# POPP. An OCR-Generated Database of the Population Censuses of Paris (1926-36)

Sandra Brée, Victor Gay, Marion Leturcq, Mariia Buidze, Baptiste Coulmont, Yoann Doignon, Thierry Paquet, Thomas Constum, Pierrick Tranouez, François Merveille

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Sandra Brée, Victor Gay, Marion Leturcq, Mariia Buidze, Baptiste Coulmont, et al.. POPP. An OCR-Generated Database of the Population Censuses of Paris (1926-36). 2025. hal-04720215v1

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Preprint submitted on 5 Sep 2025 (v1), last revised 8 Jan 2026 (v2)

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Distributed under a Creative Commons Attribution 4.0 International License
POPP. An OCR-Generated Database of the Population Censuses
of Paris (1926–36)*

Sandra Brée† Victor Gay‡ Marion Leturcq§ Mariia Buidze¶

Baptiste Coulmont‖ Yoann Doignon** Thierry Paquet††

Thomas Constum†† Pierrick Tranouez†† François Merveille‡‡

September 2025

Abstract

Empirical research in historical demography is typically time-consuming and labor-
intensive. Recent advances in machine learning open new opportunities for constructing
large-scale databases much faster and at lower cost, but they also raise methodological
challenges. This article presents the construction process of the POPP database, a
data collection project based on the nominative lists of the 1926, 1931, and 1936 Paris
population censuses. The POPP database contains records on nearly nine million
individuals, including their names, year and place of birth, nationality, relationship to
the household head, and occupation. These data provide unprecedented opportunities
to study the composition of the Parisian population during the interwar period and,
more broadly, the structure of urban populations. Beyond its substantive contribution,
this article demonstrates both the potential and the limitations of applying artificial
intelligence to the construction of large historical demographic databases.

<footnote>*This research was funded by the CollEx-Persée, the GED-Campus Condorcet, Progedo and the CNRS.</footnote>

<footnote>†CNRS, Centre National de Recherche Scientifique (CNRS), Laboratoire de Recherche Historique Rhônes Alpes (LARHRA).</footnote>

<footnote>‡Toulouse School of Economics (TSE) and Institute for Advanced Study in Toulouse (IAST).</footnote>

<footnote>§Institut National des Études Démographiques (INED).</footnote>

<footnote>¶Centre National de Recherche Scientifique (CNRS) and Progedo.</footnote>

<footnote>|| École Normale Supérieure (ENS), Paris Saclay.</footnote>

<footnote>**Centre National de Recherche Scientifique (CNRS), Identité et différenciation de l'espace, de l'environnement et des sociétés (IDEES).</footnote>

<footnote>††Université de Rouen, Laboratoire d'informatique, de traitement de l'information et des systèmes (LITIS).</footnote>

<footnote>‡‡Humathèque Condorcet.</footnote>
# 1. Introduction

From the very beginning of the field of historical demography, researchers have recognized the value of computers not only for computing statistics, but also for the creation and processing of source material—particularly in a field where collecting census or civil registration data is both highly time-consuming and repetitive. But recent advances in artificial intelligence and deep learning have now opened new possibilities to historical demographers for automated data collection. In particular, optical character recognition (OCR) technology enables computers to interpret the shapes of digitized images and translate them into alphanumeric characters. However, handwritten text recognition (HTR) remains particularly complex. This was one of the key challenges faced by the POPP project team. Indeed, the aim of the POPP project—the first historical demographic database in France to rely on artificial intelligence—aimed to construct a database of nearly 9 million entries derived from the handwritten nominative lists of the Paris population censuses of 1926, 1931 and 1936.<fnref n="1" /> A further challenge was to correct and adapt the resulting database for statistical analysis.

Although the nominative lists of the census are generally easy to interpret when read—often allowing straightforward identification of individual and household characteristics—they are far more difficult to analyze globally and automatically, especially in an urban setting. Indeed, in large cities, the size of the population often required multiple enumerators—sometimes even within a single district—and individual census forms were filled without the proper application of (limited) instructions. As a result, an HTR algorithm is insufficient on its own. We therefore had to adapt the database specifically for statistical analysis. Tasks that are manageable by hand when processing individuals one at a time, such as standardizing the spelling of birthplaces or inferring relationships within households, become infeasible when working with nearly nine million records. The advantages of this type of large-scale data collection, such as full population coverage and substantial time savings, thus come with trade-offs inherent to processing data at scale. This requires automating not only the transcription, but also the interpretation, correction, and structuring of the source material, some tasks complicated by the fact that historical documents were never intended for use in a modern database.

The technical steps involved in creating the POPP database using artificial intelligence have already been detailed in a dedicated data paper (Constum et al., 2022). The aim of this article is to demonstrate that the work of IT specialists alone is not sufficient to transform digitized source images into a structured database amenable to statistical analysis. Its

<footnote n="1">¹POPP stands for *Project for the Ocerization of the Parisian Population Census* (see https://popp.hypotheses.org).</footnote>
primary objective is to describe the additional steps required to achieve this transformation
in the case of the Paris censuses of 1926, 1931, and 1936. Following a brief overview of the
relationship between computing and historical demography (Section 2), and an introduction
to the value of nominative census records (Section 3), we turn to the creation and the post-
processing of the database (Section 4). We then outline future developments for the POPP
database and show how it will serve both academic research and civil society (Section 5).
Finally, we describe the availability of the POPP database (Section 6).

