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Poor Numbers: Statistical Chains and the Political Economy of Numbers
Published online by Cambridge University Press: 30 October 2018
Abstract
Morten Jerven's Poor Numbers sheds light on the acute fragility of African statistics, itself linked to the precarious conditions in which aggregates are produced. As patchy and problematic as they are, these numbers are nevertheless ubiquitous as instruments of proof and tools of government. Quantified fictions take shape in complex statistical chains that stretch from their producers to the economists who use them, and are mediated by international organizations. Focusing on the criterion of accuracy, Poor Numbers powerfully conveys its message of “garbage in, garbage out,” but leaves important questions related to the relevance of statistics unanswered. The history, sociology, and political economy of numbers sketched by Jerven merit closer consideration with a view to the following: identifying the connections between evolving state forms and the development of statistics; establishing a historical ethnography of the organizations that produce and use numbers; understanding the growing role of multinationals in the political economy of statistics; taking a less conciliatory view of the involvement of international organizations; and, last but not least, denaturalizing the dominant economic categories by integrating the plurality of economic approaches to statistics. The article concludes with a call for a comparative political economy of numbers that would no longer consider the African case in isolation, and would work against the idea that Africa has not entered statistical history, or has only done so “by mistake.”
- Type
- The Economics of Contemporary Africa
- Information
- Annales. Histoire, Sciences Sociales - English Edition , Volume 71 , Issue 4 , December 2016 , pp. 507 - 538
- Copyright
- Copyright © Éditions EHESS 2018
Footnotes
This article was translated from the French by Helen Tomlinson and edited by Chloe Morgan and Nicolas Barreyre.
*I am particularly grateful to Boris Samuel and Béatrice Hibou, who invited me to deliver a commentary on Morten Jerven's Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It (Ithaca: Cornell University Press, 2013), at the Centre de recherches internationales (CERI) at Sciences Po in May 2013. A brief version of this commentary appeared in Politique africaine 133 (2014): 182 – 87, alongside contributions by Nicolas van de Walle and Carlos Oya and a response by Jerven. The present essay has benefited from the comments of the members of the Savoirs et gouvernement économique (SAGE) workshop at the Centre de recherche sur les institutions, l'industrie et les systèmes économiques d'Amiens (CRIISEA), as well as those of Florence Jany-Catrice, Roser Cussó, Fabrice Bardet, Jean-Pierre Beaud, Emmanuel Didier, and the Annales’ peer reviewers, all of whom I would like to warmly thank here.
References
1. Samuel, Boris and Hibou, Béatrice, introduction to “La macroéconomie par le bas,” thematic dossier, Politique africaine 124 (2011): 5 – 27Google Scholar, here p. 16.
2. Ibid.; Gabas, Jean-Jacques, Ribier, Vincent, and Vernières, Michel, eds., “La mesure du développement. Comment science et politique se conjuguent,” thematic dossier, Revue Tiers Monde 213, no. 1 (2013)CrossRefGoogle Scholar.
3. A valuable panorama of the social sciences of quantification in France is presented in Bruno, Isabelle, Jany-Catrice, Florence, and Touchelay, Béatrice, “The Social Sciences of Quantification in France: An Overview,” introduction to The Social Sciences of Quantification: From Politics of Large Numbers to Target-Driven Politics, ed. Bruno, Jany-Catrice, and Touchelay (Berlin: Springer, 2017), 1 – 14Google Scholar.
4. In Samuel, Boris, ed., “Autour d'un livre : Poor Numbers. How We Are Misled by African Development Statistics and What to Do about It,” Politique africaine 133 (2014): 177 – 99, here p. 196Google Scholar.
5. Similarly, like Jerven, I focus on macroeconomic aggregates and do not discuss the construction of microdata. See Mesplé-Somps, Sandrine, “Fiche de lecture : Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It de Morten Jerven,” Statéco 107 (2013): 105 – 7, here p. 107Google Scholar.
