European fascist regimes have attached great importance to nationalistic families and designed policies to perpetuate them. Most offered policy packages with interest-free loans repayable through childbirth, along with allowances and tax deductions for large families. Using a difference-in-difference approach and Nazi Germany as a case study, we show that these policies may have counterproductive effects due to negative selection mechanisms in the marriage market. The excessive pressure to marry exerted on singles results in lower quality, ultimately less fertile, and more fragile unions. This finding is important as the main European far-right parties today propose reinstating these policy packages.

This note is a summary description of the set of scholars and literati who taught at the University of Ingolstadt from its inception in 1459 to its relocation in 1800.

Artificial Intelligence (AI) is rapidly transforming all scientific disciplines. Among its many applications, AI can facilitate data retrieval from a wide range of sources. We evaluate the performance of large language models in extracting data from local heritage books-valuable sources for economic and demographic history. We compare the results of AI-driven, Python code-based, and manual data retrieval for random samples of observations from three heritage books. Our analysis shows that Python code-based retrieval consistently outperforms AI, particularly in minimizing issues such as omitted or hallucinated data. Furthermore, we show that, with minor modifications our Python code-based methods can be adapted to other local heritage books, highlighting the robustness and scalability of this traditional approach.

Background. Family reconstitution and data from online genealogies, such as FamiLinx, are two potential sources for investigating mortality dynamics for the period before official lifetables became available. In this paper, we use two of them, the family reconstitution of Imhof and the FamiLinx dataset based on geni.com, to estimate dynamics in life expectancy and discuss the sex-specific differential mortality in the German Empire. Method. Sex-specific lifetables are estimated for the territory of the German Empire from the individual data of the family reconstitution and the online genealogies. On the basis of these lifetables, we estimate the conditional life expectancy and derive the corresponding sex-specific differential mortality. Findings are compared with the official lifetable of the German Empire in 1871-1910. The contribution of each age group to the differential mortality is determined using the stepwise-replacement algorithm. Results. The family reconstitution overestimates conditional life expectancy less than FamiLinx after 1871, when official lifetables are available in the German Empire. However, both sources fail to capture the sex-specific mortality differentials of the official lifetables at the end of the nineteenth century and show a higher life expectancy for males instead of females. The bias in sex-specific mortality rates is particularly pronounced in the age groups 15 to 45. Discussion. Finally, we discuss possible explanations for the biased findings. Notability bias, the patriarchal approach to family trees, and maternal mortality are important mechanisms in the FamiLinx dataset. Censoring due to mobility serves as a potential reason for the bias in the family reconstitution.

Familienrekonstitutionen und Familienstammbäume genealogischer Online-Plattformen sind 2 mögliche Datenquellen für die Untersuchung der Sterblichkeit in einer Zeit, als noch keine amtlichen Sterbetafeln verfügbar waren. Der vorliegende Artikel diskutiert anhand zweier Beispiele, der Familienrekonstitution aus Imhof und dem auf geni.com beruhenden Datensatz FamiLinx, die geschätzten Verläufe der Lebenserwartung im Deutschen Reich mit einem Fokus auf die geschlechtsspezifische differenzielle Mortalität. Mithilfe der Individualdaten aus der Familienrekonstitution und aus den Online-Genealogien werden die geschlechtsspezifischen Sterbetafeln geschätzt. Aus ihnen wird die bedingte Lebenserwartung ermittelt und die entsprechende geschlechtsspezifische differenzielle Mortalität abgeleitet und mit den amtlichen Sterbetafeln für die Jahre 1871–1910 abgeglichen. Der Beitrag der einzelnen Altersklassen zur differenziellen Sterblichkeit wird mit dem Stepwise Replacement Algorithm bestimmt. Die Ergebnisse der Familienrekonstitution überschätzen die Lebenserwartung nach 1871 weniger stark als die FamiLinx-Schätzungen. Die geringere Sterblichkeit der Frauen in der amtlichen Statistik wird von beiden Quellen nicht abgebildet. Im Gegensatz zur amtlichen Statistik ist die geschätzte Lebenserwartung der Männer höher als die der Frauen. Diese verzerrte geschlechtsspezifische Abbildung der Mortalitätsraten geht insbesondere auf die Altersklassen von 15 bis 45 Jahren zurück. Der Notability Bias, der patriarchische Ansatz in der Erstellung von Familienstammbäumen und die Müttersterblichkeit sind mögliche Ursachen für diese Beobachtungen in FamiLinx. In der Familienrekonstitution ist die mit der Mobilität einhergehende Zensierung ein Erklärungsansatz.

This note is a summary description of the set of scholars and literati who taught at the University of Prague from its inception in 1348 to the eve of the Industrial Revolution (1800).

Throughout our project on premodern academia, we use a heuristic human capital index to measure each scholar’s quality. This index is built by combining several statistics from individual Wikipedia and Worldcat pages. The question we address here is whether this measure is correlated with the actual wages professors received. This note is a technical appendix to our paper on the academic market (De la Croix et al. 2020) but also has an interest as a stand-alone publication. There is considerable evidence that compensations for academic contractswentwell beyond paid salaries.1 They included payments from students, prebends,2 and many forms of in-kind benefits. Yet, it is interesting to examine the relationship between scholars’ human capital and existing data on monetary remunerations. Such remunerations have been used by Dittmar (2019) to show that professor salaries increased significantly relative to skilled wages after printing spread, with science professors benefiting from the largest salary increases. In the two sections below, we first review the available data on salaries, and argue that such data are imperfect proxies for the overall remuneration for academic services (i.e. a scholar’s market value). Keeping in mind such limitations, we thendocument a positive correlation between monetary income and scholars’ human capital.