## 2. Historical Demography and Computers

Almost from the inception of historical demography—if not immediately—historical demographers recognized of the value of computers for the creation, processing, and statistical analysis of databases (Schofield, 1972). Indeed, anyone who has ever collected census or civil registration data knows how time-consuming and repetitive this task can be. In France, as early as 1966, Marcel Couturier introduced a “new mechanographic methodology” (which would become *Forcod B*) that enabled data to be collected using a tape recorder. At the time, he noted that researchers were then “approaching the time when [they] will be able to write instructions for the machine themselves, as ‘scientists’ (sic) have been doing for a long time” (Couturier, 1966, p. 69). He emphasized the central role of the researcher in defining their analytical objectives. Pierre Goubert—one of the founders of historical demography—expressed a similar view: “what machines give you is largely what you give them, [and that requires] a very thorough intellectual analysis.” This sentiment is echoed by Antoinette Fauve-Chamoux who later remarked, in the introduction to an article on family reconstruction, that “[t]he miracle will not come from the computer, but from the researcher himself” (Fauve-Chamoux, 1972, p. 1083). The discussion following Couturier’s article is especially interesting because it highlights the tensions between time savings, cost, data quality, and the scale of the populations analyzed.<fnref n="2" /> One of the primary challenges identified even then—tracking the same individual across multiple sources—remains a fundamental difficulty today, as we will return later in this article.

Over the past 65 years, numerous databases in historical demography have been developed (Edvinsson, Mandemakers and Smith, 2023; Kesztenbaum, 2021; Mandemakers, 2025). Recent advances in artificial intelligence have opened up new possibilities for database creation and thereby new relationships between historical demographers and computational tools. Several research teams are actively exploring the most effective techniques for automating data extraction and processing. A significant milestone in this effort was the first

<footnote n="2">²In the case of certain articles previously presented at conferences, the discussions that followed the presentation are reported after the article in the journal. This makes them a very rich historiographical source.</footnote>
European workshop on automatic registration, organized in 2019 by the International Union for the Scientific Study of Population (IUSSP). Among the four studies presented, two focused on the digitization of historical records in Danish and Spanish (Sandholt Jensen and Nørmark Sørensen, 2019; Pujadas-Mora, 2019). Longstanding projects, such as the Bal sac survey, have also been enhanced thanks to the integration of AI technologies (Tarride et al., 2023; Vézina and Bournival, 2020). The importance of these developments is reflected in the growing number of conference sessions developed to the topic in recent years. The POPP project positions itself within this broader movement, contributing to the ongoing transformation of historical demography through the use of artificial intelligence.

One of the major advantages of using these techniques to build historical demography databases—beyond the obvious time savings—is their capacity to handle very large populations. Once the software has been developed for a coherent dataset, it makes little difference whether it processes 300 or 30,000 pages—aside from processing time, which remains an important consideration. However, data curation remains particularly demanding, both in terms of time and the need for skilled research support. Before returning to the challenges of curating and adapting the POPP database, we first discuss the source material itself: the nominal lists of the population census.

# 3. Source Material

## 3.1. *The Nominative Lists of the Population Census in French Historical Demography*

Population censuses are among the richest sources available for historical demographic research. Yet, until recently, they remained largely underutilized by French historical demographers (Gourdon and Ruggiu, 2015). This limited use can be attributed in large part to the dominance of the family reconstitution method developed by Louis Henry (Henry, 1953; Henry and Fleury, 1956). The strong focus on this approach, which relied heavily on parish registers, likely contributed to the lack of response from French scholars to Peter Laslett’s influential appeal at the 1969 Cambridge Conference (Laslett, 1965; Laslett and Wall, 1972). Then, Laslett urged his colleagues to examine household structures in their own countries to test his hypothesis that, as in England, pre-industrial households were more often nuclear than previously assumed.

Critics of census-based analysis have often drawn on Lutz Berkner’s argument, contending that censuses offer only static “snapshots” of the population and household structure at a single moment in time (Gourdon and Ruggiu, 2015). As such, they are seen as quickly outdated and unable to capture the dynamic changes in household composition over time
(Berkner, 1972; 1975).

Since then, and especially the 1990s, a growing body of research has demonstrated the value of population censuses for historical demography, particularly because they reveal patterns of co-residence (Mandemakers, 2025). In response to earlier criticisms of the census as a static source, researchers have developed methods to trace individuals across multiple census waves, thereby recovering aspects of their life histories, even over relatively short periods. This approach will be presented and critically assessed in Section 4. Before turning to these methodological considerations, we begin with an examination of the French and Parisian censuses, and in particular those of the interwar period, which serve as the foundation for the POPP database.

## 3.2. *A Brief History of the French and Parisian Population Censuses*

Two types of historical sources provide individual-level information on the French population at the national scale for the post-revolutionary period: civil registers—birth, marriage, and death records—and the nominative lists of the population censuses. Civil registers have been recorded continuously since 1789—and even before for parish registers—and are generally well preserved, largely thanks to their legal status as official administrative documents admissible in court rulings (Esmonin, 1964). In contrast, census nominative lists lack such legal standing and are therefore not protected by preservation regulations, although their use may in fact predate that of civil records. As a result, many of these lists were not systematically preserved, especially those from the early nineteenth century (Biraben, 1970). In fact, nominative lists remain an intermediate document with only an immediate utilitarian function—typically, to maintain an up-to-date account of a municipality’s population. Once the information they contained became obsolete, there were no legal or institutional barriers to their destruction.

Following a decree in 1822, nominative lists were to be drawn-up along the population census—which was conducted every five years in years ending in 1 and 6—and were to include all inhabitants regardless of age. In practice, however, it was not until 1836 that these lists began to be compiled regularly. This continued until 1946, with a few exceptions: the 1871 census was postponed to 1872 due to the Franco-Prussian War, and the censuses scheduled for 1916 and 1941 were canceled because of the First and Second World Wars. After 1946, the frequency of censuses declined, as their financial and logistical costs were deemed too high to maintain the five-year interval.

