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ABSTRACTIn this article, we study variations in co-residence with kin in the Netherlands in the late nineteenth and early twentieth centuries. We use the reconstructed life courses of 17,527 individuals derived from the Historical Sample of the Netherlands (HSN) database. The life-course approach allows us to look at co-residence from the perspectives of both the receiving households and the co-resident kin. What made households take in relatives and do we find a preference for one type of relative over another? What was the background of people who decided to co-reside in another household? How important were family-related 'altruistic' motives compared with economic ones? The outcomes suggest the predominance of altruistic motives for co-residence, apart from persistent inheritance customs in the eastern part of the country.

Historical censuses are one of the most challenging datasets to compare over time. While many (successful) efforts have been made by researchers to harmonize these types of data, a lack of a generic workflow thwarts other researchers in their endeavors to do the same. In order to use historical census data for longitudinal analysis, a common process currently often loosely referred to as harmonization is inevitable. This process becomes even more challenging when dealing with aggregate data. Current approaches, whether focusing on micro or aggregate data, mainly provide specific, goal-oriented solutions to solve this problem. The nature of our data calls for an approach which allows different interpretations and preserves the link to the underlying sources at all times. To realize this we need a flexible, bottom-up harmonization process which allows us to iteratively discover the peculiarities of these types of data and provide different interpretations on the same data in an accountable way. In this article, we propose an approach which we refer to as source-oriented harmonization. We use the Resource Description Framework from (RDF) as the technological backbone of our efforts and aim to make the process of harmonization more graspable for others to stimulate similar efforts.

The question whether socioeconomic status gradients in adult mortality have changed over a broad historical period has become an important political and theoretical issue but is hard to test. In this article we study long-term trends in social inequality in adult mortality by using data for 2 (of the 11) provinces of the Netherlands for the period 1812–1922. We apply indirect estimation techniques, which have been developed for the analysis of mortality patterns in countries with deficient data. Our article shows that indeed there was a clear social class gradient in mortality, with the elite having higher survival chances between ages 35 and 55 than the middle class and farmers. Differences were even more apparent in comparison with workers. Over time there was a strong convergence among social classes in mortality levels. The implications of our results for the dominant views on the change in living standards in the past are discussed.

'Der Beitrag diskutiert am Beispiel von demographischen Mikrodaten methodologische Probleme von Längsschnittdaten. Die Herausforderungen bestehen darin, 1. Lebensverläufe in kartesische Datenformate zu transformieren, die mit den Erfordernissen gängiger statistischer Analysesysteme kompatibel sind, und 2. Datensätze für interlokale und interkulturelle Studien vergleichbar zu machen. Um dieses Ziel zu erreichen wird eine intermediäre Datenstruktur (IDS) vorgeschlagen, die auf alle Datenbanken übertragen kann. Die Autoren erläutern den Vorteil des IDS-Ansatzes und die Maßnahmen, die zur Umsetzung des Konzeptes führen werden.' (Autorenreferat)

New micro‐level data have recently become available for three provinces of The Netherlands for the period 1812–1912, which allow the study of the evolution of socio‐economic differentials in infant and childhood mortality. The authors found significant differences in the levels of infant mortality by social group between the three provinces, and a wide variety in the pattern of social inequality. This showed the importance of the regional environment for the level of infant mortality in the nineteenth century. Contrary to expectations, strong social differences were also observed in neonatal mortality. Being born in an urban environment did not have a strong effect on survival during the first year of birth.

Twenty-three major databases containing historical longitudinal population data are presented and discussed in this volume, focusing on their aims, content, design, and structure. Some of these databases are based on pure longitudinal sources, such as population registers that continuously observe and record demographic events, including migration and family and household composition. Other databases are family reconstitutions, based on birth, marriage and death records. The third and last category consists of semi-longitudinal databases, that combine, for instance, civil records and censuses and/ or tax registers. The volume traces the origins of historical longitudinal databases from the 1970s and discusses their expansion worldwide, in terms of sources and hard- and software. The contributions highlight the unique genesis and common developmental arcs of these databases, which are rooted in the fields of quantitative history, social and demographic history, and the history of ordinary people. The importance of these databases in advancing knowledge and insights in various disciplines is emphasized and demonstrated, along with the challenges and opportunities they face. The collection of technical descriptions of these databases represents the most comprehensive and up-to-date overview of large database with longitudinal micro-data on historical populations. It includes descriptions of databases from Europe, North America, East-Asia, Australia, South-Africa and Suriname. Technical details, in terms of data entry, cleaning, standardization and record linkage are meticulously documented. The volume is a must-have for all scholars in the field of historical life course studies.

This book addresses the problems that are encountered, and solutions that have been proposed, when we aim to identify people and to reconstruct populations under conditions where information is scarce, ambiguous, fuzzy and sometimes erroneous. The process from handwritten registers to a reconstructed digitized population consists of three major phases, reflected in the three main sections of this book. The first phase involves transcribing and digitizing the data while structuring the information in a meaningful and efficient way. In the second phase, records that refer to the same person or group of persons are identified by a process of linkage. In the third and final phase, the information on an individual is combined into a reconstruction of their life course. The studies and examples in this book originate from a range of countries, each with its own cultural and administrative characteristics, and from medieval charters through historical censuses and vital registration, to the modern issue of privacy preservation. Despite the diverse places and times addressed, they all share the study of fundamental issues when it comes to model reasoning for population reconstruction and the possibilities and limitations of information technology to support this process. It is thus not a single discipline that is involved in such an endeavor. Historians, social scientists, and linguists represent the humanities through their knowledge of the complexity of the past, the limitations of sources, and the possible interpretations of information. The availability of big data from digitized archives and the need for complex analyses to identify individuals calls for the involvement of computer scientists. With contributions from all these fields, often in direct cooperation, this book is at the heart of the digital humanities, and will hopefully offer a source of inspiration for future investigations