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Quantifying Mortality in Hungary: Actuaries and Statisticians (1860s-1910s)

Quantifier la mortalité en Hongrie : actuaires et statisticiens entre 1860 et 1910
Mátyás Erdélyi
p. 115-138

Résumés

L’article étudie la formation d’un savoir statistique à partir des tables de mortalité construites par les statisticiens de l’administration et les actuaires des compagnies d’assurances dans la partie hongroise de la monarchie des Habsbourg entre 1860 et 1910. La logique et la fonction des statistiques de mortalité étaient fondamentalement différentes dans les deux cas. Les statisticiens et les actuaires ont construit leurs tables et ont utilisé des formalisations mathématiques et statistiques en fonction d’objectifs distincts : projet de réforme sociale pour les premiers, et rentabilité des compagnies d’assurance pour les seconds. Ces aspects ont non seulement déterminé le choix des méthodes, mais également les fondements épistémologiques et l’objectivité de leur démarche.

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Texte intégral

  • 1 Z. Ráth, 1893, pp. 729-731.

1Mortality represents a prime object to study the social construction of statistical knowledge from a historical perspective. Death itself is a quite obvious phenomenon, yet, one could describe the mortality of a population by a multitude of indicators and rates based on the mortality of the overall or select populations for a given period divided, again, by overall or select population figures. The lack of standard measures became a source of controversy when statisticians noted that regional and international mortality comparisons varied depending on the indicator chosen. The mortality rate of Budapest – yearly overall mortality divided by the standing population – was, for example, lower than the corresponding figure in the Hungarian countryside. Yet, in terms of age specific mortality rates, all age groups in the capital had significantly higher mortality than the corresponding population in the countryside.1

2The present paper deals with the social construction of statistical knowledge based on mortality statistics compiled by government agencies (statisticians) and insurance companies (actuaries) in the Hungarian part of the Habsburg Monarchy between the 1860s and the 1910s. The main object of inquiry is the mortality table. Mortality tables generally covered a cohort of 1,000 to 100,000 persons and predicted the average number expected to remain living year up to the age of 100. These tables were generally used by insurance companies to calculate premiums. The other important indicator of mortality, the mortality rate, represents the number of deaths occurring in a given territory in a given time per unit of population, most often the number of deaths per year and per thousand inhabitants. It does not take account of the age structure of the population.

3The comparison of mortality statistics in government and insurance contexts provides a crucial means to study the application of mathematical tools in statistics. The logic and function of mortality tables in government and insurance contexts were fundamentally distinct; statisticians and actuaries applied quite separate approaches for the construction of their object of analysis and the application of mathematical and statistical tools. For actuaries, the aim was to guarantee the profitability of the business enterprise – mortality tables being the most crucial proxy in this endeavor. In the ventures of statisticians and hygienists, on the other hand, mortality rates represented an end in themselves. These experts made inferences about the causality of living conditions, infectious disease, poverty and mortality based on available statistics with the aim of providing guidelines for state intervention and helping to reduce the mortality of the local populace.

  • 2 A. Desrosières, 1998; D. A. MacKenzie, 1981; J.-G. Prévost & J.-P. Beaud, 2015; T. M. Porter, 1986 (...)
  • 3 T. M. Porter, 1995.
  • 4 F. K. Ringer, 2000, p. 4.
  • 5 O. Rey, 2016.

4The driving framework of the present investigations is the historical development of credibility in quantification.2 The pursuit of mechanical objectivity plays a key role in the competition for expert credibility. Mortality figures are used as quantitative and standardized modes of measurement which provide a technology of distance to ensure compliance with impersonal rules and calculations, and thus enabling experts to exclude bias and personal preferences.3 The standardization of mortality statistics encountered particular difficulties in both contexts. Insurance companies manipulated mortality tables in order to secure “mortality gains” that formed a substantial part of their profit in the pre-war era, whereas statisticians were under pressure to produce mortality rates more favorable to public authorities and their employers. The pursuit of expert credibility involved a power struggle over control, both social and intellectual. In the words of Fritz Ringer, they constantly competed for “the right to define or to co-define what shall count as intellectually established and culturally legitimate.”4 The main thread of the present argument is thus to find similarities and dissimilarities between the government and corporate accounts of mortality statistics in the Habsburgian context. While the empirical basis of their inquiry is plainly identical, namely, the aggregate of individual death, the methods and theories they applied were utterly different. While both social statisticians and actuaries formed part of the process in which the world became “made of numbers,”5 the objectives they pursued in the development of quantification were fundamentally different.

5The paper starts by examining the mortality statistics of government and municipal agencies before the turn of the century and describes how they became a reflection of Hungary’s relative backwardness in the nineteenth century. It then turns to the early professionalization of insurance mathematics in Hungary following the establishment of insurance companies in the country, in close connection with the rise of business education, and very much under the auspices of the Hungarian Academy of Sciences. The third part introduces the insurance companies’ joint Austrian-Hungarian project to develop mortality tables after 1900, which took place in parallel with the construction of mortality tables based on the Hungarian 1900 census under the guidance of the national statistical office. The last part of the paper deals with the epistemological consequences of insurance practices and their influence on the conceptual nature of mortality tables.

1. Mortality rates as a proxy of modernity

  • 6 Magyar Statistikai Évkönyv.
  • 7 Budapest Főváros Statistikai Hivatalának Közleményei.
  • 8 J. Kőrösy, 1878, p. 122.
  • 9 J. Kőrösy, 1878, p. 128.

6The establishment of the national statistical office (1867) and the municipal statistical office of Pest (1870) led to the rapid development of demographic statistics in Hungary. Results were published periodically in the Hungarian Statistical Yearbook6 and in the bulletins of the municipal statistical office.7 The former published results without a detailed commentary, while József Kőrösy, the director of the latter, was keen to turn his data into inferential statistics and identify the causes of high mortality in the capital. Kőrösy’s agenda was clearly formulated and adopted by other statisticians as well. For these scholars, the raison d’être of mortality rates must be sought in their practical application. The fact that mortality rates increased across Europe from West to East confirmed “the natural effects of the general law concerning European cultural development,”8 which had to be acted upon, and not simply ignored through self-deception. Budapest, and Hungary in general, needed comprehensive health reforms to “catch up” with the more favorable mortality rates in the West. It was important to publicly acknowledge the reason for Hungary’s relative backwardness; remedies could only be found that way. The measures suggested by Kőrösy were better drinking water supply, the reform of foundling care, improvement of housing conditions, the elimination of basement apartments, and the reform of municipal hospitals.9

  • 10 B. Földes, 1884, p. 257.
  • 11 B. Kenéz, 1906.
  • 12 J. Bud, 1907, p. 234.

7Others also saw mortality statistics as an indicator of modernity. As such, they illustrated the relative backwardness of Hungarian society. Béla Földes (1848–1945), professor of statistics at the Pester Handels-Akademie and at the Budapest University, called mortality a “yardstick of people’s prosperity and culture,”10 it was the task of culture and science to lower mortality rates to the “natural extent”. At the turn of the century, Béla Kenéz described mortality as the “index of civilization,”11 and János Bud called it “the strongest measure of the development of culture”.12

  • 13 J. Kőrösy, 1891a, p. 134.
  • 14 J. Kőrösy, 1878, p. 119.
  • 15 J. Kőrösy, 1876, pp. 3-4.