In Paris, the first municipal statistical office was established in 1816 (Biraben, 1963). The following year, in 1817, the city conducted its first population census. Unlike elsewhere in France, this census was not based on a general nominative list but rather on a survey of
individual and household records. This data collection method was still praised a century
later and eventually served as a model for nationwide implementation. Although praised for
its quality, this system meant that no nominal list of the population of Paris was drawn up
until 1926. Indeed, beginning with the 1866 census, the city of Paris acquired an exemption
from producing nominative lists due to the city's size and the high cost of such an enterprise
(Biraben, 1963). As a result, no nominal list from Paris prior to 1926 has survived—most
likely because they were never created. Fortunately, the nominative lists from the 1926, 1931,
and 1936 censuses were preserved and are available in the Paris municipal archives—hence
the focus of the POPP project on the interwar period.<fnref n="3" /> Although statistics have been
compiled for the city of Paris since 1817—including particularly detailed district-level data
between 1881 and 1896—the published statistics for the interwar period are much sparser
and limited to the city level.<fnref n="4" /> The individual-level information contained in the nominative
lists of the interwar censuses thus offer unprecedented opportunities for analyses, both at
the individual and highly granular aggregated levels—down to the neighborhood, street, or
even building levels—as we discuss in Section 5.

### 3.3. The Interwar Parisian Population Censuses

In France, the census nominative list records all the inhabitants of a commune and
specify the household to which each person belongs.<fnref n="5" /> In Paris, however, the population is
not classified by commune but along its 80 districts (quartiers), themselves grouped into 20
arrondissements (Figure 1).

The census distinguishes between three categories of population: the population of usual
residence, the population counted separately (collective dwelling), and visiting guests. In
practice, individual and household census forms were distributed by enumerators to the

<footnote n="3">³The nominative lists from the 1946 census are also available. We hope to process them using OCR technology and integrated them into a structured database as well. These lists were not included in the current analysis alongside the three interwar censuses due to the specific historical context of the Second World War. Moreover, the layout and formatting of the 1946 census tables differ significantly from those of the interwar period, which requires the development of an alternative deep learning algorithm tailored to their structure.</footnote>

<footnote n="4">⁴Statistical publications for the city of Paris have been produced since the early nineteenth century and contain a wealth of information on the population but also on hospitals, the economy, weather, prisons, schools, and other aspects of urban life. Depending on the period, population data from the census were included therein. See *Recherches statistiques sur la ville de Paris* (six volumes published between 1821 and 1860; a seventh volume, completed and ready for publication, unfortunately burned along with the other archives of the Parisian population during the Commune in May 1871); *Statistique municipale de la ville de Paris* (1865-79; much less detailed and only on the movement of the population. The results of the 1866, 1872 and 1876 censuses do not appear therein). Finally, the *Annuaires statistiques de la ville de Paris* (1880-1967). Four very detailed publications exist for the 1881, 1886, 1891, and 1896 censuses (see Brée, 2016, pp. 59-93).</footnote>

<footnote n="5">⁵On the origins, development, and availability of this archival sources, see Pinchemel (1954), Biraben (1963), and Haug (1979).</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 1. The 80 districts of Paris, grouped into 20 arrondissements</figcaption></figure>

Source: Nagaï (2002, p. 325).

place of residence a few days before the actual census date, when forms were collected. In the following two weeks, enumerators compiled the nominative list. This document consists in tables with 30 rows—each row corresponding to one individual (Figure 2).

<figure><img src="image_4.png" /><figcaption>Figure 2. Nominative list of the 1926 census (Belleville district)</figcaption></figure>

Source: Archives de Paris, D2M8 307. The corresponding annotation of the first row is “Poli-tis/Mathieu/93/Roumanie/grec/M/ch./¤/e. cinéma/?19.387.”

The Parisian nominal lists are distinctive in that their tables are more complete than those of the rest of France. Specifically, two additional columns appear in the Parisian
tables, which are absent from the lists compiled in other départements: marital status and
level of education (the latter absent in 1936). Unfortunately, however, a gender column is
missing in Paris, as elsewhere in the country, even though this information was collected on
individual forms.<fnref n="6" />

As many historical administrative records, the nominative lists of the Parisian population censuses of 1926, 1931 and 1936 are now available online in digital form.<fnref n="7" /> Indeed, institutions “have invested a lot of resources in the last decade in digitizing large collections of historical documents not only for preserving them in a digital format, but also to give access to scholars and citizens at large through web-based digital repositories” (Fornés, Lladós and Pujadas-Mora, 2019, p. 2). However, these online platforms are typically limited to browsing and searching based on image metadata, and do not allow for direct queries to the actual content of digitized documents.

During the interwar period, Paris reached its historical population peak, with almost 3 million inhabitants: 2,871,429 in 1926; 2,891,020 in 1931; and 2,829,746 in 1936. Each census therefore represents approximately 50,000 images, typically composed of two double pages, hence covering up to 60 individuals. In total, processing the three censuses required handling about 150,000 images, i.e., 300,000 handwritten pages. To make this exceptionally rich material usable for research, the POPP project created a structured database of nearly 9 million individual records based applying OCR technology to the 300,000 images from the 1926, 1931, and 1936 Parisian censuses.

# 4. Adaptations and Corrections of the Database

## 4.1. *Creation of the Database by the IT Team*

The three censuses processed by the POPP project share an almost identical structure.<fnref n="8" /> This fixed structure significantly facilitated the recognition of handwritten information, as each cell was expected to contain a specific type of information—a name, date, address, etc.—that could be modeled using dictionaries or regular expressions to guide and constrain the recognition process.

Because the original tables were filled in by hand, the column layout was often only loosely

<footnote n="6">⁶In fact, the information recorded on individual bulletins and family forms was far more detailed than the fifteen entries included in the nominative lists. While this richer information was used to produce aggregate statistics published in the official census reports, it is no longer accessible at individual level or at any aggregate level than that of the commune.</footnote>

<footnote n="7">⁷The nominative lists of the Parisian population censuses of 1926, 1931, and 1936 are accessible at http://archives.paris.fr/s/11/denombrements-de-population.</footnote>

<footnote n="8">⁸Between 1926 and 1936, the address columns were simplified and the education level column appeared in 1936.</footnote>
followed. Some words frequently spilled over into adjacent columns or were written between
rows, making the automated processing challenging. In addition, since the tables were filled
out by different enumerators—sometimes multiple within the same district—certain fields
were filled. For instance, in the “place of birth” column, some entries include both the
commune and département, while others provide only one of the two.

