<mark>International Development (USAID, an organization charged with “demonstrating the impact of every aid cent that Congress approves”), argues compellingly that the tendency in aid and development towards what he called “Obsessive Measurement Disorder” (OMD) is a manifestation of a core dictum among field-based development p</mark> ractitioners – namely “that those development programs that are most precisely and easily measured are the least transformational, and those programs that are most transformational are the least measurable.” The change we often desire most is in very difficult-to-measure aspects, such as people’s habits, their cultural norms, leadership characteristics, or mindsets.

This reality is also aptly illustrated in many anti-corruption efforts, whereby imported solutions have managed to change the easy-to-measure – new legislation approved, more cases brought, new financial systems installed, more training sessions held – but failed to shift cultural norms regarding the non-acceptability of whistle blowing or the social pressures for nepotism (Andrews 2013). A failure to measure and therefore manage these informal drivers of the problem ensures that any apparent reductions in fund abuses tend to be short-lived or illusory.

<mark>This phenomenon is hardly limited to poor countries. A more brutal example of how what cannot be measured does not get managed, with disastrous results, can be found in the UK’s National Health System. Wh</mark> ile investigating the effects of competition in the NHS, Propper et al. (2008) discovered that the introduction of inter-hospital competition improved waiting times while also substantially _in_ creasing the death rate following emergency heart attacks. The reason for this was that waiting times were being measured (and therefore managed), while emergency heart-attack deaths were not tracked, and were thus neglected by management. The result was shorter waiting times but more deaths as a result of the choice of measure. The authors note that the issue here was not intent, but the extent to which one target consumed managerial attention, to the detriment of all else; as they note, it “seems unlikely that hospitals deliberately set out to decrease survival rates. What is more likely is that in response to competitive pressures on costs, hospitals cut services that affected [heart-attack] mortality rates, which were unobserved, in order to increase other activities which buyers could better observe” (Propper at al. 2008).

More recently, in October 2019 the Global Health Security Index sought to assess which countries were “most prepared” for a pandemic, using a model that gave the highest ranking to the United States and the United Kingdom, largely on the basis of these countries’ venerable medical expertise and technical infrastructure, factors which are readily measurable.<sup>22</sup> Alas, the model did not fare so well when an actual pandemic arrived soon thereafter: a subsequent analysis, published in the _Lancet_ on the basis of pandemic data from 177 countries between January 2020 - September 2022, found that “[p]andemic-preparedness indices ... were not meaningfully associated with standardised infection rates or IFRs [infection/fatality ratios]. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates” (Bollyky et al et al 2022, p. 1).

Needless to say, variables such as ‘trust’ and ‘government corruption’ are (a) hard to measure, (b) hard to incorporate into a single theory anticipating or informing a response to a pandemic, and (c) map awkwardly onto any corresponding policy instrument. For present purposes, the 22 See https://www.statista.com/chart/19790/index-scores-by-level-of-preparation-to-respond-to-an-epidemic/ 12