8The drive towards regional and international comparisons was the consequence of this conceptualization of mortality as an indicator of modernity. The main analytical tool of such mortality statistics was thus the comparative dimension of cross tables. This, at the same time, presupposed a certain degree of standardization at the international level. In Kőrösy’s words, the “statistical photo”13 could be meaningful only if commensurable, since “statistical conclusions could only be made through comparisons.”14 The 1872-1873 mortality rates in Pest, compiled by Kőrösy, were therefore presented in the form of a comparison with 41 cities of more than 150,000 inhabitants.15 Mortality rates in the Hungarian capital (47.7 per thousand) were twice as high as those of the most advanced cities, such as Philadelphia (19.3), Paris (22.4) and Vienna (24.6).

  • 16 Közegészségügy és törvényszéki orvostan - Melléklet az Orvosi Hetilap 41-dik számához, no. 5, 1877 (...)
  • 17 J. Kőrösy, 1878, p. 133.
  • 18 Borsszem Jankó, vol. IX, no. 48, 1878, p. 5.

9The result was inevitably displeasing to local notables because it suggested that poor sanitary conditions were responsible for the high mortality of lower-class inhabitants in the capital. The validity of Kőrösy’s statistics was immediately challenged,16 forcing him to clarify why and how his numbers should be acknowledged, taken as facts, and acted upon. Kőrösy sarcastically compared his critics, who doubted the accuracy of mortality rates in Pest, to the case of Schleswig in Imperial Germany, whose population angrily destroyed the poles recently installed to warn of a coming storm, because they naively presumed that it must be the poles themselves that had caused the tempest.17 Peppercorn Jackie, a satirical journal, duly ridiculed Kőrösy’s opponents: mortality rates for Budapest must have been overestimated in any case, since “the Hungarian man, even if dead, has more vitality than a Dutchman – so he can be simply omitted from the list of the deceased.”18

  • 19 G. Körmendi, 1989; M. Szalay, 2006; G. Vargha, 1903.
  • 20 J. Jekelfalussy, 1892, p. 60.
  • 21 J. Kőrösy, 1891a; J. Jekelfalussy, 1891; J. Kőrösy, 1891b; J. Jekelfalussy, 1892.

10The theoretical underpinnings of Kőrösy’s work were harshly attacked by a fellow statistician, József Jekelfalussy. The social gulf between the two men could not have been wider: Kőrösy was the son of an impoverished Jewish merchant, with no university education, who had recently Magyarized his name from Hajduska to Körösi, and, on the occasion of his ennoblement, to Kőrösy. He was a member of the upwardly mobile Jewish middle class viewed with contempt by the gentry.19 Jekelfalussy, a landowner’s son, noble by birth, and a former soldier, possessed all the necessary social prerogatives to express his disdain for Kőrösy by quoting Phaedrus: “ignotos fallit, notis est derisui.”20 Jekelfalussy’s frequent use of Latin phrases and expressions, completely absent from the writings of the Jewish statistician, was a status symbol connecting him to the ideal of the classical Bildung. It was for the same reason that he saw himself as the protector of Hungarian statistics in response to Kőrösy’s vanguard ideas about the nature of statistical inferences.21

  • 22 J. Jekelfalussy, 1891, p. 947.

“It is something else that obliged me to speak in this journal, although, I have to admit, it is rather difficult to contribute to such an odious thing; however, those who are active in periodicals have the moral obligation to pull out all the weeds from the press.”22

  • 23 J. Jekelfalussy, 1891, p. 948.

11According to Jekelfalussy, Kőrösy’s work had never really been discussed in Hungarian periodicals, though the director of the municipal statistical office tended to boast about his international success, which could never really be verified by fellow statisticians at home. Jekelfalussy claimed that his rival liked to focus attention on himself, to attitudinize, but in fact only managed to show his ineptitude and to impose “yackety-yaks” on the public. “[…] trying to find meaning in meaninglessness is a useless trouble,”23 he concluded. While the harshness of this treatment might appear shocking, the debate symbolized a lot more than a mere theoretical tussle between the two statisticians.

  • 24 J. Kőrösy, 1874, p. 88.

12As a response to critics, in 1874, Kőrösy set out the ideal method by which average life expectancy and mortality should be recorded and calculated. The most important criterion was that no a priori assumption should precede calculations; the statistician had to determine the figures based on the “unbiased perception of phenomena”.24 A major part of the 1874 study dealt with the refutation of all prior deduction-based methods, like those of Süssmilch, Halley, and Farr. In contrast, Kőrösy proposed an inductive approach for calculating mortality rates and suggested implementing an “individual and direct method”. The core of this method was to register every death individually on the census files in each municipality. On the occasion of the next census, all those who had moved to another municipality could be removed from the list and added to the corresponding list of their new residence. By observing the trajectory of each individual, the statistician could thus avoid the numerous analytical assumptions inherent to other methods.

  • 25 T. Szél, 1927, pp. 127-131.
  • 26 E. Bratassevič, 1891, p. 83.
  • 27 Borsszem Jankó, vol. V, no. 241, 1872, p. 98.

13However, Kőrösy’s method also entailed methodological errors and was impossible to implement, as his failed attempts demonstrated.25 One such error was the omission of migration that significantly influenced the mortality of certain regions; migrants usually represented the young and vigorous sections of the population.26 The practical problem was that the individual method proved too costly and labor-intensive, so not even Kőrösy was able to implement it in Budapest. This issue could only be resolved using the method proposed by Peppercorn Jackie: the satirical journal kindly asked the deceased to go directly to the municipal statistical office and report their death and its cause.27 At the Chicago meeting of the Institut international de statistique, Heinrich Rauchberg justifiably lamented that Kőrösy’s method could be discussed in a meaningful way only if at least one model census was carried out successfully in Budapest.

  • 28 Z. Ráth, 1893, p. 98.
  • 29 Z. Ráth, 1893, pp. 90-99.
  • 30 Z. Ráth, 1893, pp. 81-82.

14The social and political context significantly influenced Hungarian statistical thinking. Gender bias is a case in point. In nineteenth-century censuses, age statistics relied on self-reported age and date of birth and this became a source of concern in the processing of data. The over-representation of age cohorts with a jubilee in the census year increased with the respondents’ age, which, of course, served as proof of large-scale inaccuracies in age reporting. To remedy the situation, the statistician could apply “probability reasoning”28 to identify the extent of the error in the raw data. Zoltán Ráth (1863-1902), an employee of the national statistical office and later professor of statistics at the Academy of Law in Kassa (Košice, Kaschau), interpreted the variation of age-specific proportions in the raw data as a proof of error.29 Once a significant error was identified, Ráth started to search for a possible cause, and he made a blatant distinction between men’s and women’s attitudes towards their age. For men, he argued, age misreporting was basically a rational means to avoid civic duties such as tax payment, military conscription, or compulsory education, which primarily concerned the younger generations. Women, on the other hand, were motivated by vanity and their age misreporting was an emotional action in the Weberian sense. For Ráth, young and middle-aged women wished to appear younger, whereas elderly women preferred to be seen as older than their true age.30

  • 31 The question on national identity changed between 1890 and 1900 from a mere “What is your mother t (...)