6. In other words, the idea that the quality of the results is a function of the data entered.
7. The useful expression “statistical chain” should not be automatically conflated with the idea of linear processes, which would imply distinct actors at each link in the chain. International organizations, for example, are involved in both the production of data and their transformation and interpretation. See Samuel, Boris, “Calcul macroéconomique et modes de gouvernement : les cas de la Mauritanie et du Burkina Faso,” Politique africaine 124 (2011): 101 – 26CrossRefGoogle Scholar.
8. Jerven, Poor Numbers, ix – x.
9. Ibid., 15.
10. Ibid., 23.
11. These are five of the six criteria of statistical quality defined by the directorate-general of the European Commission in charge of statistical information, Eurostat: see Desrosières, Alain, L'argument statistique, vol. 2, Gouverner par les nombres (Paris: Presses de l’École des mines, 2008)CrossRefGoogle Scholar, chap. 6. The sixth criterion, to which I shall return later, is relevance.
12. Jerven, Poor Numbers, 18 – 19.
13. Ibid., xv.
14. The notion of “evidence-based policy” is much more interesting than the slogan to which it is sometimes reduced; it notably includes the regime of the accessibility of knowledge, the methods of production of meta-analyses, and so forth. For more details on the origin of this notion, its variations, its necessity, and its practical limits, see Laurent, Catherine et al., “Pourquoi s'intéresser à la notion d’‘evidence-based policy’ ?” Revue Tiers Monde 200, no. 4 (2009): 853 – 73CrossRefGoogle Scholar.
15. “Le point de vue de Carlos Oya,” in Samuel, “Autour d'un livre,” 187–92, here p. 189.
16. Lascoumes, Pierre and Le Galès, Patrick, eds., Gouverner par les instruments (Paris: Presses de Sciences Po, 2005)Google Scholar.
17. Desrosières, L'argument statistique, vol. 2.
18. Ibid., 8.
19. Jerven, Poor Numbers, 71.
20. Durlauf, Steven et al., “Growth Econometrics,” in Handbook of Economic Growth, ed. Philippe Aghion and Steven Durlauf (Amsterdam: Elsevier, 2005), 556 – 663Google Scholar, here table 8, p. 574.
21. It should be noted that this interpretation is not an isolated phenomenon. It is part of a broader ideological movement idealizing the informal sector, a problematic concept created and promoted by writers like Hernando de Soto in The Other Path: The Invisible Revolution in the Third World (New York: Harper and Row, 1989), and international organizations such as the International Labour Office (ILO) and the World Bank, which, from 1987, “faced with the social catastrophe caused by structural adjustment, located the solution to all social problems in the informal sector.” See Lautier, Bruno, L’économie informelle dans le Tiers monde (1994; repr. Paris: La Découverte, 2004), 3Google Scholar.
22. This situation is somewhat reminiscent of the primary specialization of African economies, with transformation activities mainly carried out by multinational firms in the Global North.
23. Jerven, Poor Numbers, xvii.
24. Ibid., 97 – 99.
25. Bruno, Jany-Catrice, and Touchelay, “The Social Sciences of Quantification in France.”
26. Desrosières, L'argument statistique, 2:135.
27. Jerven, Poor Numbers, 23.
28. Ibid., 22.
29. Samuel, “Calcul macroéconomique,” 105. It should be noted that, unlike Jerven, Samuel subtly differentiates the activities of these international experts depending on the international organization in question (the IMF, the World Bank, the United Nations) and their role within each of them.
30. Jerven, Poor Numbers, 81 – 82.
31. Thévenot, Laurent, “Les investissements de forme,” Conventions économiques (Paris: Presses universitaires de France, 1986), 21 – 71Google Scholar.
32. See below, pp. 524 – 28.
33. Seers, Dudley, “The Role of National Income Estimates in the Statistical Policy of an Under-Developed Area,” Review of Economic Studies 20, no. 3 (1952 – 1953): 159 – 68CrossRefGoogle Scholar, cited in Jerven, Poor Numbers, 36.
34. Jerven, Poor Numbers, 34.
35. Naudet, Jean-David, “Les guignols de l'info. Réflexion sur la fragilité statistique en Afrique,” Nouveaux Cahiers de l'Institut universitaire d’études du développement 10 (2000): 31 – 55Google Scholar, here § 63 of the online edition (http://books.openedition.org/iheid/2578).