Transforming digitized images into a structured CSV database involved several distinct stages (Figure 3). While we provide a brief overview here, a detailed account of the technical workflow is available in an article dedicated to the IT component of the POPP project (Constum et al., 2022). The processing pipeline comprises four main stages: image pre-processing, handwriting recognition, integration of domain-specific knowledge, and content verification.

<figure><img src="image_5.png" /><figcaption>Figure 3. Workflow of the OCR pipeline</figcaption></figure>

Source: Constum et al. (2022, p. 5).

The pre-processing stage focuses on detecting table structure, cropping images, and de-
tecting the individual lines within each table. As previously mentioned, the row structure
of the table was generally well respected. In contrast, the column structure was often com-
promised due to the limited writing space, leading to frequent overlaps between entries in
adjacent columns. As a result, it was not feasible to identify table cells based on visual
boundaries. Instead, cell identification had to rely on interpreting their textual content. Al-
though nominative lists do not contain actual paragraphs, we decided to treat them as blocks
of text, each row corresponding to a line. This approach required implementing automated
line detection as an important step in the recognition process.

To adapt the handwriting recognition model, an initial set of training data was required. Although the structure of the tables is highly consistent across pages, there is great variability in writing style, background color, ink type and image resolution. To account for this variability, we have therefore annotated one double page for each of the 80 districts in the 1926 census so as to create a training set that was as representative as possible of the corpus as a whole. This initial dataset comprises 160 pages for a total of 4,800 lines of handwritten
text. In addition, in order to conduct experiments with a single writing style, we annotated another dataset based on 49 pages (1,470 lines) from the Belleville district, all written in a single writing style. Of these, 39 pages were used for training, 5 for validation, and 5 for testing. Training our model on the generic dataset yielded a character error rate (CER) of 7.08% and a word error rate (WER) of 19.05% on the test set. These initial results are acceptable, though the error rate is higher than the state-of-the-art on other datasets. To improve performance, we then applied a self-training approach, a technique in which a model trained on manually labeled data (the “teacher”) is used to generate pseudo-labels that are then used to train another model (the “student”). This technique is particularly effective when large quantities of unlabeled data are available, as is the case in the POPP project. The unlabeled dataset consisted of 2.4 million linear images randomly selected from the 1926 census. Using this self-training approach, we reduced the CER from 7.08% to 4.52%.

We now turn to domain knowledge extraction and content verification. To improve the output of the OCR model, we used a combination of column-specific grammar rules and dictionaries—these dictionaries were constructed using multiple sources, including the initially annotated pages. In addition, we implemented a rejection mechanism that compared the output of the optical model with that of the grammatical model, which enabled us to reject sequences for which the divergence between the best optical and grammatical paths exceeded a predefined threshold. This approach helped filter out implausible outputs and improved the overall reliability of the extracted data.

Note that the first five columns—relating to addresses and household identifiers—were not processed using the automated method described above due to several challenges specific to these fields. Indeed, enumerators often recorded street names inconsistently: sometimes at the top of the page, sometimes in the middle, and both horizontally or vertically. These variations made automatic recognition particularly difficult. As a result, we relied on a specialized scanning company to manually extract address information. Similarly, household identification posed other difficulties, as enumerators used a range of notations to indicate households—numbers, brackets, or sometimes more ambiguous marks. Where available and clearly recorded, we used the “head of household” information to infer household membership automatically (see below).

## 4.2. Correcting and Adapting the Database

Although OCR error rates are very low for the POPP database (Appendix Table A1), various types of errors persist. To improve data quality and enable reliable statistical analysis, we made a series of corrections and adjustments to the initial database. First, we created new variables—most notably gender, which was not included in the original nomi-
native lists. Second, we corrected the existing variables. The corrections all follow the same general principle: we retain the variable as read by the OCR. We then create an additional variable that corrects spelling errors while remaining faithful to the source. Finally, we harmonize the categories to facilitate the statistical analysis. Examples are provided below. Data normalization is not merely a technical operation aimed at reducing noise or addressing missing values. Rather, it often requires navigating ambiguity and making explicit choices about what kind of information to prioritize: standardization for analytical comparability or preservation of original forms for interpretive richness. In our case, while the primary objective is to produce usable structured variables, we were cautious not to erase potentially meaningful variation, especially when it could signal historically or culturally situated forms of identity.

The remaining errors fall into two main categories: incorrect character recognition and misalignment of text into the wrong columns. To prepare the database for statistical analysis, we thus carried out a canonicalizing process to standardize expressions—ensuring that all variations referring to the same entity were written in a consistent way. During the data extraction phase, the algorithm inputted the symbol $ to flag entries that were not validated by the system. These flagged strings received specific attention during the post-processing phase and were systematically reviewed and corrected. In addition, some instances of column misalignment—marked with the symbol /—were identified. In these cases, the system failed to detect column breaks, resulting in data from one column spilling into another. Other types errors were specific to individual column. The corrections made during post-processing are documented in detail in the variable dictionary accompanying the database. However, several are worth highlighting here, including the correction of first names, the creation of the gender variable, the specific corrections of the places of births, the procedures used to clean and harmonize occupations, and those used to reconstruct the household structures.

**Types of population.** The POPP database includes 2,845,057 individuals in 1926; 2,828,614 in 1931; and 2,784,494 in 1936 (Table 1). The nominal lists divided the population into three categories: the “usual population” (about 96% of the total population); the “population counted separately” (residents of hospices, prisons, and similar institutions, representing 1.4–2.2%); and “temporary visitors” (1.8–2.3%). The latter group consists of individuals present in Paris at the time of the census but who cannot properly be considered part of the “Parisian population.”