This note is a summary description of the set of scholars and literati who taught at the University of Freiburg from its inception in 1457 to the eve of the Industrial Revolution (1800).

This note is a summary description of the set of scholars and literati who taught at the University of Leipzig from its inception in 1409 to the eve of the Industrial Revolution (1800).

We argue that market forces shaped the geographic distribution of upper-tail human capital across Europe during the Middle Ages, and contributed to bolstering universities at the dawn of the Humanistic and Scientific Revolutions. We build a unique database of thousands of scholars from university sources covering all of Europe, construct an index of their ability, and map the academic market in the medieval and early modern periods. We show that scholars tended to concentrate in the best universities (agglomeration), that better scholars were more sensitive to the quality of the university (positive sorting) and migrated over greater distances (positive selection). Agglomeration, selection, and sorting patterns testify to an integrated academic market, made possible by the use of a common language (Latin).

We build a unified model of growth and internal migration and identify its deep parameters using an original set of Swedish data. Our structural estimation and counterfactual experiments suggest that conditions of migration between the countryside and cities have strongly shaped the timing and the intensity of the transition to growth. Mobility cost had to be low enough to enable population movement. Furthermore, initial productivity in rural industries had to be moderate to sustain the first phase of industrialization appearing in the countryside without delaying too much the second phase of industrialization taking place in cities. More than the initial productivity of rural industries or migration costs alone, what truly mattered for the transition to modern economic growth was the interplay between these two elements. By contrast, we evidence a poor role for mortality decline in the whole process. Finally, we discuss why our conclusions on Sweden are exemplary for the rest of Western Europe.

Crowdsourced online genealogies have an unprecedented potential to shed light on long-run population dynamics, if analyzed properly. We investigate whether the historical mortality dynamics of males in familinx, a popular genealogical dataset, are representative of the general population, or whether they are closer to those of an elite subpopulation in two territories. The first territory is the German Empire, with a low level of genealogical coverage relative to the total population size, while the second territory is The Netherlands, with a higher level of genealogical coverage relative to the population. We find that, for the period around the turn of the 20th century (for which benchmark national life tables are available), mortality is consistently lower and more homogeneous in familinx than in the general population. For that time period, the mortality levels in familinx resemble those of elites in the German Empire, while they are closer to those in national life tables in The Netherlands. For the period before the 19th century, the mortality levels in familinx mirror those of the elites in both territories. We identify the low coverage of the total population and the oversampling of elites in online genealogies as potential explanations for these findings. Emerging digital data may revolutionize our knowledge of historical demographic dynamics, but only if we understand their potential uses and limitations.

This note is a summary description of the set of scholars and literati who taught at the University of Heidelberg from its inception in 1386 to the eve of the Industrial Revolution (1800).

This note is a summary description of the set of scholars and literati who taught at the University of Gießen from its inception in 1607 to the eve of the Industrial Revolution (1800).

When did mortality first start to decline, and among whom? We build a large, new data set with more than 30,000 scholars covering the sixteenth to the early twentieth century to analyze the timing of the mortality decline and the heterogeneity in life expectancy gains among scholars in the Holy Roman Empire. The large sample size, well-defined entry into the risk group, and heterogeneity in social status are among the key advantages of the new database. After recovering from a severe mortality crisis in the seventeenth century, life expectancy among scholars started to increase as early as in the eighteenth century, well before the Industrial Revolution. Our finding that members of scientific academies-an elite group among scholars-were the first to experience mortality improvements suggests that 300 years ago, individuals with higher social status already enjoyed lower mortality. We also show, however, that the onset of mortality improvements among scholars in medicine was delayed, possibly because these scholars were exposed to pathogens and did not have germ theory knowledge that might have protected them. The disadvantage among medical professionals decreased toward the end of the nineteenth century. Our results provide a new perspective on the historical timing of mortality improvements, and the database accompanying our study facilitates replication and extensions.

This note is a summary description of the set of scholars and literati who taught at the University of Lund from its inception in 1666 to the eve of the Industrial Revolution (1800).

This note is a summary description of the set of scholars and literati who taught at the University of Jena from its inception in 1558 to the eve of the Industrial Revolution (1800).

This paper investigates the problem of an “optimum population” concerning age structures in a 3-period OLG-model with endogenous fertility and longevity. The first-best solution for a number-dampened total social welfare function, including Millian and Benthamite utilitarianism as two extreme cases, identifies the optimal age structure, which generally fails in laissez-faire economies. As individuals do not internalize the effect of longevity on life-cycle income, they over-invest in health. Additionally, they choose a non-optimal number of offspring. A calibration exercise for 80 countries emphasizes that the over-aging of populations crucially depends on social preferences and on observed age structures. Interestingly, it appears that, unlike taxes on health expenditures, taxes or subsidies on children to decentralize the first-best solution are sensitive to social preferences. Still, with the introduction of sufficiently large positive externalities of health expenditures or of individuals who do not fully internalize the effect of health efforts on longevity, taxes might become subsidies on health efforts to avoid an under-investment in longevity.

This chapter deals with the demographic and economic development in Germany and its federal states Hamburg and Mecklenburg-Western Pomerania between 2005 and 2030. It contains a common demographic-change framework and projection as input for all infrastructure models used or constructed within the InfraDem project. Germany is likely to experience ageing and shrinking of the population, but with large regional differences. For example, demographic change is expected to be exceptionally weak in Hamburg and particularly strong in Mecklenburg-Western Pomerania. The labour force is shrinking more strongly than the total population all over; it could even shrink in Hamburg, where the total population is still growing. The number of households is projected to increase and their size to decrease. Ageing is occurring within the smaller households; larger households (three and more members) stay young or are becoming even younger. The gross domestic product is expected to grow both in total and per capita, but at diminishing rates, and in regions with strong demographic change to a lesser extent.