36. Mäki, Uskali, “As If,” in The Handbook of Economic Methodology, ed. Davis, John B., Wade Hands, D., and Mäki, Uskali (Cheltenham: Edward Elgar, 1998), 156 – 60Google Scholar.
37. Friedman, Milton, “The Methodology of Positive Economics,” Essays in Positive Economics (Chicago: University of Chicago Press, 1953), 3 – 43Google Scholar. When Friedman published his seminal text, numerous surveys of business leaders showed that they were unaware of the notion of marginal cost, thus fueling a realist critique of the neoclassical approach. Friedman replied that the degree to which a theory reflected empirical reality should be gauged not on the basis of its hypotheses (which are necessarily reductionist), but on the basis of the empirical predictions logically deduced from these hypotheses. See Favereau, Olivier, “Arrogance de l’économie, économie de l'arrogance,” in L'arrogance. Un mode de domination néo-libéral, ed. Enriquez, Eugene (Paris: Éd. In Press, 2015), 147 – 64Google Scholar, here p. 159: “The surest way to bring ridicule on oneself at an academic economics conference is to formulate a ‘literary’ critique (i.e., in everyday language) of this or that theoretical assertion in the name of realism—the only admissible (and valued) critique is a sophisticated econometric test on the empirical implications of a theoretical model, in most cases an aggregative one.”
38. Following Mäki, “As If,” it is worth noting that Friedman's instrumentalism can be distinguished from other forms of instrumentalism (in physics, for example), in the sense that it is based upon deliberately false hypotheses and not on the practical impossibility of testing these hypotheses.
39. Lordon, Frédéric, “Le désir de ‘faire science,’” Actes de la recherche en sciences sociales 119, no. 1 (1997): 27 – 35CrossRefGoogle Scholar. This can also be true of some forms of development microeconomics that are nevertheless close to the reality on the ground—such as randomized controlled experiments in which that reality is seen first as a “statistical playing field” and not as a source of knowledge in its own right. See Labrousse, Agnès, “Not by Technique Alone: A Methodological Comparison of Development Analysis with Esther Duflo and Elinor Ostrom,” Journal of Institutional Economics 12, no. 2 (2016): 277 – 303;CrossRefGoogle Scholar and Jatteau, Arthur, “Expérimenter le développement ? Des économistes et leurs terrains,” Genèses 93, no. 4 (2013): 8 – 28CrossRefGoogle Scholar.
40. As we will see, this situation does not reflect a consubstantial characteristic of the profession, see below, pp. 532 – 35.
41. Foucault, Michel, “The Discourse on Language,” in The Archaeology of Knowledge and the Discourse on Language, trans. Smith, A. M. Sheridan (New York: Pantheon Books, 1972), 215 – 37Google Scholar, here p. 224.
42. In other words, the number of publications by subject studied. See Jatteau, “Expérimenter le développement,” 16 – 17.
43. This is not a criticism of econometrics in itself but of a particular use of it, commonly known in the profession as “button-pressing econometrics.”
44. See the famous article by Sala-i-Martín, Xavier, “I Just Ran Two Million Regressions,” American Economic Review 87, no. 2 (1997): 178 – 83Google Scholar.
45. Naudet, “Les guignols de l'info,” § 1 and 34 – 36 of the online edition.
46. Samuel, “Calcul macroéconomique et modes de gouvernement,” 106.
47. Ferguson, James, The Anti-Politics Machine: “Development,” Depolitization, and Bureaucratic Power in Lesotho (Cambridge: Cambridge University Press, 1990), 41Google Scholar, cited in Naudet, “Les guignols de l'info,” § 58 of the online edition.
48. Foucault, “The Discourse on Language,” 224.
49. Jerven explains that the “concept of validity is related to whether the measure is accurate, and the concept of reliability is related to whether the measure is similarly inaccurate or accurate each time” (Poor Numbers, 16). In fact, this criterion of reliability is similar to that of data comparability across time and space (Desrosières, L'argument statistique, 2:135).