Within the present population of Paris—those normally residing in the city—some individuals were absent at the time of the census. In the nominal lists, their entries are crossed out and often marked “ABS” (Figure 4). On average, absentees account for 2.8% of the
<table><caption>Table 1. Population counts in Paris in 1926, 1931, and 1936</caption><thead><tr><th>Census</th><th>Usual Population (A)</th><th>Population counted separately (B)</th><th>Total resident or legal population (A+B)</th><th>Present or de facto population (A+B-abs.)</th><th>Temporary visitors (D)</th><th>All population (A+B+D)</th><th>All absent individuals</th></tr></thead><tbody><tr><td>1926</td><td>2,739,706</td><td>41,098</td><td>2,780,804</td><td>2,688,370</td><td>64,253</td><td>2,845,057</td><td>92,434</td></tr><tr><td>1931</td><td>2,714,401</td><td>63,511</td><td>2,777,912</td><td>2,700,071</td><td>50,702</td><td>2,828,614</td><td>77,841</td></tr><tr><td>1936</td><td>2,678,222</td><td>51,887</td><td>2,730,109</td><td>2,668,494</td><td>54,167</td><td>2,784,276</td><td>61,615</td></tr></tbody></table>

Source: POPP database.

Notes: for the usual population (A), the population counted separately (B) and the legal population (A+B), absent people are included; but they are excluded from the present, or the *de facto* population.

present population across the three censuses (3.3% in 1926, 2.8% in 1931, and 2.3% in 1936). As in most of the census studies, the results presented in this paper will focus mainly on this present or *de facto* population, that is the population usually residing in Paris, excluding those who were absent at the time of the census.

<figure><img src="image_6.png" /><figcaption>Figure 4. Absent individual in the 1936 census (Père Lachaise district)</figcaption></figure>

Source: Archives de Paris, D2M8 702.

Details on population categories are available in the *Paris Statistical Yearbook (Annuaire Statistique de Paris)*, but only for 1926. In that year, the officially recorded total resident population exceeds that found in the POPP database by about 3.2% (Table 2): 125 individuals are missing from the separately counted population (0.3% of that group), and 90,500 from the usual population (3.2%). In 1931 and 1936, the discrepancies for the entire legally domiciled population are 3.9% and 3.5%, respectively. In 1931, part of the difference arises from the missing nominative lists for the Enfants Rouges and Sainte-Avoye districts (3rd ar.) and the Saint-Georges district (9th ar.). In 1936, the Sainte-Marguerite district (11th ar.) list is very incomplete, and parts of the Javel (15th ar.) and Belleville (20th ar.) lists are also missing.

**First and last names** No correction was applied to the “last name” variable. However, we applied substantial corrections to the “first names” variable. Initially, 8.5% of individuals
<table><caption>Table 2. Population counts in Paris in 1926, 1931, and 1936</caption><thead><tr><th>Census</th><th>Usual Population (A)</th><th>Population counted separately (B)</th><th>Total resident or legal population (A+B)</th><th>Present or de facto population (A+B-abs.)</th></tr></thead><tbody><tr><td>1926</td><td>2,830,206</td><td>41,223</td><td>2,871,429</td><td>2,838,416</td></tr><tr><td>1931</td><td>No data</td><td>No data</td><td>2,891,020</td><td>No data</td></tr><tr><td>1936</td><td>No data</td><td>No data</td><td>2,829,746</td><td>No data</td></tr></tbody></table>

Source: *Annuaires statistiques de la ville de Paris*, several years.

Notes: temporary visitors are not indicated. The total resident population or legal population (“Population domiciliée ou de droit”) comprises the usual population and the population counted separately. The present population comprises the total resident population, excluding absent individuals.

in the dataset had unrecognized first names—that is, names not included in the dictionaries provided at the OCR stage. Longer first names such as “Marguerite,” those ending in “-e,” and abbreviated forms such as “M” for “Marie,” “Jh” for “Joseph,” or “Lse” for “Louise” were particularly prone to recognition error. To address this issue, we conducted a visual check of approximately 400 of the most common errors in order to identify the correct first name—e.g., “Gacton” could be interpreted as “Gaston,” etc. We then cross-referenced unrecognized first names against an extended list of 50 thousand first names based on the *Fichier des prénoms* (First names file, INSEE 2022) and the *Fichier des personnes décédées* (Deceased persons file, INSEE 2023).<fnref n="9" /> If an unrecognized first name appeared in either of these datasets, it was validated. If, by adding a final “-e” produced a first name that was present in either of the datasets, this form was also accepted. This procedure reduced the share of unrecognized first names to 3.5%—a decrease of nearly 60% in the number of initially invalid first names entries. We provide tables of the most frequent last names by census year, as well as first names by gender in Appendix Tables A2 and A3.

**Gender** The reconstruction of the missing “gender” variable relied primarily on the use of first names. We first created a gendered dictionary of first names using the INSEE databases mentioned above, and classified individuals as men or women if their first name was associated with a given gender in at least 75% of the cases in the INSEE database. However, some individuals could not be assigned a gender due to ambiguity—such as names like “Camille”—when the gender of their first name fell between 75% and 90%, or when the first name was not found in the dictionary, particularly in the case of foreign first names. For

<footnote n="9">⁹The *First names* file contains the first names of all babies born since 1945 and individuals born after 1900 who are still alive in 1945. The *Deceased persons* files contains the last and first names of individuals who died in France since 1970.</footnote>
first names falling within the 75-90% ambiguity range, we turned to contextual variables such as household position and occupation. Certain household positions—such as “wife,” “mother,” “sister,” “aunt,” or “sister-in-law”—clearly indicate a female gender, while others—such as “husband,” “father”, or “brother”—clearly indicate a male gender. Similarly, we relied on occupational titles when they were clearly gender specific. For instance, “maids” were classified as women and “valets” as men. We limited this approach to occupations that are unambiguously gendered and excluded those where gender is indicated only by a morphological variation such as a final “-e” to avoid introducing further uncertainty. We identify the gender of 96.25% of the population in 1926, 95.71% in 1931 and 95.79% in 1936. Most of cases of individuals whose gender was not identified is due to individuals whose first name was not found in the INSEE databases because of misspellings. Other cases, albeit a minority, are due to missing first names or ambiguous names. For instance, in 1926, the gender of 3.75% of individuals was not identified: 2.67% because of a name not found in the INSEE database, 0.39% because of a missing first name and 0.69% because of an ambiguous first name (see Table 3).