15The logic of nationalism also transcended statistical thinking. In terms of content, the efforts at Magyarization from above were accompanied by a careful statistical attention to processes of assimilation. To boost the proportion of Hungarians, the concept of ethnicity was defined in terms of language use from 1900,31 and more elaborate language statistics were produced in the 1900 and 1910 census publications. Nonetheless, ethnicity (language use) was not connected to mortality in statistical data, and statisticians had to use geographical categories to compare the mortality of different nationalities. For example, the mortality of counties having a Magyar majority (over 75 percent of the population reported Magyar as their language of primary use) was unconvincingly compared with that of counties having a Romanian or Slovak majority. Naturally, this type of comparison undermined the demographers’ ambition to produce theoretically unbiased and “scientific” statistics.

2. Early life insurance and mortality tables

  • 32 V. Ormody, 1908.
  • 33 The Hungarian Statistical Yearbook published data on life insurance only from 1895 and plainly dif (...)
  • 34 Magyar Biztosítási Évkönyv.

16The actuarial sciences remained in an embryonic state in Hungary until the turn of the century. Before the first “Magyar” life insurance company was established in 1857,32 only “foreign” insurance companies had operated in the country. For Hungarian business activities, they adopted mortality tables and premium calculations constructed at the home branch.33 Most companies calculated premiums based on a modified version of the English Seventeen Life Assurance Officers’ Table (1843), that remained the main reference until as late as the 1910s.34 The subsequent development of “Magyar” actuarial sciences was triggered by two factors: the need for proper actuarial calculations at the Erste Ungarische Assekuranz Gesellschaft and other Hungarian companies, and the rise of business education in Hungary. The two fields were interconnected on an individual level since many insurance mathematicians like Vincze Weninger, Jakab Lewin, Miksa Havas, and Samu Bogyó pursued parallel careers as business school professors. Their profiles were similar on many levels: they became professors at the Budapest Academy of Trade, published the standard textbooks on “political arithmetic” until 1914, were employed by insurance companies (except Lewin), and were of Jewish origin (except Weninger).

  • 35 E. Fényes, 1859.
  • 36 Contrary to assimilation trends at the turn of the century, Fényes remarked upon the Jewish commun (...)
  • 37 E. Fényes, 1844; id., 1860.
  • 38 E. Fényes, 1859, p. 25.

17The Erste Ungarische was keen to put its business on a firm basis and asked the renowned statistician Elek Fényes (1807-1876) to act as chief mathematician at the life insurance branch. Fényes had been an active public figure from the 1830s and published several statistical studies of the population and economy of Hungary. However, this collaboration was soon terminated because of a dispute between Fényes and the board. When Fényes decided to resign in 1859, he explained his decision in a virulent memorandum.35 In his view, the Erste Ungarische, contrary to its agenda, did not represent Hungarian interests; instead, the director of the institute, Henrik Lévay, was seeking to boost profits by merely increasing insurance tariffs. By governing the institute in a purely for-profit spirit, the national mission was being greatly hindered, and high tariffs discouraged potential clients. Another complaint was about the official language used, which was still German, a situation made worse by Lévay’s hiring of German-speaking Jewish agents and clerks.36 The plethora of complaints revealed a textbook confrontation between the for-profit logic of life insurance and the national agenda of the social reformer. Fényes had been committed to serving the Magyar cause37 and was unwilling to accept a profit-oriented business strategy at a “national” company. Fényes did not blame foreign companies for “profiteering,” but a patriotic insurance company should, in his eyes, “make sacrifices for the benefit of the Magyar nation”.38 Premiums should be determined according to the needs of the Magyar people and not the insurance company’s economic rationality.

18The remaining mathematical calculations were carried out by Vincze Weninger, a graduate of the Technical University and lecturer in political arithmetic at the Pester Handels-Akademie. In the early 1860s Weninger was convinced that only life insurance had a truly “mathematical basis” and

  • 39 V. Weninger, 1862, p. 254.

“in other types of insurance, the calculations of probability were overwritten by practical life, by experience, because the character of risk was changing so fast that even the experience of several years could not produce reliable calculations.”39

19Agricultural insurance was a case in point: weather forecasting was unable to anticipate risk and local conditions were highly variable, making it impossible to create homogeneous risk categories like the age groups of mortality tables. Life insurance, meanwhile, was a fertile ground for mathematical calculations.

  • 40 V. Weninger, 1875, p. 387.

“What occurred as a gamble for decades is no longer a gamble; we have gained knowledge of the limitations concerning risks in life insurance, and this knowledge now supports the most beautiful of humanistic institutions.”40

  • 41 V. Weninger, 1864.

20In Weninger’s thinking, the limited application of mathematical probability was soon replaced by a wide-ranging understanding of the law of large numbers. In 1864, he claimed that all types of insurance should have recourse to the law of large numbers. There was no event that did not follow a certain law from a numerical point of view, and even apparent hazards conformed to laws.41 Weninger strongly believed in the regularity of human actions and took Quetelet’s examples – the steady rates of suicide, marriage, and crime – to prove his point. In his eyes, the law of large numbers (referring to Poisson and not to Bernoulli) and la loi des accidents (referring again to Quetelet) ensured that statistical observations produced the same results from year to year. The logical base of insurance was thus founded upon sound statistical data and the principles of mathematical probability.

  • 42 L. Kőváry, 1884, p. 105.

21Regularity and the law of large numbers were key elements in the thinking of other insurance experts. László Kőváry (1819-1907), a historian and statistician in Transylvania, became acquainted with the insurance industry as an agent of the Erste Ungarische in Kolozsvár (Cluj, Klausenburg) and as the director of the local Victoria Insurance Company. He compiled an encyclopedia of insurance in the 1870s that remained largely unpublished except for the parts on life insurance. In his eyes, “actuarial sciences” were based on the “law of large numbers: this confirmed the assumption that individual accidental events all follow certain laws on the large scale.”42 This made it possible to calculate premiums for a given risk. The main problem of the actuarial sciences was thus framed in a probabilistic language: insurance mathematicians should calculate how long a person would probably live, how much in premiums this person would pay during the insured period, and when the insurance payout could be expected.

  • 43 V. Weninger, 1861-1862, pp. 54-59.

22The cases of Fényes, Weninger, and Kőváry show that actuarial practices and the study of demography were not clearly separated in the early history of life insurance in Hungary. Prior to their encounter with life insurance, all of them produced general statistical studies and were keen to apply the methods of demography in their actuarial endeavors. For example, Weninger’s main interest was in applied mathematics as a clerk of the Erste Ungarische (after 1858), head of department at the joint Ministry of Finances (1867-1870), and chief executive at the Hungarian General Credit Bank (1870-1879). Yet, he was elected as a correspondent member of the Hungarian Academy of Sciences, and this dual affiliation had an impact on his actuarial ideas. When talking about mortality tables, Weninger was keen to use census data and the mortality tables of the overall population in life insurance. For Weninger, three methods were ideally available to construct scientifically accurate mortality tables: one included the statistics of insured lives compiled by insurance companies and another was based on the decennial census data. However, the best empirical option was the panel study, to be carried out by village teachers, pastors, and public servants.43 This proposal was very similar to the individual method of Kőrösy, who, in contrast, turned to insurance practices as a source for his investigations of mortality rates.