50. See the press review available on Jerven's website: http://mortenjerven.com/press-and-reviews/.
51. Justin Sandefur and Amanda L. Glassman, “The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics” (working paper 373, Center for Global Development, 2014), 1.
52. This is often reflected in extended reviews of Jerven's work published in French-language journals. Thus Joseph Tédou, director of the National Institute of Statistics of Cameroon, focuses solely on the revision of Ghana's GDP: Joseph Tédou, “Tribune sur Poor Numbers,” Statéco 108 (2014): 99 – 101.
53. Jerven, Poor Numbers, 109.
54. Desrosières, Alain, “La statistique entre le langage de la science et celui de l'action. Comment discuter l'indiscutable ?” Correspondances. Bulletin d'information scientifique de l'Institut de recherche sur le Maghreb contemporain 39 (1993): 3 – 8Google Scholar, here p. 3.
55. Porter, Theodore M., Trust in Numbers: The Pursuit of Objectivity in Science and Public Life (Princeton: Princeton University Press, 1995)Google Scholar; Merry, Sally Engle, “Measuring the World: Indicators, Human Rights, and Global Governance,” Proceedings of the Annual Meeting of the American Society of International Law 103 (2009): 239 – 45Google Scholar.
56. Jerven, Poor Numbers, xii.
57. Desrosières, L'argument statistique, vol. 1, Pour une sociologie historique de la quantification (Paris: Presses de l’École des mines, 2008), chaps. 6 and 9; Desrosières, L'argument statistique, vol. 2, chap. 6.
58. Jerven, Poor Numbers, xiii and 10.
59. Ibid., 111.
60. Ibid.
61. See, in the French case, Fourquet, François, Les comptes de la puissance. Histoire de la comptabilité nationale et du plan (Paris: Éd. Recherches, 1980)Google Scholar; Vanoli, André, Une histoire de la comptabilité nationale (Paris: La Découverte, 2002)Google Scholar.
62. Desrosières, L'argument statistique, 1:43.
63. Desrosières, Alain, “La mesure du développement. Un domaine propice à l'innovation méthodologique,” Revue Tiers Monde 213, no. 1 (2013): 23 – 32CrossRefGoogle Scholar, here p. 24.
64. On the symbolic, operational, and political (mis)uses of GDP, see the teaching report by Pierre Lachaize and Julien Morel, “Les usages du PIB,” 2013, https://theshiftproject.org/wp-content/uploads/2017/05/the_shift_project-rapport_final-les_usages_du_pib_0.pdf, which nevertheless ascribes excessive importance to GDP in countries of the Global South.
65. Gadrey, Jean and Jany-Catrice, Florence, Les nouveaux indicateurs de richesse (Paris: La Découverte, 2005)Google Scholar; Joseph E. Stiglitz, Amartya Sen, and Jean-Paul Fitoussi, eds., “Rapport de la Commission sur la mesure des performances économiques et du progrès social,” 2010, http://www.ladocumentationfrancaise.fr/var/storage/rapports-publics/094000427.pdf.
66. See the Forum pour de nouveaux indicateurs de richesse (FAIR), “La richesse autrement,” special issue, Alternatives économiques 48 (2011), and the dossier “Qui décide de ce qui compte ?” Revue Projet 331 (2012): 6 – 67.
67. Jerven, Poor Numbers, 3.
68. Desrosières remarks that “in his everyday practice, the statistician is plunged into a world of conventions, which he records or shapes himself. The fact that the measurement results from this succession of conventional decisions is therefore obvious to him. But then, he puts on another hat without realizing it, and adopts realist language as soon as he addresses the outside world.” See L'argument statistique, 2:139.
69. Bhaskar, Roy A., The Possibility of Naturalism: A Philosophical Critique of the Contemporary Human Sciences (1979; repr. London: Routledge, 1998)Google Scholar.
70. As he does to a greater extent in Jerven, Morten, “Un demi-siècle de fictions de croissance en Afrique. Entretien de Béatrice Hibou et Boris Samuel avec Morten Jerven,” Politique africaine 124 (2011): 29 – 42CrossRefGoogle Scholar. This may reflect the questions that were posed in this interview, which were informed by a constructivist epistemology.