<table><caption>Table 3. Distribution of genders in the POPP database (%)</caption><thead><tr><th rowspan="2">Gender</th><th rowspan="2">1926</th><th rowspan="2">1931</th><th rowspan="2">1936</th><th colspan="3">Among non-missing</th></tr><tr><th>1926</th><th>1931</th><th>1936</th></tr></thead><tbody><tr><td>Men</td><td>43.52</td><td>43.46</td><td>43.31</td><td>45.22</td><td>45.41</td><td>45.21</td></tr><tr><td>Women</td><td>52.73</td><td>52.25</td><td>52.48</td><td>54.78</td><td>54.59</td><td>54.79</td></tr><tr><td>Total non-missing</td><td>96.25</td><td>95.71</td><td>95.79</td><td>100.00</td><td>100.00</td><td>100.00</td></tr><tr><td>Undeclared first name</td><td>0.39</td><td>0.49</td><td>0.7</td><td></td><td></td><td></td></tr><tr><td>First name not found in database</td><td>2.67</td><td>3.15</td><td>2.91</td><td></td><td></td><td></td></tr><tr><td>Ambiguous gender from first name</td><td>0.69</td><td>0.65</td><td>0.6</td><td></td><td></td><td></td></tr><tr><td>Total missing</td><td>3.75</td><td>4.29</td><td>4.21</td><td></td><td></td><td></td></tr><tr><td>Total</td><td>100.00</td><td>100.00</td><td>100.00</td><td></td><td></td><td></td></tr><tr><td>Total (n)</td><td>2,688,370</td><td>2,700,071</td><td>2,668,494</td><td></td><td></td><td></td></tr></tbody></table>

Source: POPP database.

Notes: the population is the *de facto* population, i.e., the usual population and the population counted separately, excluding absent individuals.

**Years of birth** Less than 3% of records for this variable did not match the format YYYY. When the cell contained one or two digits with the letter “A” appended, we deduced that the information was an age and reconstructed the year of birth by assuming that the age given
was the age reached in the census year. Where the information was given as a date in MM:YY or MMYY format, we extracted the numbers corresponding to the years. After these corrections, there were 1.1% of remaining missing values.<fnref n="10" /> Table 4 shows the age distribution found in the POPP database in 1926. Less than 1% of individuals whose gender was identified have an invalid age, but this proportion raises to 10% of individuals whose gender was not identified, mostly coming from an undeclared date of birth.

<table><caption>Table 4. Distribution of age groups by gender in the POPP database (%)</caption><thead><tr><th rowspan="2">Age group</th><th rowspan="2">Men</th><th rowspan="2">Women</th><th rowspan="2">Gender missing</th><th rowspan="2">Total</th><th colspan="4">Among non-missing</th></tr><tr><th>Men</th><th>Women</th><th>Gender missing</th><th>Total</th></tr></thead><tbody><tr><td>0-9</td><td>10.44</td><td>8.36</td><td>5.63</td><td>9.16</td><td>10.52</td><td>8.42</td><td>6.27</td><td>9.26</td></tr><tr><td>10-19</td><td>13.44</td><td>11.81</td><td>8.91</td><td>12.41</td><td>13.55</td><td>11.89</td><td>9.92</td><td>12.55</td></tr><tr><td>20-39</td><td>40.34</td><td>40.79</td><td>43.43</td><td>40.69</td><td>40.67</td><td>41.08</td><td>48.34</td><td>41.15</td></tr><tr><td>40-59</td><td>27.12</td><td>27.22</td><td>23.49</td><td>27.03</td><td>27.34</td><td>27.41</td><td>26.15</td><td>27.33</td></tr><tr><td>60+</td><td>7.86</td><td>11.12</td><td>8.38</td><td>9.6</td><td>7.92</td><td>11.20</td><td>9.33</td><td>9.71</td></tr><tr><td>Total non-missing</td><td>99.2</td><td>99.3</td><td>89.84</td><td>98.89</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td></tr><tr><td>Undeclared year of birth</td><td>0.5</td><td>0.4</td><td>7.87</td><td>0.72</td><td></td><td></td><td></td><td></td></tr><tr><td>Undefined year of birth</td><td>0.27</td><td>0.28</td><td>2.24</td><td>0.35</td><td></td><td></td><td></td><td></td></tr><tr><td>Negative value</td><td>0.03</td><td>0.03</td><td>0.05</td><td>0.03</td><td></td><td></td><td></td><td></td></tr><tr><td>Value higher than 100</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.01</td><td></td><td></td><td></td><td></td></tr><tr><td>Total missing</td><td>0.81</td><td>0.72</td><td>10.17</td><td>1.11</td><td></td><td></td><td></td><td></td></tr><tr><td>Total</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td></td><td></td><td></td><td></td></tr><tr><td>Total (n)</td><td>1,169,909</td><td>1,417,486</td><td>100,975</td><td>2,688,370</td><td></td><td></td><td></td><td></td></tr></tbody></table>

Source: POPP database.

Notes: the population is the *de facto* population, i.e., the usual population and the population counted separately, excluding absent individuals.

