Comparability of the databases

The first issue related to the comparability of the data is related to the period covered by the databases for each city (see figure 2.4). The population registers from the Antwerp COR*database started in 1846. This means that from that year on it is possible to follow the life course of migrants and natives. The last register covered by the Antwerp COR* database is the register that opened in 1910 and closed in 1920. For Rotterdam we have register data from 1850 to 1940. However, the DVI dataset followed a slightly different periodization as the first cohort of migrants were followed only from 1870 on. For more details see (Mandemakers 2006). The Stockholm Historical Database covers the period 1878-1926. We can therefore conclude that the period 1878-1920, making up in total 42 years, is covered by all three databases. Before that date we are missing information on Stockholm. The period 1920-1930

71 is not covered by Antwerp and for the same period three years are missing for Stockholm, i.e. 1927-1930.

Figure 2.4 Period covered by the data for each city

Although the three databases contain highly comparable data, not every issue of this PhD thesis can be perfectly compared across all three cities, as a consequence of data constraints resulting mostly from differences in the source material and different ways of sampling. In the Rotterdam DVI dataset, for instance, the Italians and Germans are the only two international migrant groups. These two groups consisted only of stayers, who became parents and grandparents in the Dutch city, as the database makers had very specific research aims in mind (see Mandemakers 2006). Stayers are, of course, a highly selective group, which cannot be used, for example, to evaluate the migrants’ access to marriage and reproduction, as those migrants who did not get access to the marriage market are simply not in the data.

Next, certain databases are more suited to going deeper into a certain topic than others, due to certain adavantags. We can provide one example: The data on Rotterdam allowed us to follow internal migrants across the whole country, which is not the case for Antwerp and Stockholm. We have taken advantage of this interesting characteristic of the data to evaluate whether migrants who left the city were a selective group of vulnerable people, who were at an increased risk of dying, as has been posited by the so-called Salmon-Bias hypothesis.

1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940

72 Table 2.1 Comparing the three databases

Antwerp COR*-database Historical Sample of the Netherlands

Stockholm Historical Database

Source materials Population registers and birth, marriage and death certificates

Population registers and birth, marriage and death certificates

Roteman population register

Sampling method Letter sample; ‘vacuum cleaing’ method

Random sample from the birth certificates; aim of reconstructing as many full life courses as possible.

Total population

Geographic coverage of the database

Antwerp district The Netherlands Stockholm city (23 out of 36 rotars were digitalized at the time of the

retrievals) Geographic origin of

migrants

Internal and international migrants, mainly from neigbouring countries

Internal migrants from the whole country in main dataset; Internal migrants from the provinces of Zeeland and Brabant and German, Italian and Italian- speaking Swiss migrants in DVI sample

Internal and international migrants, mainly from neigbouring countries

Type of migrants in terms of duration of stay

Both stayers and leavers Stayers and leavers among the internal migrants from the main dataset; stayers only among the DVI sample

Both stayers and leavers

Main characteristics of migrants included (not exhaustive)

First name, last name, sex, age, address, occupation, civil status, family composition (in- and outside the household), dates and places of birth, marriage and death, dates of migrations and origin and destination of migrations.

First name, last name, sex, address, age,

religion, occupation,

address, civil status, family composition (inside the household), dates and places of birth, marriage and death, dates of migrations and origin and destination of migrations.

First name, last name, sex, address, age, occupation, civil status, family composition (inside the household), dates and places of birth, marriage and death included, dates of

migrations and origin and destination of migrations.

Finally, the fact that all three databases contain highly comparable information does not mean that comparisons are easily made. The main problem is that all three databases have a different data structure and have deviant variable names. This made it impossible to automate the

73 database management process. Instead, database management tasks had to be processed separately for each database, which turned out to be an extremely time-consuming task. With database management we refer to the process that transforms data on individuals from a large number of tables in relational databases into rectangular files, which can be read by statistical programs. The process, which is illustrated in a simplified way in figure 2.4, had to be conducted three times for each sub-topic of this PhD thesis and for each database, in order to obtain a uniform table of analysis every time. This required several thousands of queries, and was extremely time-consuming.

Figure 2.5: From raw data to tables of analyses

75

3 Research Context

This chapter draws in part upon the following publications:

Puschmann, P., Van den Driessche, N., Grönberg, P., Van de Putte, B., Matthijs, K. (2015). From outsiders to Insiders? Partner choice and marriage among internal migrants in Antwerp, Rotterdam & Stockholm, 1850-1930. Historical Social Research - Historische Sozialforschung (Köln), 40 (2), 319- 358.

Puschmann, P., Grönberg, P., Kok, J. & Matthijs, K. (2012). Upward Mobility Among Different Groups of Migrants and Natives in Stockholm, 1878-1926. Working paper WOG/HD/2012-7, Leuven: Centrum voor Sociologisch Onderzoek.

76