Are Development Statistics Manipulable

Coordinating foreign aid distribution to the poorest countries requires classifying them into developmental cohorts. In principle these designations are objective and immune from manipulation by aid-seeking countries. The objectivity and reliability of these data are important for aid distribution as well as for the use of these data in social scientific applications. We ask whether there are indications that these data are being influenced by aid-seeking manipulation. To do so we examine the distribution of GNIs per capita around the eligibility threshold for World Bank’s International Development Association (IDA). We examine the data as whole and separately for countries that are plausibly more motivated to aid-seek by virtue of their aiddependence or more capable of doing so by virtue of being perceived as trustworthy. We show that the distribution of GNIs per capita from aid-dependent countries displays indications of aidseeking data manipulation. This finding is robust to a variety of model specifications, but somewhat sensitive to the exclusion of individual countries from the sample. As such, these findings are more suggestive than definitive, but they do lend credence to the idea of data generation as a strategic process and suggest the need for more research in this area. About the authors: Andrew Kerner is an assistant professor of political science at the University of Michigan. His research interests include the politics of finance and FDI, corporate governance and the role of institutional investors (particularly pension funds) in emerging market policymaking. His MA and PhD are from Emory University. Morten Jerven is an economic historian and an associate professor at Simon Fraser University. His publications include Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It (Cornell University Press, 2013). His second book, Economic Growth and Measurement Reconsidered in Botswana, Kenya, Tanzania, and Zambia, 1965–1995, appeared with Oxford University Press in 2014. Alison Beatty is a PhD candidate in public policy and political science at the University of Michigan. Her research addresses interstate competition for FDI and the developing role of US states in the global economy. Before her doctoral studies Beatty interned with the US State Department’s Office of Central Europe. About the publisher: The School for International Studies (SIS) fosters innovative interdisciplinary research and teaching programs concerned with a range of global issues, but with a particular emphasis on international development, and on global governance and security. The School aims to link theory, practice and engagement with other societies and cultures, while offering students a challenging and multi-faceted learning experience. SIS is located within the Faculty of Arts and Social Sciences at Simon Fraser University. Our website is www.sfu. ca/internationalstudies. Are Development Statistics Manipulable? 5 Are Development Statistics Manipulable? Common understandings of which countries are poor and which countries are not are crucial to both the study and conduct of international politics. For the most part these common understanding come from development statistics such as GDP or GNI. The accuracy of these data and the understandings that follow from them are crucially important. To take an example at the heart of this paper, coordinating aid distribution to the poorest countries requires a common understanding of which countries are poorer than others, and this understanding is typically drawn from development statistics. The study of development’s political and economic causes and consequences also requires common understandings of which countries are poor and that the data from which these understandings are drawn be reasonably accurate. Social scientific analyses also require that these data be unbiased, which is to say that whatever inevitable errors exist in the data be orthogonal to the political and economic attributes whose relationship to development social scientists seek to uncover. This paper considers a threat to the reliability and unbiasedness of development statistics in many social science applications.1 The threat to reliability and unbiasedness is as follows. Poorer countries are typically eligible for and receive more aid than richer countries. However, the statistics used to establish which countries are poorer than others represent economic concepts that cannot be observed directly. They must be estimated and these estimates are based on data that originates in national statistical offices. Aid-seeking countries may therefore prefer to report estimates that give them the appearance of being poor.2 Doing so need not entail reporting data that is per se “wrong. ” The range of plausible estimates can be quite large, and national statistical agencies from aid-seeking countries may simply choose from the large set of plausible estimates those that are likely to generate more aid.3 We’ll refer to such a dynamic as 1 When we refer to the “biasedness” of data we are referring to the suitability of data in a particular context, rather than any intrinsic quality. We are particularly concerned about the use of data to answer questions such as “Does democracy promote development? ” or “Does foreign aid promote development? ” To be unbiased in these contexts requires that errors in the data are not correlated either democracy or aid-dependence. 2 Aid-seeking is surely not the only motive a government might have to control their data. Other governments likely want to appear to be richer, larger, or growing faster than they really are. Argentina, for example, was recently censured by the IMF for providing inaccurately rosy inflation and GDP growth data (Economist 2013). 3 For example, recent rebasing of Nigerian GDP data suggests that prior estimates understated Nigerian GDP by as much as 60%. http://www.economist. com/blogs/baobab/2014/01/nigerias-economy-will-soon-overtake-southSimons Papers in Security and Development No. 37/2014 6 “data manipulation,” though we reiterate that in this context data manipulation does not imply inaccurate or unjustifiable estimates, but simply estimates that are purposefully chosen in order to further a country’s aid-seeking interests. The potential for aid-seeking data manipulation is mitigated by the central role that international organizations play in the oversight, production, and dissemination of data. International organizations’ role in this process is important and intentional (Barnett and Finnemore 2004, 2; Abbott and Snidal 1998, 8; Keohane 2005 92; Koremenos, Lipson and Snidal 2001, 766). The authority of data released by international organizations reflects users’ trust that the data represent that organization’s best approximation of reality, uncolored by individual countries’ political or economic interests (Abbott and Snidal 1998, 20). In practice, however, the disseminators of international data rely exclusively on the official estimates provided by member countries. The World Bank, for example, typically adjusts nationally reported figures, but if there are problems with the national data, the international datasets may inherit them (Jerven 2013a). 4 This paper asks whether the macroeconomic datasets commonly used by researchers evince any evidence of aid seeking through data manipulation. The possibility that there exists a “political economy of data” is notable in itself and these sorts of questions are taking on increasing prominence in the academic literature (on this topic, see, for example, Ward 2004; Finnemore 2013; Hollyer, Rosendorff, and Vreeland 2011; Wallace 2014; De Castro, Perez, and Vives 2011; Alt, Lassen, and Wehner 2014). Our secondary purpose in asking this question refers to the use of development statistics in social scientific contexts. Aid-seeking data manipulation would complicate empirical studies based on the relationship between aid and development as the countries that receive more aid would also be more likely to manipulate their data in ways that would dampen the appearance of economic growth. These problems would be compounded if certain kinds of countries were systematically more willing to report low estimates of their development levels and/or more able to have those low estimates reflected in africas 4 As Jerven (2013b) notes, the process by which the data reported by national statistical offices gets translated into the data reported by the World Bank is proprietary to the World Bank. Are Development Statistics Manipulable? 7 the data released for social scientists use. To that end we ask whether there exists evidence of data manipulation in general and whether such evidence is more apparent in the data from countries with more incentive or opportunity to manipulate their data. We consider two possibilities concerning which countries should be more likely to successfully aid-seek through data manipulation. Our central hypothesis considers the possibility that governments in relatively aid-dependent countries may be more willing to appear poor on paper if it facilitates continued aid flow.5 We also consider the relationship between data manipulation and democracy. In particular, we consider the possibility that democratic governments are viewed as being trustworthy and that this perception might, perversely, allow their manipulated estimates to receive less scrutiny by international organizations and therefore be more likely to manifest in the released data.6 To explore whether aid-seeking data manipulation occurs we analyze the distribution of gross national income (GNI) per capita statistics around the eligibility threshold for grants and concessional loans from the World Bank’s International Development Association (IDA). These data are well suited to our purposes for two reasons. First, IDA eligibility matters. The IDA is an important capital source for poor countries, and the eligibility