A comparison between the age distribution generated from the POPP database and that based on published aggregate data revealed a notable discrepancy: a significant portion of the population—primarily men aged 20 to 39—were missing from the distribution derived from the POPP database. This significant bias indicated that the automated gender attribution process was less effective for men of foreign nationality, whose first names are underrepresented in the French first name databases we use. To address this issue, we manually attributed a gender to these first names, which substantially reduced the number of individuals with unassigned gender and helped correct the observed distortion in the age groups and gender distribution. The discrepancy between official statistics and POPP database re-

<footnote n="10">¹⁰This error rate includes about 90 thousand individuals and many unread signs or words (about 80 thousand).</footnote>
mains larger for men than women—albeit smaller than 1% for all age groups, see Appendix Table A5.

**Places of birth** The “place of birth” column includes a variety of information: the name of a commune (particularly in the case of large cities), a département, or a country for individuals born outside France. To standardize this variable, we created four distinct fields: “commune of birth,” “département of birth,” “country of birth,” and “other birth.” The “other birth” variable captures entries that could not be classify as a commune, département, or country, or which content belonged to another column. When the machine-generated output was vague with result like “Core,” the ambiguity is high: is this referring to “Corée” (Korea) or “Corse” (Corsica)? We chose not to force a normalization. Instead, such cases were either retained in the “other birth” variable or marked as “missing,” depending on the likelihood of a correct attribution.

Through manual review, we were able to reassign a large share of the ambiguous entries to the appropriate columns, thereby improving the overall quality and consistency of the birthplace data. Table 5 shows that approximately 4% of places of birth could not be identified.

<table><caption>Table 5. Distribution of places of birth in the POPP database (%)</caption><thead><tr><th rowspan="2">Place of birth</th><th rowspan="2">1926</th><th rowspan="2">1931</th><th rowspan="2">1936</th><th colspan="3">Among non-missing</th></tr><tr><th>1926</th><th>1931</th><th>1936</th></tr></thead><tbody><tr><td>Seine</td><td>35.8</td><td>35.51</td><td>35.89</td><td>37.44</td><td>36.81</td><td>37.05</td></tr><tr><td rowspan="2">France (mainland, excluding Seine)</td><td>49.41</td><td>49.75</td><td>50.32</td><td rowspan="2">51.67</td><td></td><td></td></tr><tr><td>51.58</td><td>51.94</td><td></td><td></td><td></td></tr><tr><td>Other country</td><td>10.42</td><td>11.2</td><td>10.67</td><td>10.90</td><td>11.61</td><td>11.01</td></tr><tr><td><em>Total non-missing</em></td><td>95.63</td><td>96.46</td><td>96.88</td><td>100.00</td><td>100.00</td><td>100.00</td></tr><tr><td>Undefined place of birth</td><td>3.46</td><td>2.68</td><td>2.36</td><td></td><td></td><td></td></tr><tr><td>Undeclared place of birth</td><td>0.91</td><td>0.86</td><td>0.76</td><td></td><td></td><td></td></tr><tr><td><em>Total missing</em></td><td>4.37</td><td>3.54</td><td>3.12</td><td></td><td></td><td></td></tr><tr><td><em>Total</em></td><td>100.00</td><td>100.00</td><td>100.00</td><td></td><td></td><td></td></tr><tr><td>Total (n)</td><td>2,688,370</td><td>2,700,071</td><td>2,668,494</td><td></td><td></td><td></td></tr></tbody></table>

Source: POPP database.

Notes: the population is the *de facto* population, i.e., the usual population and the population counted separately, excluding absent individuals.

The proportion of missing places of birth is higher for those individuals whose gender was
not identified, particularly when the first name was not declared (see Appendix Table A6). A comparison with official statistics shows that the POPP database tends to underestimate the proportion of men born outside France, which is likely due to the lack of effectivity in the automated gender attribution process for men of foreign nationality.

The data cleaning process inevitably involves trade-offs between harmonizing the data for analytical purposes and preserving the nuances originally present in the source material. This tension is particularly salient in the case of variables like “country of birth”. For example, while départements of birth could be matched against a fixed list of départements—making standardization relatively straightforward and low-risk in terms of information loss—the naming of countries is far more complex and historically contingent. Respondents or enumerators may refer to countries using names that reflect historical, colonial, or even political contexts, such as “Dahomey” or “Prussia”. In such cases, we deliberately chose not to normalize these entries to their current country name, as doing so could obscure the respondent’s perception or the socio-historical framing of their answer. Instead, we opted to harmonize the orthography while preserving the original denomination as much as possible. We created an additional variable that harmonized the country names.

**Nationality** Nationality introduced an additional layer of ambiguity. Not only did we face the same historical naming issues as with countries of birth, we also had to account for cases where nationality and country of birth places diverged, especially for individuals born outside France.

Correcting this variable requires interpreting how enumerators filled in the registers. In many cases, information on nationality is missing, as enumerators often left this column blank for people born in mainland France, while filling it in when it differed from the expected default. In other cases, the enumerator wrote the name of the country in both columns, meaning that it is both the place of birth and the nationality. It is therefore impossible for us to determine automatically (and it is not always clear in the source itself) whether a blank in the nationality column means that the line above or the column indicating the place of birth should be copied.

As a result, nationality cannot always be identified unambiguously. It is clear for French individuals born in mainland France—where the place of birth is recorded as a département and nationality is left blank (see Figure 5, top panel, rows 1–2)—as well as for foreigners born in mainland France, where the place of birth is a département and nationality is explicitly indicated. Nationality is also clear for foreigners whose nationality differs from their country of birth: in this case, the place of birth is recorded as a country, and nationality is indicated.