3. The joint project of mortality tables

  • 44 Magyar Biztosítási Kurír, 17.10.1913.
  • 45 See the volumes of the Hungarian Insurance Yearbook (Magyar Biztosítási Évkönyv) that enumerated t (...)
  • 46 Biztosítási és Közgazdasági Lapok, 1904, no. 1, pp. 6-7; no. 2, p. 5.
  • 47 T. L. Alborn, 2009, pp. 109-110; W. I. Hughes, 1889, p. 21.

23The approach to mortality of business endeavors, especially insurance companies, differed from that of social reformers. The Magyar Biztosítási Kurír outlined the contrast between the practical goals of demographic statistics and the objectives of an insurance manager.44 The sources of profit in life insurance, it was claimed, were not connected to the business activities of companies. Instead, profit was created by the additional fee to cover the policy acquisition costs (the cost of the agent, already included in the net premium), by the surplus of interest when the accrued interest was above the preliminary calculations, and by the over-pessimistic calculation of mortality rates. In general, insurance companies were not interested in compiling up-to-date mortality tables relevant to Hungarian lives and they simply used a modified version of the Seventeen Life Insurance Officers’ Table.45 The profit of life insurance companies indeed depended to a large extent on actual mortality that was consistently below the estimated rates. In this way, Hungarian insurance companies earnt 1,114,954 Austro-Hungarian kronen of profit in 1890 and K 5,256,092 in 1900, which, of course, made it possible to pay dividends of five to ten percent each year to shareholders.46 This was not a uniquely Hungarian practice as Austrian, German, and English life insurance companies also tended to overestimate the mortality of insured lives to secure higher profits.47

  • 48 T. M. Porter, 1986, p. 6.

24The main difference between the mortality statistics produced by the likes of Kőrösy and those used by insurance companies could be found in the specific interpretation of the law of large numbers that was tailored to fit the goals of the given endeavor. Both believed in the “leitmotif of nineteenth century statistical thinking,”48 that order is to be found in large numbers, although their perspectives were very different. Kőrösy’s example showed that order was crucial in its implication of unknown causes that could account for social problems; interpretive statistics surpassed mere social accounting. In life insurance, on the other hand, order was a guarantee of the predictability and regularity of mortality, and thus made it possible to produce reliable and profitable calculations.

  • 49 G. Altenburger, 1907b.
  • 50 T. L. Alborn, 2009, pp. 296-312.

25The most important factor for calculating risk was the age of the insured person, but actuaries searched for other factors as well: nationality, the insured person’s particular body, hereditary disease, gender, occupation, wealth, marital status, and temperament. Even though calculations only extended to the age of insured persons, life insurance was very much an interdisciplinary enterprise. A central figure in the subsequent discussions, Gyula Altenburger (1866-1945), described the “science of life insurance” as a collection of mathematical, commercial, legal, and medical problems.49 This enumeration reminds us that along with the “numerical view of life”, as Timothy Alborn put it,50 insurance companies took into account other “manifestations of modernity” that affected their business: the “medical view of life”, the scientific evaluation of a given individual’s risk of death at any moment; the “commodified view of life”, the process of putting a value on human life; and the “sympathetic view of life”, that referred to the humanistic goals of insurance in general.

  • 51 Mitteilungen des Verbandes der österr. und ungar. Versicherungs-Techniker, Heft 2, 1900, pp. 1-2.
  • 52 Magyar Nemzet, vol. XIX, no. 197, 1900, p. 6.
  • 53 Absterbe-Ordnungen…, vol. I, 1907, pp. 14-15; Magyar biztosítottak halandósága, 1910, pp. XIII-XIV

26The construction of the Hungarian and Austrian insurance companies’ mortality tables was pursued as a joint project in both halves of the Habsburg Monarchy after 1901. Ernst Blaschke (1856-1926), the government insurance commissioner in Vienna and professor of insurance mathematics at the University of Vienna, initiated the project in 1899 at the meeting of the Verband der österreischischen und ungarischen Versicherungstechniker.51 This explains Blaschke’s influence on the implementation of the project and the similarities of the final tables in Austria and Hungary. The Hungarian part of the project was commissioned in July 1900 by the Minister of Trade, Sándor Hegedűs, and it also included the parallel construction of life tables for the overall population based on the 1900 census,52 a venture that was accomplished a decade later in Austria based on the 1910 census. The comparability of the Austrian and Hungarian mortality tables was further enhanced by the use of similar data gathering and processing methods (report cards, counting method, graduation method). This served the interests of companies that had life insurance branches in both parts of the Monarchy and resulted in the construction of life tables for the entire Monarchy, presented later at the Viennese international congress of actuaries in 1909.53

  • 54 Magyar biztosítottak halandósága, 1910, p. V.

27Insurance mathematicians became key actors in both projects in Hungary. Mortality tables based on the census data were constructed under the guidance of Jákó Raffmann (1858-1930), who created the mathematical basis for the calculations. Besides his telling links to astronomy, not much is known about Raffmann: born in 1858 in Nyitra (Nitra), he was an assistant clerk in the observatory in Ógyalla (Hurbanovo, Altdala) and in Vienna in the 1880s, and later joined Erste Ungarische as an actuary. The committee of the insurance project included such figures as Gyula Altenburger (Adriai), Károly Bein and Vilmos Ormody (Erste Ungarische), or James Klang and Alfréd Tauber (Phönix),54 all members of the Viennese actuarial society that served as a hub of professional collaboration for insurance mathematicians in both parts of the Habsburg Monarchy. In the Versicherungswissenschaftliche Mittheilungen, the journal of the association, papers were published and discussed by members from Vienna, Prague, Budapest, or Trieste. It thus served as a platform for knowledge transfer at the imperial level. In contrast, the Hungarian association of insurance mathematicians worked as a merely local meeting point. The Hungarian Insurance Yearbook published articles written solely by Magyar insurance experts, while the non-Magyar nationalities were not represented at all in the association’s proceedings.

  • 55 J. Raffmann, B. Kenéz & G. Vargha, 1906, p. 18*.

28The goals and methods of the two projects were quite different. The statistical office regarded mortality tables as a tool for social analysis and reform. Béla Kenéz (1874-1946), a clerk at the National Statistical Office and later professor of statistics at the university in Kolozsvár, suggested that the regional disparities in mortality pointed to the lack of social policy. It was unacceptable, for instance, that average male life expectancy was as high as 52 years and 11 months in Vas county, but only 27 years and 8 months in Trencsén county.55 Here, the relative backwardness of Hungary made it urgent to remedy the situation. Kenéz also expressed the old Statist view about the importance of human life for a strong state. “Human life” needs to be protected by public health measures, because it forms the most valuable part of a nation’s wealth. The mortality tables of insured lives served the business goals of insurance companies, as Vilmos Ormody, director of the Erste Ungarische, rather optimistically claimed:

  • 56 Magyar biztosítottak halandósága, 1910, p. XIV.