71. Samuel, “Autour d'un livre,” 179.
72. This is the recurrent term in articles inspired by Jerven's research—but one that he does not himself endorse. It is used particularly often by Shantayanan Devarajan (chief economist at World Bank Africa): Devarajan, “Africa's Statistical Tragedy,” Africa Can End Poverty, June 10, 2011, http://blogs.worldbank.org/africacan/africa-s-statistical-tragedy.
73. Samuel, “Autour d'un livre,” 296 – 97.
74. Desrosières, L'argument statistique, 2:119 – 41.
75. Philippe Couty, “Qualitatif et quantitatif,” in Philippe Couty and Gérard Winter, “Qualitatif et quantitatif : deux modes d'investigation complémentaires. Réflexions à partir des recherches de l’ORSTOM en milieu rural africain” (working paper 43, AMIRA, 1983), 35 – 49, here p. 44, http://horizon.documentation.ird.fr/exl-doc/pleins_textes/divers4/15290.pdf (my emphasis).
76. Philippe Couty, “Des éléments aux systèmes. Réflexions sur les procédés de généralisation dans les enquêtes de niveau de vie en Afrique,” Statéco 30 (1982): 18 – 54, http://www.epsilon.insee.fr/jspui/bitstream/1/14799/3/Stateco30.pdf.
77. Sara Randall and Ernestina Coast, “Poverty in African Households: The Limits of Survey Representations” (paper given at the conference “African Economic Development: Measuring Success and Failure,” Simon Fraser University, Canada, 2013).
78. Guyer, Jane I. and Peters, Pauline E., “Introduction to Conceptualizing the Household: Issues of Theory and Policy in Africa,” Development and Change 18, no. 2 (1987): 197 – 214CrossRefGoogle Scholar.
79. Hill, Polly, Development Economics on Trial: The Anthropological Case for Prosecution (Cambridge: Cambridge University Press, 1986)CrossRefGoogle Scholar.
80. Jerven, Poor Numbers, 77 – 78.
81. On this subject, see Bruno, Jany-Catrice, and Touchelay, “The Social Sciences of Quantification in France,” and the dossier “Qui décide de ce qui compte ?”
82. Desrosières, “La mesure du développement,” 28.
83. Perroux, François, L’économie du xxe siècle (1961; repr. Paris: Presses universitaires de France, 1964)Google Scholar, chap. 3, “La notion de développement.” See also Myrdal, Gunnar, “Institutional Economics,” Journal of Economic Issues 12, no. 4 (1978): 771 – 83CrossRefGoogle Scholar.
84. For a presentation and critique of this model, see Guerrien, Bernard and Gun, Ozgur, “Putting an End to the Aggregate Function of Production … Forever?” Real-World Economics Review 73 (2015): 99–109Google Scholar, http://www.paecon.net/PAEReview/issue73/GuerrienGun73.pdf.
85. Rostow, Walt Whitman, “Development: The Political Economy of the Marshallian Long Period,” in Pioneers in Development, ed. Meier, Gerald M. and Seers, Dudley (Oxford/New York: Oxford University Press/World Bank, 1984), 229 – 61Google Scholar, here p. 238 (my emphasis).
86. Along with its variants, gross national product (GNP) and gross national income (GNI).
87. Couty, “Qualitatif et quantitatif,” 44.
88. Cicourel, Aaron V., Method and Measurement in Sociology (New York: Free Press, 1964)Google Scholar.
89. Desrosières, L'argument statistique, 1:157.
90. Ibid., chap. 16; Desrosières, “La mesure du développement”; Pierre Bourdieu et al., Travail et travailleurs en Algérie (Paris: Mouton, 1963).
91. Couty, “Qualitatif et quantitatif,” 37. See also, for the countries of the Global North, Cédric Lomba, “Avant que les papiers ne rentrent dans les cartons : usages ethnographiques des documents d'entreprises,” in Observer le travail. Histoire, ethnographie, approches combinées, ed. Anne-Marie Arborio et al. (Paris: La Découverte, 2008), 29 – 44; and Hatzfeld, Nicolas, “Maladies professionnelles. La reconnaissance des troubles musculo-squelettiques. Une histoire administrative et scientifique (1982-1996),” Corps. Revue interdisciplinaire 6 (2009): 47 – 52Google Scholar, which considers work and its quantification from an ethnographic, historical, and sociological perspective.
