managed, mapped, and manipulated for explicit policy purposes. We hasten to add that such “thin simplifications” (Scott’s term) of reality can be both benign and widely beneficial: comprehensive health insurance programs and pension systems have largely tamed the otherwise debilitating historical risks of, respectively, disease and old age by generating premiums based on general demographic characteristics and the likelihood of experiencing different kinds of risks (e.g., injuries, cancer) over the course of one’s life.

A less happy aspect of apprehending deep contextual variation via simplified (often categorical) data, however, is the corresponding shift it can generate in the political status and salience of social groups. The deployment of the census in colonial India, for example, is one graphic demonstration of how the very act of ‘counting’ certain social characteristics – such as the incidence of caste, ethnicity and religion – can end up changing these characteristics themselves, rendering what had heretofore been relatively ‘fluid’ and ‘continuous’ categories as ‘fixed’ and ‘discrete’. In the case of India, this massive exercise in data collection on identity led to “caste” being created, targeted and mobilizable as a politically salient characteristic that had (and continues to have) deep repercussions (e.g., at independence, when Pakistan split from India, and more recently the rise of Hindu nationalism) (see Dirks 2011).<sup>11</sup> More recently, influential scholars have argued that the infamous Hutu/Tutsi massacre in Rwanda was possible at the scale at which it was enacted because of ethnic categories being formalized and fixed via public documents whose origins lie in colonial rule (e.g., Mamdani 2002).

For Scott (1998), public administration can only function to the extent its measurement tools successfully turn wide-spread anthropological variation, such as languages spoken, into singular modern categories and policy responses (e.g., to ensure that education is conducted in one national language, in a school, on the basis of a single curriculum<sup>12</sup> ); the net welfare gains to society might be unambiguous, but poorer, isolated, marginalized, and less numerous groups are likely to bear disproportionately the costs of this trade-off. If official ‘data’ itself constitutes an alien or distrusted medium by which certain citizens are asked to discern the performance of public agencies, merely providing (or requiring) “more of it” is unlikely to bring about positive change. In such circumstances, much antecedent work may need to be undertaken to earn the necessary trust from citizens, and to help them more confidently engage with their administrative systems.<sup>13</sup> By way of reciprocity, perhaps it will also require such systems to interact with

> 11 One could say that this is a social scientific version of the Heisenberg Uncertainty Principle, in which the very act of measuring something changes it. See also Breckenridge (2014) on the politics and legacy of identity measurement in pre- and post-colonial South Africa, and Hostetler (2021) on the broader manner in which imposing singular (but often alien) measures of time, space and knowledge enabled colonial administration. More generally, Sheila Jasanoff’s voluminous scholarship shows how science is a powerful representation of reality, which when harnessed to technology can reduce “individuals to standard classifications that demarcate the normal from the deviant and authorize varieties of social control” (Jasanoff 2004: 13).

> 12 Among the classic historical texts on this issue are _Peasants into Frenchman_ (Weber 1976) and _Imagined Communities_ (Anderson 1983). For more recent discussions, see Lewis (2015) on “the politics and consequences of performance measurement” and <mark>Beraldo and Milan (2019) on the politics of Big Data.</mark>

> 13 This is the finding, for example, from a major empirical assessment of cross-country differences regarding Covid19 (Bollyky et al 2022), wherein – controlling for a host of potential confounding variables – those countries with both high infections and high fatalities are characterized by low levels of trust between citizens and their government, and between each other. See further discussion on this study and its implications below.

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