“The observed cases that are the basis of these tables, 5⅓ million observed years and almost 93,000 deaths among men and almost 1 million observed years and 22,000 deaths among women, form such a huge mass that these mortality tables will become assuredly the practical basis of insurance business.”56

  • 57 K. Goldziher, 1912, p. 465.
  • 58 K. Goldziher, 1912, p. 471.

29These tables were produced using the direct and individual method, with each insurance policy registered on an individual card (start date of the insurance, end date, reason for cancellation, etc.). All insurance policies contracted between 1876 and 1901 were taken into account in the survey. In the Budapesti Szemle, Károly Goldziher presented the work as a key step to “solidify the mathematical basis of life insurance and consequently to ensure business security”57 in the insurance industry. He also clarified the rationale for separating the two types of mortality tables: the mortality tables of insured lives addressed particular business objectives, and insured lives formed a homogenous group due to their medical selection.58

  • 59 J. Raffmann, B. Kenéz & G. Vargha, 1906, p. 2*.
  • 60 J. Raffmann, B. Kenéz & G. Vargha, 1906, pp. 23*-43*.
  • 61 Magyar biztosítottak halandósága, 1910, p. LVI. The Austrian project on the mortality tables of in (...)

30The methodological challenges that arose were closely connected to the empirical basis of the projects. Mortality tables based on the 1900 census had to tackle the problem of unreliable self-reported age, the issue being that people celebrating a jubilee in the year of the census were overrepresented and the proportion of ages ending in zero and five grew with the respondent’s age.59 This forced Raffmann to rethink the graduation method applied in the construction of age specific mortality rates.60 The mortality tables of insured lives posed different methodological problems. Here, there was no uncertainty about age, since only “selected lives” were included in the survey61 and policy holders had to provide a birth certificate before signing the insurance policy.

  • 62 Magyar biztosítottak halandósága, 1910, VII-IX.
  • 63 Magyar biztosítottak halandósága, 1910, VIII.
  • 64 Magyar biztosítottak halandósága, 1910, XIV.

31The method of counting insurance policies represented a notable methodological challenge as there was no theoretical rule about whether separate cases should be counted per person or per medical selection, i.e., each time a policy holder passed a medical examination.62 The Hungarian project complied with the instructions of the Austrian project and both methods were applied so that the results could be compared. According to the editors, this comparison was reassuring: “[…] the variance between the mortality probabilities obtained with ‘person counting’ and ‘selection counting’ was so minor in the Hungarian case, that it had no practical significance at all.”63 Another challenge of person counting was the identification of policy holders having multiple policies over time: 43.2 percent of (male) insurance policies belonged to policy holders with multiple policies64, so the Magyarization of names became a source of practical difficulty. This could only be remedied in a mechanical way by manually checking policyholders who had the same birth date.

  • 65 The Mortality Experience of Life Assurance Companies, 1869, p. 2.
  • 66 The Mortality Experience of Life Assurance Companies, 1869, p. 18.

32The Hungarian project relied heavily on methods used by previous mortality tables and was carried out in close collaboration with the Austrian insurance companies’ mortality project. The English Healthy Males Table (1869) introduced practices that were subsequently applied by both Habsburg projects: the card system was introduced to facilitate the handling of data without “frequent risk of error”;65 theoretical assumptions were made about the precise age of the insured persons at the beginning of the contract to deal with year-fractions in calculations; and seasonal fluctuations in the number of contracts over the calendar year were to be disregarded.66 The latter two theoretical assumptions belonged to the type that demographers had previously rejected as false premises, like that of “stationary populations” and the idea that negative and positive errors would cancel each other out, as in the case of migration in overall statistics. Yet, actuaries were happy to use such assumptions as a means to simplify calculations.

  • 67 S. Bogyó, 1905.

33Actuaries had to apply incorrect theoretical assumptions, for example, to operationalize the length of insurance policies. While the unit of measurement was given in whole years, people took out and cancelled insurance policies and died at different times in the year. The practical solution put forward by Samu Bogyó (1881-1928), professor at the Pester Handels-Akademie and actuary of several insurance companies, was to assume that these events were evenly distributed,67 a theoretical assumption that had been severely criticized by social statisticians a few decades earlier. Gyula Altenburger was more vocal about the preference for practical solutions over theoretical objections.

  • 68 Magyar biztosítottak halandósága, 1910, p. XLVII.

“The theoretical framework of the investigation first and foremost has to identify those circumstances that are important from the perspective of the business side of insurance practice and has to make a compromise with the existing practice if theory cannot provide better, simpler, and in that way more practical solutions. Taking into account these practical necessities is the reason why we applied consciously such hypotheses that were in the past criticized and successfully attacked on theoretical grounds.”68

34In the actual calculations, false hypotheses of this kind were made concerning the separation of age cohorts, the duration of the observed period, and the unit of counting.

  • 69 E. Blaschke, 1890.
  • 70 Deutsche Sterblichkeits-tafeln aus den Erfahrungen von dreiundzwanzig Lebensversicherungs-Gesellsch (...)

35Ernst Blaschke played a crucial role in disseminating actuarial methods within the Habsburg Monarchy, and in introducing German and English actuarial practices. From 1882 to 1896, Blaschke worked as an actuary at the Erster Allgemeiner Beamtenverein and he established the basis of his actuarial theory during these years. When revising the mortality tables of the Beamtenverein pension fund, Blaschke implemented the core of the method that was later used in the mortality tables of Austrian and Hungarian insurance companies.69 The problem with the Beamtenverein mortality tables was that, based on the modified versions of English and German tables, they underestimated the mortality of younger members of the pension fund and overcalculated the mortality of older members. While this was disadvantageous for policyholders, it had a positive effect on the financial balance of the fund: the lower than expected interest rates could be offset by the gains on mortality over the long term. In these mortality tables produced in 1890, Blaschke opted for the card system and copied the card templates used for the German mortality tables,70 that, in turn, copied the 1869 English ones. The same templates were used for the Austrian and Hungarian mortality tables of insured lives a decade later.

  • 71 Absterbe-Ordnungen…, vol. I, 1907, pp. 6-7.
  • 72 E. Blaschke, 1888.
  • 73 E. Blaschke, 1890.

36Another practice that reveals Blaschke’s influence was the counting method. Theoretically, the counting unit could be the insurance policy, the insured sum, the medical examination, or the insured person.71 Blaschke preferred to use the medical examination as the counting unit from the 1880s.72 This choice was implemented for the Beamtenverein mortality tables, and later in the case of the Austrian life tables. Blaschke’s reasoning went as follows. The probability of mortality (w) is the function of age (x), year of birth (y), and the observed duration (z) [w = f (x, y, z)]; accordingly, only those empirical observations that have an equal value for x, y, and z will have the same probability (w). This can only be guaranteed if the medical examination is taken as the counting unit.73

  • 74 Magyar biztosítottak halandósága, 1910, pp. XLIX-LII.

37Even though the actuary of the Hungarian project, Gyula Altenburger, did not agree with Blaschke’s method, the Hungarian project nonetheless applied “medical counting” in the processing of data, which shows the strength of the link between the two Habsburg projects. The counting method should equally be conceptualized, Altenburger explained, as a method for weighting individual observations. Weighting of observations was necessary because the practical goal of mortality tables was to determine the expected business cost of each age cohort in the upcoming years. If person counting was used, it became unnecessary to divide age cohorts into sub-groups according to the value of policies, because the same mortality would be applied to all sub-groups. The variation of sub-groups was merely caused, in this case, by observation error.74 The Hungarian project pursued the processing of data using both person and medical counting so as to compare the results and settle the counting method issue once and for all. Surprisingly, the results of the two methods did not differ significantly, and Altenburger finally complied with the more practical solution. Person counting was no longer used as it involved a huge amount of additional work without producing the expected benefits.