92. Jerven, Poor Numbers, 33 – 54.
93. Ibid., 36 sq.
94. Ibid., 50. Cussó has signaled to me, however, that the League of Nations had published statistics on agricultural productions: wheat in South Africa, Algeria, Egypt, Eritrea, Kenya, Morocco, Sudan, and Tunisia, cocoa in Cameroon and the Congo, etc. Statistics on coal production likewise seem to have existed.
95. Bonnecase, Vincent, La pauvreté au Sahel. Du savoir colonial à la mesure internationale (Paris: Karthala, 2011)Google Scholar.
96. Ibid., 107.
97. Jerven, Poor Numbers, 42.
98. Ibid., 45.
99. Ibid., 53.
100. Ibid., 45 sq.
101. Ibid., 51.
102. Ibid., 47.
103. Ward, Michael, Quantifying the World: UN Ideas and Statistics (Bloomington: Indiana University Press, 2004)Google Scholar.
104. Jerven, Poor Numbers, 52.
105. Ibid., x. Curiously, this period is briefly evoked in the preface and at the end of the book, but not in the chapter on the history of statistics.
106. Ibid., 106.
107. Ibid., 105.
108. Desrosières, L'argument statistique, vol. 1, chap. 3. The portrait of a historical succession of dominant statistical regimes sketched by Jean-Pierre Beaud and Jean-Guy Prévost, based on the Americas, could also be adapted to Africa: pre- or proto-statistical regimes, nationalization, macromanagement, then neoliberal globalization. See Beaud and Prévost, “L'histoire de la statistique canadienne dans une perspective internationale et panaméricaine,” in Estatísticas nas Américas. Por uma agenda de estudos históricos comparados, ed. Nelson de Castro Senra and Alexandre de Paiva Rio Camargo (Rio de Janeiro: Instituto Brasileiro de geografia e estatística, 2010), 37 – 65.
109. There is therefore also a Human Development Index for indigenous peoples (HDI-IP). See Parizet, Raphaëlle, “Mesurer le développement pour gouverner les peuples autochtones,” Revue Tiers Monde 213, no. 1 (2013): 143 – 60CrossRefGoogle Scholar.
110. Desrosières, L'argument statistique, 1:54.
111. Ibid.
112. Beaud, Jean-Pierre, “Controverses, crises et changement dans les systèmes statistiques,” Statistique et société 2, no. 3 (2014): 41 – 48Google Scholar, version provided to the author.
113. Roser Cussó and Sabrina D'Amico, “Vers une comparabilité plus normative des statistiques internationales de l’éducation : de l’éducation de masse aux compétences,” in Annie Vinokur, ed., “Pouvoirs et mesure en éducation,” special issue, Cahiers de la recherche sur l’éducation et les savoirs 1 (2005): 1 – 47.
114. Clément, Alain, “Les mercantilistes et la question coloniale aux xvie et xviie siècles,” Outre-mers. Revue d'histoire 348 – 49 (2005): 167 – 202CrossRefGoogle Scholar. An initial, predatory conception of colonization views unequal trade with the colonies as a source of enrichment without heavy investment on site; a second conception considers it a source of enrichment stemming from the development of the colony and more balanced but exclusive exchanges; finally, a last conception views colonization as a source of impoverishment.
115. Bonnecase, La pauvreté au Sahel.
116. Jerven, Poor Numbers, 103.
117. Nubukpo, Kako, “Politique monétaire et servitude volontaire : la gestion du franc CFA par la BCEAO,” Politique africaine 105 (2007): 70 – 84, here p. 73CrossRefGoogle Scholar.
118. Naudet, “Les guignols de l'info.”
119. Gunnar Myrdal, “International Inequality and Foreign Aid in Retrospect,” in Meier and Seers, Pioneers in Development, 151 – 65, here p. 159.
