Histories of how statistical thinking developed and became embedded in government, science, and social life — essential context for understanding what numbers about poverty can and cannot tell us.
Porter (2020)
IntermediatePorter argues that the drive to quantify is not simply a response to scientific success but a political and cultural phenomenon: numbers are trusted precisely because they appear to transcend individual judgement. For poverty measurement, this insight is crucial — the poverty line's credibility depends on its numerical form, not on its empirical adequacy.
Hacking, I. (1990). The Taming of Chance. Cambridge University Press.
IntermediateHacking traces the nineteenth-century emergence of statistical reasoning as a way of governing populations, from actuarial tables to crime statistics. His concept of "making up people" through classification — turning statistical categories into social kinds — is directly relevant to how poverty thresholds create the very population they purport to describe.
Gigerenzer, G., Swijtink, Z., Porter, T., Daston, L., Beatty, J., & Krüger, L. (1989). The Empire of Chance: How Probability Changed Science and Everyday Life. Cambridge University Press.
IntermediateA collaborative history of probability and statistics from the seventeenth century to the present, written by historians and philosophers of science. More accessible than Hacking or Desrosières, it is especially strong on the passage from classical probability to frequentist statistics and on the reception of probabilistic reasoning in different sciences.
Espeland, W. N., & Stevens, M. L. (2008). A sociology of quantification. European Journal of Sociology, 49(3), 401–436.
IntermediateA concise sociological framework for understanding how quantification transforms the things it measures. Espeland and Stevens introduce the concept of "reactivity" — how measures change the behaviour of the people and institutions they track — which is directly applicable to welfare conditionality and poverty line politics.
Desrosières (1998)
AdvancedDesrosières offers a comparative history of national statistical systems, showing how different countries' political cultures produced different statistical traditions. His account of how statistics and the state mutually constituted each other over two centuries is indispensable for understanding why poverty measurement looks so different in the US, UK, and France.
MacKenzie (1981)
AdvancedMacKenzie's sociological study of British statistics from 1865–1930 shows how statistical methods were developed in the context of eugenics and class politics. The correlation coefficient, the chi-squared test, and regression analysis were not neutral tools — they were instruments shaped by the social anxieties of their creators. Essential background for any critical use of statistics.
Stigler, S. M. (1986). The History of Statistics: The Measurement of Uncertainty before 1900. Harvard University Press.
AdvancedStigler's authoritative history of statistics traces the development of least squares, regression, correlation, and Bayesian inference, with detailed attention to the mathematical ideas and their social contexts. Less critical than MacKenzie but more technically thorough; useful as a reference for understanding the mathematical machinery before reading the critical histories.
Daston, L. (1987). The domestication of risk: Mathematical probability and insurance 1650–1830. In L. Krüger, L. J. Daston, & M. Heidelberger (Eds.), The Probabilistic Revolution: Vol. 1. Ideas in History (pp. 237–260). MIT Press.
AdvancedDaston's essay on the social history of probability shows how mathematical risk was domesticated for commercial insurance contexts long before it entered scientific practice. This historical grounding helps explain why probabilistic concepts feel natural in welfare contexts and why they carry ideological freight about individual responsibility and actuarial fairness.