4. The epistemology of life insurance

  • 75 A Magyar Filozófiai Társaság Közleményei, no. 10, 1904, p. 22.
  • 76 B. Kenéz, 1903, p. 71.

38From a statistical perspective, death is an obvious phenomenon. Yet, connecting the individual and the collectivity raises significant conceptual problems. The previous section described the difficulties involved in constructing mortality tables as a scientific object, which brought the individual into the purview of the collectivity. At the same time, actuaries and statisticians also needed to reflect on the nature of the predictions that mortality tables can make about individuals. The controversy was often framed in terms of a contradiction between statistical laws and free will. József Kőrösy, for example, thought that it was impossible to accept the constraint of statistical laws and free will at the same time. If statistical laws work with the same inevitability as the laws of the natural sciences, there can be no place for free will in human action. If the suicide rate is three per thousand in a given population and 997 people decide not to commit suicide, this leaves no choice for the remaining three persons. If we presume that the doctrine of free will is not challenged, he concluded, then statistical laws cannot act with absolute constraint on the individual.75 Béla Kenéz also tried to tackle the apparent contradiction between the regularity of “moral statistics” and the doctrine of free will. Given that similar causes result in similar effects, statistical numbers can achieve regularity over time. The law of large numbers, in this sense, is just a confirmation that on a large scale one can continuously experience “the overall effects of the forces that act on the collectivity”.76 Kenéz escaped the contradiction by stating that both individual choice and external (social) causes are essential components of human action.

  • 77 Absterbe-Ordnungen…, 1907, p. 18.
  • 78 Magyar biztosítottak halandósága, 1910, XLIX-LI.

39Insurance mathematicians, on the other hand, did not have to tackle the problem of free will; emphasis was placed rather on the nature of their predictions about the collectivity and the individual policy holder. For them, human life was a commodified object having a fluctuating value in the eyes of the company that depended on the worth and type of insurance policy and the insurance reserves accumulated. The practical goal of mortality tables was to enhance business stability. Thus, instead of taking individuals as the basis of calculation, actuaries often took the value of insurance policies to weight data and to graduate mortality tables. This method makes good sense, because insurance companies were interested in monetary risks rather than in the expected and actual mortality of the future. Still, Altenburger, did not go as far as American actuaries77 in describing the mortality rate of “K 1,000” and predicting the number of insured Österreichisch-Ungarische Kronen expected to remain “living” each year on average up to the age of 100. Altenburger considered this to be an impractical solution as the essential question was about the worth of insurance policies and the risk not covered by insurance reserves at the time of the policy holder’s death. Consequently, it was impossible to calculate collective risk based on the specificities – type of insurance and duration of the contract – of each policy. Altenburger found the solution, as described earlier, in the person counting method.78

  • 79 G. Altenburger, 1942, p. 1.
  • 80 G. Altenburger, 1942, p. 6.
  • 81 G. Altenburger, 1905; id., 1907a.

40The relation between collective observation and individual probabilities, as well as the status of long-term predictions based on mortality tables, remained an enigma to be explained by insurance mathematicians. According to Altenburger, the “unconscious question” represented a philosophical problem: “Why should we believe that we can predict the future based on the past?”79 If the same cause, he replied, resulted in the same effect, then statistics could establish “the limits within which, most probably and without absolute certainty, truth can be found.”80 Statistics cannot reveal reality because actual conditions are abundant and unknowable; in other words, the insurance mathematician can only study probabilities but never certainties. It also follows that no law of mortality can be formulated analytically, and that pursuing such a law must not be the purpose of insurance mathematics. Consequences were crucial to the practice of life insurance. The method of least squares could be used to eliminate measurement errors in astronomy, because Gauss knew the law he wanted to test and measure. However, as insurance mathematicians can never know the law of mortality, the observed mortality errors (i.e., when the normal order of mortality is modified by chance) cannot be easily corrected. Actuaries were forced to use a graduation method devoid of theoretical assumptions in order to construct flat mortality tables. It was in this regard that Altenburger produced his main actuarial achievement: the invention of a mechanical graduation method that did not set any a priori assumption about the order of mortality.81

  • 82 G. Altenburger, 1898, p. 156; M. Havas, 1918, p. 145.
  • 83 Jogtudományi Közlöny, vol. XXX, no. 22, 1895, pp. 169-75.
  • 84 G. Altenburger, 1899.

41The practice of life insurance drew further attention to the relation between the collectivity of policy holders and the individual client. The very principle of insurance placed the collectivity at the forefront: the basic idea was that the losses of the individual were distributed among and compensated by the collectivity.82 In the event of policy cancellation, however, insurance companies had to determine how much of the actuarial reserves individual policy holders were entitled to receive. Ferenc Nagy (1852-1928), state secretary and author of the non-ratified insurance legislation in 1895, claimed that the actuarial reserves in life insurance belonged to policy holders and not to the company, so in the event of policy cancellation, individuals were entitled to receive their share. Nagy conceptualized life insurance as a savings account plus a risk payment. The insured sum was thus composed of the increasing amount on the savings account and the rest covered by the risk payments, the latter decreasing at the same time.83 For Altenburger, the primacy of the individual over the collectivity was unacceptable and he proposed a fundamentally collectivist epistemology. Mortality tables were applicable to the death probabilities of the collectivity and individual risk could not be determined. The actuary was not able to withdraw individuals from the collectivity and determine their share of the actuarial reserves. To do so, the actuary would have to know the future: the business details of the company (the premiums paid by clients, future interest rates, business expenditures) and the future mortality of policy holders.84

  • 85 G. Altenburger, 1898, pp. 153-154.
  • 86 G. Altenburger, 1900.
  • 87 G. Altenburger, 1907b.

42In addition, actuarial reserves were calculated based on the law of large numbers and were relevant only for the whole group of policy holders. The reserves were assessed on the assumption that actuaries were dealing with a homogenous group, but in the case of policy cancellations, the multitude of policy holders became very much a heterogenous group: it included people in excellent health but also some who had a terminal illness.85 The consequences were crucial for insurance legislation as well. Actuarial reserves were an undivided and indivisible entity, and clients were not automatically entitled to a refund; they could only be made at the company’s discretion. Mathematical probability provided the remedy for this impasse and also changed Altenburger’s stance on the question: later, he was able to make a conservative estimate about the individual’s share in the reserves, thus reinstating their right in the event of cancellation.86 Yet, he still held mathematical probability in low esteem for, very often, “real life” disproved its results. It could only anticipate the likelihood of events, and the “expected error” of predictions could always be overwritten by real life.87 A practical consequence was that, in contrast to the views of such statisticians as Lexis and Bortkiewicz, the actuary could not know anything about the real risk of an insurance company.