120. Jerven, Poor Numbers, 5.
121. Ibid., xii.
122. Samuel and Hibou, “La macroéconomie par le bas.”
123. Jerven, Poor Numbers, 105.
124. In Samuel, “Autour d'un livre.”
125. Sandefur and Glassman, “The Political Economy of Bad Data,” 2, n. 4.
126. Jerven, Poor Numbers, 105.
127. Raffinot, Marc, “Vite fait ou bien fait ? L’économie politique de la lutte contre la pauvreté au Mali,” L’économie politique 16, no. 4 (2002): 55 – 69CrossRefGoogle Scholar, here p. 64.
128. Sandefur and Glassman, “The Political Economy of Bad Data.”
129. Chiapello, Ève et al., “À propos de l'emprise du chiffre,” Entreprises et histoire 79 (2015): 174 – 87CrossRefGoogle Scholar, here p. 182.
130. Samuel and Hibou, “La macroéconomie par le bas.”
131. Jerven, Poor Numbers, 61.
132. Ibid., 13.
133. Bonnie Campbell, “Le chiffre comme outil politique” (paper given at the conference “La mesure du développement,” UNESCO, Paris, 2012), p. 2, http://www.ieim.uqam.ca/IMG/pdf/B_Campbell_Le_chiffre_comme_outil_politique_0102_2012.pdf.
134. For a consideration of these issues in the case of agri-food companies in developed countries, see Lyazid Kichou and Christian Palloix, “Les groupes agroalimentaires multinationaux en Europe. Entre exubérance financière et inversion des chaînes de valeur” (paper given at the conference “Les entreprises multinationales, les chaînes de valeur mondiales et la régulation sociale,” CRIMT, Montreal, 2011). Here again, these issues are not specific to Africa, even though the stakes on that continent are particularly high.
135. Campbell, “Le chiffre comme outil politique,” 3, quotes the United Nations Development Programme (UNDP) discussion paper “Illicit Financial Flows from the Least Developed Countries, 1990 – 2008,” 2011, http://content-ext.undp.org/aplaws_publications/3273649/IFFs_from_LDCs_web.pdf. Six of the ten countries most affected by these cumulative flows are located in Africa: Angola (second), Lesotho (third), Chad (fourth), Uganda (seventh), Ethiopia (ninth), and Zambia (tenth).
136. Jerven, Poor Numbers, 112.
137. See Mirowski, Philip, More Heat than Light: Economics as Social Physics, Physics as Nature's Economics (Cambridge: Cambridge University Press, 1989)CrossRefGoogle Scholar, on the debatable transposition of concepts deriving from nineteenth-century mechanics by neoclassical economists.
138. “The atomistic hypothesis that has worked so splendidly in Physics breaks down in Psychics. We are faced at every turn with the problems of Organic Unity, of Discreteness, of Discontinuity—the whole is not equal to the sum of the parts, comparisons of quantity fail us, small changes produce large effects, the assumptions of a uniform and homogeneous continuum are not satisfied.” John Maynard Keynes, cited in Delorme, Robert, Deep Complexity and the Social Sciences: Experience, Modelling and Operationality (Cheltenham: Edward Elgar, 2010), 66CrossRefGoogle Scholar (my emphasis).
139. See in particular Keynes's work on probabilities, discussed in ibid., 64 – 77.
140. Quoted in ibid., 69.
141. Labrousse, Agnès, “Éléments pour un institutionnalisme méthodologique : autonomie, variation d’échelle, réflexivité et abduction,” Économie et institutions 8 (2006): 5 – 53CrossRefGoogle Scholar.
142. Menard, Claude, “Trois formes de résistance à la statistique : Say, Cournot, Walras,” in Pour une histoire de la statistique (Paris: INSEE/Economica, 1977)Google Scholar, 1:417 – 29.
143. A position influenced by neo-Kantian constructivism in the case of the German historical school and by the pragmatism of John Dewey and Charles Sanders Peirce in the case of American institutionalism. The conventional nature of statistics is evident for the recent current of the economics of conventions, which is also an economics of statistical conventions: see Desrosières, Alain, “The Economics of Conventions and Statistics: The Paradox of Origins,” Historische Sozialforschung 36, no. 4 (2011): 64 – 81Google Scholar.
