International Statistical Review (2005),73, 2, 223–226, Printed in Wales by Cambrian Printersc© International Statistical Institute

Statistics and the Wealth of Nations

Stephen M. Stigler

University of Chicago, Statistics Department, Chicago, USA. E-mail: stigler@galton.uchicago.edu

Summary

The relationship of statistics and national prosperity is not a simple causal one, but some historicalexamples suggest a persistent association. A number of examples are considered, from the politicalarithmetic of the seventeenth century to the quality assurance methods of the 1950s, and a few speculativeconclusions are offered.

Key words: John Graunt; William Petty; Statistical education; Process design.

1 Introduction

Statisticians, almost by reflex, will nod assent to the proposition that statistics plays a vital rolein assuring national prosperity. But what is the basis for this belief? Adam Smith is silent upon thisgeneral issue in his great treatise, as are Ricardo and Mill. It is true that the association betweenstatistics and national prosperity is strong and has been noted over a long time. Generally speaking,over the past 400 years the more prosperous a nation-state is, the more extensive and the higherquality its system of national statistics. And the better the national statistics, the more prosperousthe nation. There has been a similar, if less pronounced, association between statistical research andprosperity. But as strong as these associations are, little is known about their cause. Do good statisticsbreed prosperity, or does prosperity induce better statistics, or is the relationship more complex thanthrough a simple unidirectional causation?

2 Some History

The antiquity of the relationship is clear. While a few literary works survive from the ancientand prosperous civilizations of the Middle East, these are vastly outnumbered by clay tablets thatwe could loosely categorize as being economic statistics: agricultural accounts and censuses, andrecords of transactions or contracts of one sort or another. In more recent times, as the preparationand study of such records became a recognizable form of statistical science, the science tended to bepracticed more assiduously in the more prosperous nations. In reviewing this more recent history Ishall principally limit the discussion to the history that I know the best, to European history.

In England in the 1600s, John Graunt and William Petty made systematic studies that startedwith observations drawn from public health records. Graunt’s background was as a tradesman, buthis curiosity was broader. In a path-breaking work first published in 1662, Graunt used the recordsof deaths in London’s parishes to try to estimate the population and particularly the population ofmen aged 16 to 56, what he termed “fighting men”. Graunt was mainly concerned with questionsof population and public health, but his friend William Petty (1691) subsequently went further,inventing the term “Political Arithmetick” for the science of deriving comparative figures on therelative wealth of European nations from trade statistics, public health records, and parish level data.

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Indeed, Petty’s stated goal was to correct widespread misapprehensions about the wealth and trade ofEngland, in order to better advise the Crown on economic policies needed to direct the nation. Pettysought to use his statistical methods to counter what he believed was an unwarranted but widespreadpessimism about England’s fortunes, by demonstrating England’s relative prosperity compared toother European nations, and at the same time to explain the basis of that wealth so that nationalpolicies could be better directed. Those were indeed times of great British economic strength, buteven the fact of that strength could be missed without statistical support. Petty sought scientificunderstanding with the explicit goal of national prosperity.

In that same century in France, Blaise Pascal and Pierre Fermat were exploring an early probabilitytheory that foreshadowed modern financial engineering, while in Holland, Christian Huygens workedon many of the same problems and Jan de Witt was taking first steps toward a mathematical frameworkfor life insurance. To a degree the pursuit of all of these statistical sciences in these European nationsrose and fell with their economic fortunes. With the rise of British sea trade and the decline of Dutchtrade, there was a rise in British statistics while Dutch work in that area languished. As Frenchinfluence increased in the late 1700s and with the Napoleonic era, so too did their statistics, bothmathematical (Laplace) and national (from Turgot and Necker to the development and spread ofstatistical accounting under Napoleon and even afterwards, as in the compilations of Fourier andChabrol). So too the unification and growth in economic power of Germany was accompanied by agreat increase in the collection and analysis of economic statistics, from the early national statisticsaround 1800 (Lueder, Meusel) to the vast Prussian publications towards the end of the nineteenthcentury and the advances of Lexis and Bortkiewicz.

The formal introduction of regular censuses also was tied to national growth, in the United States,Great Britain, and France, where censuses began to take hold as growing territories—empires in somecases—came to cope with the need to “number the people” for all sorts of reasons, from taxationto conscription to determining needed social services. And smaller nations and states exhibited thesame association, on a smaller scale. Belgium’s statistical apparatus expanded immensely with theformation of the Belgian nation in 1830, due in large measure (but not entirely) to Adolphe Quetelet.Individual states in the United States, such as Minnesota from the 1850s, had their own statisticaloffices and publications from the time of their initial expansion in population and prosperity.