5. Concluding remarks

  • 88 I. Hacking, 1990, pp. 1-11.
  • 89 A. C. Janos, 1982; V. Karády, 2000; G. Gyáni, G. Kövér & T. Valuch, 2004.

43Both social hygienists and actuaries profited from and participated in the “taming of chance” that took place in the nineteenth century88 in the sense that the possibility of statistical laws made disorder and chance epiphenomenal from the perspective of their agenda. However, they took different approaches to the law of large numbers and the conception of normalcy that followed. For Kőrösy, the statistician and social hygienist, the focus was on identifying the pathological in order to change the existing social laws by promoting social care. Kőrösy’s struggles showed how a Jewish intellectual had to overcome the consequences of a lower social status to gain intellectual credibility for his statistical work. This type of confrontation was an essential constituent of Hungarian modernization in the second half of the nineteenth century, in which the Jewish minority in Hungary played a crucial role and participated significantly in the country’s economic and cultural modernization, very much to the chagrin of the gentry.89 The other element was the relative backwardness of Hungarian society. Alongside Kőrösy, other demographers also looked at mortality statistics from the perspective of Hungary’s belated development. They set the goal of catching-up with the “civilized countries” and searched for causes and potential remedies.

44In contrast, actuaries were neither interested in the pathological, nor in what constitutes normalcy. They cared about the convergence of expected and actual mortality in the long run to ensure the viability of their business. Medical selection and the identification of healthy males were crucial to the goal of excluding high risk. However, having signed the insurance policy, insurance companies mostly directed their attention to disease in cases of insurance fraud. This focus changed slightly at the turn of the century when they became interested in insuring inferior or high-risk lives and realized that early signs of disease could predict divergence from the overall mortality rate. Despite these differences, proponents of both groups became agents of modernization and thus contributed to the crystallization of a particular mindset that included a tendency toward rationalization, a strong belief in the calculability of all social phenomena, and a Weberian disenchantment. This emerging belief eventually facilitated different kinds of human organization while paving the way for the “upsurge” of the industrial revolution and the emergence of modern economic growth as theorized by Kuznets, with population growth being a prerequisite for both. The latter, to duly close the circle, was made possible by statistics and the advances of vaccination and other measures that reduced mortality from infectious diseases.

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Bibliographie

Primary sources

Press (daily or other)

Biztosítási és Közgazdasági Lapok

Borsszem Jankó

Budapest Főváros Statistikai Hivatalának Közleményei

Budapesti Szemle

Jogtudományi Közlöny

Közgazdasági Szemle (previously Nemzetgazdasági Szemle)

Közegészségügy és Törvényszéki Orvostan

Magyar Nemzet

Magyar Statistikai Évkönyv

Magyar Statisztikai Évkönyv, Új folyam

Mitteilungen des Verbandes der österr. und ungar. Versicherungs-Techniker

Österreichische Revue. Organ für Assecuranz und Volkswirtschaft

Oesterreichische Versicherungs-Zeitung

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A Magyar Kir. Központi Statisztika Hivatal munkássága (1871-1911), Budapest, Pesti Könyvnyomda, 1911.

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Magyar biztosítottak halandósága, Budapest, Magyar Halandósági Táblákat Szerkesztő Központi Hivatal, 1910.

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Altenburger, Gyula, “Állami ellenőrzés az életbiztosítás terén”, Közgazdasági Szemle, 1898, vol. 22, pp. 148-164.

Altenburger, Gyula, “A biztosítási díjtartalékról », Magyar Biztosítási Évkönyv, 1899, vol. 2, pp. 74-82.

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Altenburger, Gyula, “Beiträge zum Problem der Ausgleichung von Sterblichkeitstafeln”, Versicherungswissenschaftliche Mitteilungen, 1905, vol. 1, no. 4, pp. 211-282.

Altenburger, Gyula, “Versuch einer allgemeiner Theorie der mechanischen Ausgleichungs-Methoden”, Mitteilungen des Österreichisch-ungarischen Verbandes der Privat-Versicherungs-Anstalten, 1907a, vol. 3, no. 1, pp. 45-83.

Altenburger, Gyula, “Mozgalom az életbiztosítás tudományának terén”, Magyar Biztosítási Évkönyv, 1907b, vol. 9, pp. 227-254.

Altenburger, Gyula, A biztosítás matematikájának ismeretelméleti jelentősége, Budapest, [Pallas Ny.], 1942.

Blaschke, Ernst, “Ueber die Behandlung der dasselbe Leben betreffenden mehrfachen Versicherungen bei Constatirung der Sterbenswahrscheinlichkeiten ausgelesener Leben”, Österreichische Revue. Organ für Assecuranz und Volkswirtschaft, 1888, vol. 8, no. 21, pp. 81-82.

Blaschke, Ernst, “Megvizsgált férfiéletre vonatkozó halandósági táblázat szerkesztése az osztrák-magyar első általános tisztviselő-egylet huszonnégy évi tapasztalata nyomán”, in Az Osztrák-Magyar Első Általános Tisztviselő-Egylet: alapításának története, fejlődése és működése fennállásának első 25 évében 1865-1890, Budapest, Buschmann Ny., 1890, pp. 483-512.

Bogyó, Samu, “A halandósági táblák készítése”, Biztosítási és Közgazdasági Lapok, 1905, vol. 11, nos. 1, 2, 3, pp. 1-2, 3, 4.

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Bud, János, “Népünk halandósága és élettartama”, Közgazdasági Szemle, 1907, vol. 31, no. 37, pp. 229-239, 320-342.

Fényes, Elek, Magyarország hátramaradása ügyében felelet Dr. Wildner Ignácz urnak, Lipcsében, Ottónál, 1844.

Fényes, Elek, Szózat a magyar biztosító társulat érdekében, Bécs, Sommer Ny., 1859.

Fényes, Elek, A magyar elem s ellenesei, Pest, Wodianer Ny., 1860.

Földes, Béla, “Újabb adatok hazánk halandósági és közegészségi viszonyairól”, Budapesti Szemle, 1884, vol. 40, no. 95, pp. 257-290.

Goldziher, Károly, “Magyar biztosítottak halandósága”, Budapesti Szemle, 1912, vol. 149, pp. 465-472.

Havas, Miksa, “Fokozott koczkázat az életbiztosításban”, Közgazdasági Szemle, 1918, vol. 42, no. 59, pp. 145-171.

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Notes

1 Z. Ráth, 1893, pp. 729-731.

2 A. Desrosières, 1998; D. A. MacKenzie, 1981; J.-G. Prévost & J.-P. Beaud, 2015; T. M. Porter, 1986; id., 1995; S. M. Stigler, 1986; id., 1999.

