity score of at least 90 out of 100 is returned.

  9. NGOs are considered to have consultative status with the UN ECOSOC if they had obtained such an arrangement in all years up until the time when they participated in the UPR.

  10. I collect the texts of NGO reports from the official UPR website. Background documents that accompany the main NGO reports are excluded from my analysis.

  11. Although most NGO reports are in English, some are in other UN official languages. Because the R package sentimentr can process only English text and it is important to evaluate the emotional valence of text within the same context of vocabulary and grammar, I use Google’s online translation tool to translate all non-English text into English text before I conduct sentiment analysis. Some NGOs submit reports in both English and other languages(s). In these cases, I keep only documents written in English.

  12. Cuba was reviewed on 5 February 2009 during the first UPR cycle.

  13. See detailed descriptions in online Appendix, available on the Review of International Organizations’ website.

  14. See table A1 in online Appendix.

  15. National reports are missing from the UN website for Cabo Verde in the first UPR cycle and Dominica in the second URR cycle. Comoros only made an oral presentation in the first UPR cycle. In these cases, the value of the NGO-State report similarity is not defined.

  16. In a few instances, this variable is not defined because there is only one NGO report available for analysis in a country. For example, although there are two NGO reports submitted for San Marino during the first UPR cycle, the one filed by Global Initiative to End All Corporal Punishment of Child is missing.

  17. Note that the summary statistics are computed based on the sample in the full regression analysis. See also Figure A2 in online Appendix for a visual display of the distributions of both the dependent and independent variables.

  18. See Figure A3 in online Appendix. Based on the 13 topics I estimated, I combine topics of similar kind and create seven rights categories for the ease of presentation. Specifically, topics 1 and 8 are combined into one category called “Physical integrity rights.” A new category “Political rights” is generated to include topics 2, 3, and 11. The category “Sexual rights” is created to include topics 5 and 12. The category “Social and economic rights” includes topics 4, 9, and 10. Topics 6, 7, and 13 remain as stand-alone rights categories. An NGO report is assigned to one of the seven rights categories based on its most prevalent topic.

  19. However, I observe a negative and statistically significant effect of the number of NGO submissions in an alternative, fractional probit regression model (see Table A2 in online Appendix). Because there is no prior belief to determine the functional form of the expected percentage of NGO information integrated into OHCHR summary reports, the findings about the effect of the number of NGO submissions are inconclusive.

  20. See Table A2 in online Appendix.

  21. See Table A3 in online Appendix.

  22. See Table A4 in online Appendix. Results show that NGOs with roster status are no different from those without UN consultative status. Consistent with my argument, NGOs with either general status or special status are treated more favorably by OHCHR in the UPR process.

  23. See Table A5 in online Appendix.

  24. See Table A6 in online Appendix.

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Acknowledgements

Earlier versions of this article were presented at the Political Science Research Workshop at Purdue University (2020) and the annual meetings of the Midwest Political Science Association (2019) and the American Political Science Association (2020). For valuable feedback, I thank Ann Marie Clark, Xinyuan Dai, Geoff Dancy, Christopher J. Fariss, Shareen Hertel, Andrew C. McWard, Cleo O'Brien-Udry, Matthew Pietryka, Keith L. Shimko, Mao Suzuki, Logan Strother, Jonas Tallberg, Mark Tilton, Giancarlo Visconti, Eric N. Waltenburg, and three anonymous reviewers. All errors are my own.

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    Mintao Nie

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Nie, M. IOs’ selective adoption of NGO information: Evidence from the Universal Periodic Review. Rev Int Organ 18, 27–59 (2023). https://doi.org/10.1007/s11558-022-09455-3

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