India developed its statistical offices already in the 1800s, as part of the British Empire, and thenfrom the 1930s under the management of Mahalanobis they achieved first-class status separate fromBritish Civil Service, a status they maintain today. Australia, which had been content with statestatistical offices before they were federated under one constitution in 1901, soon developed a trulyexcellent system of national statistics, an excellence that persists to this present day. I do not knowthe state of Japanese statistics prior to 1945, but the national prosperity after 1950 was closely relatedto the adoption of statistical quality control methods in industrial production. And in modern Koreaof course we also see this association strongly in evidence. Conversely, one might cite the exampleof the general weakness of Soviet statistics leading up to their economic difficulties in the 1980s,notwithstanding their extraordinary strength in mathematics and theoretical probability over thisperiod.

3 Some Speculation

The relationship between statistics and prosperity has not been perfect; counterexamples canbe found; but it has been general and persistent. What sorts of causal relationships underlie thisassociation? Were first-class statistical agencies and first-class statistical research simply luxuries thatonly a prosperous nation could afford? Or were they intrinsic to the achievement of that prosperity?Were good national statistical systems, including statistical education and research, necessary toprosperity, or were they merely symptoms of that prosperity? Put bluntly, is prosperity good for

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statistics or is statistics good for the economy?Statistical development has certainly been made feasible by increasing prosperity. But it seems

equally undeniable that statistical development itself could play a crucial role in achieving increasedprosperity, even if one accords the lion’s share of the credit to other factors Adam Smith studied, suchas free markets, natural resources, and human capital. To demonstrate this conclusively is beyond thereach of methods of causal inference, partly because data on historical wealth far exceeds objectivedata on statistical systems and their development. But a plausible speculation of why it should betrue is not difficult.

To see how investments in statistical expertise can earn huge benefits at low cost, considerstatistical experimental design, itself the product of statistical research, and the role it has playedin manufacturing in some sectors of the US economy. In the 1960s, it was common to find thatproduction levels in chemical manufacturing plants were acceptable, leading to moderate profitsand complacency by managers, understandably reluctant to tamper with a functioning process. Butstatisticians found that by the use of on-line statistically designed experimentation,minor adjustmentsin production settings could be found that could increase yields by (say) 0.5%. To some that maysound like a very small gain, but it is not. It represents a gain that comes at essentially no increasein production cost, and it could be a large fraction of the profit margin: perhaps a 25% increase inprofit for a few hours of well-planned statistical work.

Just so, the gain from a well-run national statistical system, including research and education, is again in production efficiency. Such a gain would entail a possibly substantial decrease in waste andincreased delivery of services, fewer unwarranted changes in policy directions, and better estimatesof future demands. The costs of the statistical system are non-trivial but not huge. The marginal costof an excellent statistical system over the record keeping that would be needed simply to surviveis small. But more is needed: without the associated statistical educational system and trainingin research on statistical methods, the means of maintaining the system with a steady supply ofprofessional statisticians is lacking. The gains to be had from such investment come on top of thereturns to an already functioning economic system: gains in efficiency can be the least costly and themost rewarding.

Historically, we may see in retrospect how this has worked. In France, the finance ministers ofthe last decade of the old regime struggled to build a new economic statistics system. After thechaotic years of 1793–1797, Napoleon exerted strong leadership to build civil systems capable ofadministering an empire, and also an educational system to provide staff for the civil system. Frenchscience thrived, French government thrived, and even after 1815 France was able to recover withremarkable speed to achieve a century of remarkable prosperity. Their statistical systems permittedefficient administration and played an important role in this. In England in the late 1600s thesituation was much the same. William Petty’s statistical thinking reflected an approach to efficientadministration that helped lead the nation, working with and through the South Seas Company,to a remarkable era of prosperity. It was not so much a question of control; control of events onthe high seas of the late 1600s was not to be guaranteed by any system. Rather it was a questionof information, both for planning and rapid flexible adjustment, with able local offices having theauthority and capability to use local statistical information effectively. The ability three centuries agoto learn from and coordinate distant national outposts is like the ability to plan and execute at thelocal stations in modern manufacturing plants while coordinating with a wider distribution network.Statistical information, understanding, and training can increase the efficiency of any system. Justso, national prosperity is inextricably bound to good information systems, where this of necessitymeans not simply enhanced efficiency in communication, but also effective use of trained statisticalunderstanding to plan and execute.

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References

Graunt, John (1662).Natural and Political Observations Mentioned in a Following Index and Made upon the Bills of Mortality.Petty, William (1691).Political Arithmetick. Written in the 1670s.Stigler, S.M. (1986).The History of Statistics: The Measurement of Uncertainty Before 1900. Cambridge, Mass: Harvard

University Press.

Related Reading

Stigler, S.M. (1986).The History of Statistics: The Measurement of Uncertainty Before 1900. Cambridge, Mass: HarvardUniversity Press.

[Received March 2005, accepted May 2005]