144. Myrdal, “Institutional Economics,” 776 and 781.
145. McCartney, Matthew, “Can a Heterodox Economist Use Cross-country Growth Regressions?” Post-Autistic Economics Review 37 (2006): 45 – 54Google Scholar. Institutionalist and neoclassical conceptions of statistics would come into conflict during the Rutledge Vining versus Tjalling Koopmans controversy of the 1940s: not entirely in good faith, the physicist-turned-economist Koopmans reproached the institutionalists from the National Bureau of Economic Research (NBER), including Mitchell, for practicing “measurement without theory,” because they refused to draw out universal economic laws and confined themselves to situated regularities.
146. Jerven, Poor Numbers, 92.
147. “La statistique publique, un bien public original,” INSEE inter-union conference, 2011, http://ancien.cgtinsee.org/Kolok/kolok5/Actes_colloque2011_integrale.pdf.
148. André Orléan, “Humility in Economics,” Post-Autistic Economics Newsletter 5 (2001): http://www.paecon.net/PAEtexts/Orlean1.htm.
149. Desrosières, “La statistique entre le langage de la science et celui de l'action,” 1.
150. I agree with Bruno Théret regarding the profound heuristic interest of extreme cases in international comparisons, which should not be dismissed as “outliers,” but on the contrary made central to the analysis. See Théret, “Méthodologie des comparaisons internationales, approches de l'effet sociétal et de la régulation. Fondements pour une lecture structuraliste des systèmes nationaux de protection sociale,” L'année de la régulation 1 (1997): 163 – 228.
151. Guy Marchal, “Les statistiques chinoises mettent les économistes au défi,” L'Agefi, August 24, 2015, http://www.agefi.fr/asset-management/actualites/hebdo/20160210/statistiques-chinoises-mettent-economistes-defi-158931.
152. Giroir, Guillaume, “Statistiques et territoire en Chine,” L'information géographique 69, no. 1 (2005): 91 – 101CrossRefGoogle Scholar, here p. 92.
153. Cited in ibid., 94.
154. Ibid.
155. Ibid., 93.
156. Attané, Isabelle, “La fécondité chinoise à l'aube du xxie siècle : constats et incertitudes,” Population 55, no. 2 (2000): 233 – 64CrossRefGoogle Scholar, here pp. 234 – 35: “While, until the 1990 census, China had abundant and relatively reliable demographic data, this has not been the case since. The last two surveys (the fertility and birth control survey of 1992 and the inter-census survey of 1995), were unanimously recognized as being of mediocre quality, including by China itself, and could not be exploited effectively. Civil registry data have not sufficed to fill in these gaps. With the dismantling of the people's communes in the early 1980s and the progressive disintegration of the system of state supervision over individuals, civil registers have become so patchy that the data they compile are no longer used in demographic analysis. Increasing internal migrations that make it difficult to monitor a growing swath of the population, the loss of state control over the populace, negligence in the reporting of births and deaths, the refusal of couples to report an illegal marriage or unplanned birth, and the falsification of statistics by officials to satisfy the limits imposed on births are all factors in the deterioration of the system for the continuous recording of the population.”
157. Rawski, Thomas G., “What's Happening to China's GDP Statistics?” China Economics Review 12, no. 4 (2001): 347 – 54CrossRefGoogle Scholar.
158. Éric Béziat and Henri Olivier, “Faut-il croire le chiffre officiel de la croissance chinoise ?” Le Monde, January 19, 2016.
159. Beaud, “Controverses, crises et changement,” version provided to the author.
160. See in particular the work of Nelson de Castro Senra in Brazil, Hernán Otero in Argentina, and Leticia Mayer in Mexico, cited in ibid.
161. Beaud and Prévost, “L'histoire de la statistique canadienne.”
162. Blum, Alain and Mespoulet, Martine, L'anarchie bureaucratique. Statistiques et pouvoirs sous Staline (Paris: La Découverte, 2003)Google Scholar.