3 T. M. Porter, 1995.

4 F. K. Ringer, 2000, p. 4.

5 O. Rey, 2016.

6 Magyar Statistikai Évkönyv.

7 Budapest Főváros Statistikai Hivatalának Közleményei.

8 J. Kőrösy, 1878, p. 122.

9 J. Kőrösy, 1878, p. 128.

10 B. Földes, 1884, p. 257.

11 B. Kenéz, 1906.

12 J. Bud, 1907, p. 234.

13 J. Kőrösy, 1891a, p. 134.

14 J. Kőrösy, 1878, p. 119.

15 J. Kőrösy, 1876, pp. 3-4.

16 Közegészségügy és törvényszéki orvostan - Melléklet az Orvosi Hetilap 41-dik számához, no. 5, 1877, pp. 82-84.

17 J. Kőrösy, 1878, p. 133.

18 Borsszem Jankó, vol. IX, no. 48, 1878, p. 5.

19 G. Körmendi, 1989; M. Szalay, 2006; G. Vargha, 1903.

20 J. Jekelfalussy, 1892, p. 60.

21 J. Kőrösy, 1891a; J. Jekelfalussy, 1891; J. Kőrösy, 1891b; J. Jekelfalussy, 1892.

22 J. Jekelfalussy, 1891, p. 947.

23 J. Jekelfalussy, 1891, p. 948.

24 J. Kőrösy, 1874, p. 88.

25 T. Szél, 1927, pp. 127-131.

26 E. Bratassevič, 1891, p. 83.

27 Borsszem Jankó, vol. V, no. 241, 1872, p. 98.

28 Z. Ráth, 1893, p. 98.

29 Z. Ráth, 1893, pp. 90-99.

30 Z. Ráth, 1893, pp. 81-82.

31 The question on national identity changed between 1890 and 1900 from a mere “What is your mother tongue?” to an interpretive “What is your mother tongue, that is the language that you consider yours, that you prefer to use and that you speak the best?” (A Magyar Kir. Központi Statisztikai Hivatal munkássága (ed.), 1911, pp. 457, 461).

32 V. Ormody, 1908.

33 The Hungarian Statistical Yearbook published data on life insurance only from 1895 and plainly differentiated between “domestic” (hazai) and “foreign” companies (Magyar Statisztikai Évkönyv (Új folyam), vol. III, 1895, pp. 340-341).

34 Magyar Biztosítási Évkönyv.

35 E. Fényes, 1859.

36 Contrary to assimilation trends at the turn of the century, Fényes remarked upon the Jewish community’s lack of acculturation and assimilation: “the Jews, even more than the German population, hold on to the German language, to the extent that they remain German even in purely Hungarian villages” (E. Fényes, 1859, p. 47).

37 E. Fényes, 1844; id., 1860.

38 E. Fényes, 1859, p. 25.

39 V. Weninger, 1862, p. 254.

40 V. Weninger, 1875, p. 387.

41 V. Weninger, 1864.

42 L. Kőváry, 1884, p. 105.

43 V. Weninger, 1861-1862, pp. 54-59.

44 Magyar Biztosítási Kurír, 17.10.1913.

45 See the volumes of the Hungarian Insurance Yearbook (Magyar Biztosítási Évkönyv) that enumerated the specific mortality tables used by each company year by year. The idea of compiling the Seventeen Offices Table was initiated by the Statistical Society of London in 1838 and revived by a proposal of Benjamin Gompertz in the same year. They were produced and published five years later in order to guarantee the authenticity of life insurance premiums (T. L. Alborn, 2009, pp. 110-113). Hungarian experts complained that most European insurance companies still used the Seventeen Offices Table, though it was seriously out of date and mortality rates had decreased significantly between the 1840s and the turn of the century (Magyar Biztosítási Évkönyv, vol. VI, 1904, p. 14).

46 Biztosítási és Közgazdasági Lapok, 1904, no. 1, pp. 6-7; no. 2, p. 5.

47 T. L. Alborn, 2009, pp. 109-110; W. I. Hughes, 1889, p. 21.

48 T. M. Porter, 1986, p. 6.

49 G. Altenburger, 1907b.

50 T. L. Alborn, 2009, pp. 296-312.

51 Mitteilungen des Verbandes der österr. und ungar. Versicherungs-Techniker, Heft 2, 1900, pp. 1-2.

52 Magyar Nemzet, vol. XIX, no. 197, 1900, p. 6.

53 Absterbe-Ordnungen…, vol. I, 1907, pp. 14-15; Magyar biztosítottak halandósága, 1910, pp. XIII-XIV.

54 Magyar biztosítottak halandósága, 1910, p. V.

55 J. Raffmann, B. Kenéz & G. Vargha, 1906, p. 18*.

56 Magyar biztosítottak halandósága, 1910, p. XIV.

57 K. Goldziher, 1912, p. 465.

58 K. Goldziher, 1912, p. 471.

59 J. Raffmann, B. Kenéz & G. Vargha, 1906, p. 2*.

60 J. Raffmann, B. Kenéz & G. Vargha, 1906, pp. 23*-43*.

61 Magyar biztosítottak halandósága, 1910, p. LVI. The Austrian project on the mortality tables of insured lives compiled tables for policyholders with and without a medical examination.

62 Magyar biztosítottak halandósága, 1910, VII-IX.

63 Magyar biztosítottak halandósága, 1910, VIII.

64 Magyar biztosítottak halandósága, 1910, XIV.

65 The Mortality Experience of Life Assurance Companies, 1869, p. 2.

66 The Mortality Experience of Life Assurance Companies, 1869, p. 18.

67 S. Bogyó, 1905.

68 Magyar biztosítottak halandósága, 1910, p. XLVII.

69 E. Blaschke, 1890.

70 Deutsche Sterblichkeits-tafeln aus den Erfahrungen von dreiundzwanzig Lebensversicherungs-Gesellschaften, 1883, p. LXIII.

71 Absterbe-Ordnungen…, vol. I, 1907, pp. 6-7.

72 E. Blaschke, 1888.

73 E. Blaschke, 1890.

74 Magyar biztosítottak halandósága, 1910, pp. XLIX-LII.

75 A Magyar Filozófiai Társaság Közleményei, no. 10, 1904, p. 22.

76 B. Kenéz, 1903, p. 71.

77 Absterbe-Ordnungen…, 1907, p. 18.

78 Magyar biztosítottak halandósága, 1910, XLIX-LI.

79 G. Altenburger, 1942, p. 1.

80 G. Altenburger, 1942, p. 6.

81 G. Altenburger, 1905; id., 1907a.

82 G. Altenburger, 1898, p. 156; M. Havas, 1918, p. 145.

83 Jogtudományi Közlöny, vol. XXX, no. 22, 1895, pp. 169-75.

84 G. Altenburger, 1899.

85 G. Altenburger, 1898, pp. 153-154.

86 G. Altenburger, 1900.

87 G. Altenburger, 1907b.

88 I. Hacking, 1990, pp. 1-11.

89 A. C. Janos, 1982; V. Karády, 2000; G. Gyáni, G. Kövér & T. Valuch, 2004.

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Mátyás Erdélyi, « Quantifying Mortality in Hungary: Actuaries and Statisticians (1860s-1910s) »Histoire & mesure, XXXIII-2 | 2018, 115-138.

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Mátyás Erdélyi, « Quantifying Mortality in Hungary: Actuaries and Statisticians (1860s-1910s) »Histoire & mesure [En ligne], XXXIII-2 | 2018, mis en ligne le 02 janvier 2022, consulté le 14 mai 2025. URL : http://journals.openedition.org/histoiremesure/8062 ; DOI : https://doi.org/10.4000/histoiremesure.8062

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Mátyás Erdélyi

PhD candidate in Comparative History, Central European University, Budapest. E-mail: erdelyi_matyas@phd.ceu.edu

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