Ambiguity is highest for people born abroad with no nationality indicated: this could
<figure><img src="image_7.png" /><figcaption>Figure 5. Examples of places of birth and nationalities for non-French individuals</figcaption></figure>

Source: Archives de Paris, D2M8 230 (Panel a) and D2M8 679 (Panel b).

mean either French nationality or the nationality of the country of birth. For instance, in Figure 5 (top panel, row 3), an individual born in Switzerland is likely Swiss, since “Switzerland” is written across the two columns. However, the AI would read this as “place of birth” and leave nationality blank. In Figure 5 (bottom panel), the first row shows an individual born in Morocco with no nationality recorded, whereas in row 2 both country and nationality are explicitly indicated (both Dutch), while in row 3 a French individual is indicated as French born in Turkey. The same ambiguity arose in the case of French colonies. For example, an individual born in Algeria could be either a French citizen or considered an Algerian native (*indigène algérien*), so the country of birth alone does not give a precise indication of the nationality.

As a consequence, the “nationality” variable is likely biased, for several underlying reasons. The source itself is unclear and requires cautious interpretation when information is missing. Moreover, the machine cannot capture the subtleties of how enumerators reported place of birth and nationality, which varied from one enumerator to another. Since our corrections must apply consistently across the entire dataset and cannot account for individual enumerator practices, we adopted a cautious approach. We corrected nationality in unambiguous cases but left ambiguous ones unchanged, flagging them accordingly. This variable should be interpreted with great caution, and “place of birth” should be preferred whenever possible.
**Marital status** Because of its simplicity, the “marital status” variable required corrections of only 0.7% of cases. We performed a flat sort of this variable to identify the most common irregular entries that needed corrections. Some values indicated a marital status that did not match the grammar, which consists of four categories: married, single, widowed, or divorced. However, we retained the abbreviation “sep” to denote individuals who were separated—that is, still married but no longer cohabiting.

Where possible, we corrected values to match the grammar. We also manually corrected all values that occurred at least ten times across all three censuses. In addition, we addressed the cases of column mismatches by reallocating values to their appropriate column on a case-by-case basis. For instance, the value `$FILLE$` (girl) do not denote a marital status, but rather the relationship to the household head. In such cases, we removed `$FILLE$` from the “marital status” column and reassigned it to the “relationship to household head” column. These cleaning procedures reduced the share of incorrect values to just 0.1% across the three censuses (see Table 6).

<table><caption>Table 6. Distribution of marital statuses by gender in the POPP database (%)</caption><thead><tr><th rowspan="2">Marital status</th><th colspan="3">All</th><th colspan="3">Men</th><th colspan="3">Women</th></tr><tr><th>1926</th><th>1931</th><th>1936</th><th>1926</th><th>1931</th><th>1936</th><th>1926</th><th>1931</th><th>1936</th></tr></thead><tbody><tr><td>Married</td><td>53.62</td><td>54.49</td><td>55.51</td><td>60.50</td><td>60.94</td><td>62.93</td><td>48.67</td><td>49.67</td><td>50.19</td></tr><tr><td>Separated</td><td>0.01</td><td>0.01</td><td>0.02</td><td>0.01</td><td>0.01</td><td>0.02</td><td>0.01</td><td>0.02</td><td>0.02</td></tr><tr><td>Divorced</td><td>1.79</td><td>2.30</td><td>2.69</td><td>1.27</td><td>1.68</td><td>2.00</td><td>2.23</td><td>2.85</td><td>3.30</td></tr><tr><td>Widowed</td><td>9.79</td><td>10.09</td><td>10.42</td><td>3.83</td><td>3.93</td><td>4.05</td><td>14.7</td><td>15.25</td><td>15.73</td></tr><tr><td>Never married</td><td>34.70</td><td>32.88</td><td>31.12</td><td>34.32</td><td>33.3</td><td>30.85</td><td>34.29</td><td>32.01</td><td>30.54</td></tr><tr><td>Total non-missing</td><td>99.91</td><td>99.77</td><td>99.76</td><td>99.93</td><td>99.86</td><td>99.85</td><td>99.9</td><td>99.80</td><td>99.78</td></tr><tr><td>Total missing</td><td>0.10</td><td>0.22</td><td>0.24</td><td>0.07</td><td>0.14</td><td>0.16</td><td>0.10</td><td>0.20</td><td>0.22</td></tr><tr><td>Total</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td></tr><tr><td>Total (n)</td><td>2,310,268</td><td>2,318,954</td><td>2,256,139</td><td>983,851</td><td>984,653</td><td>950,589</td><td>1,234,030</td><td>1,226,738</td><td>1,200,962</td></tr></tbody></table>

Source: POPP database.

Notes: the population is the *de facto* population, i.e., the usual population and the population counted separately, excluding absent individuals. Individuals are aged 15 and over.

**Relationship to the household head** The “relationship to the household head” variable indicates the link to the household head, such as the head himself or herself, their spouse, children, mother, sister-in-law, domestic servant etc. Household heads are predominantly men. Indeed, in households consisting of a married couple, with or without children, the man is always recorded as the household head. However, women can also be designated as household heads, especially when they live without a partner.
The “relationship to the household head” variable initially contained 5.4% of entries that did not match our dictionary. We applied the same cleaning methodology as for the “marital status” variable. However, some values could not be matched to any of the modalities in other variables. For such entries occurring more than a hundred times, we manually reviewed the corresponding images to decipher the handwritten terms and determine an appropriate correction. Due to the time-intensive nature of this process, it was carried out only for this variable given its central importance in constructing household typologies. These cleaning efforts successfully reduced the proportion of incorrect values to 1.5% across the three censuses.

We then had to reconstruct the household compositions. The “relationship to the house-
hold head” variable was one of the key pieces of information we used to reconstruct the
household units. According to census instructions, individuals belonging to the same house-
hold were to be identified with a household identifier and grouped with a brace, with the
household head clearly indicated. In practice, however, these guidelines were inconsistently
followed as some enumerators included only braces or only household identifiers, and in
some cases failed to specify the household head altogether. To deal with this challenge,
we used household identifiers as our primary reference whenever they were present in the
original manuscript. For districts lacking these identifiers—which accounted for 36.4% of
the population of usual residence, generally cases marked only with a brace—we relied on
the “relationship to the household head” whereby each instance of a new household head
marked the transition to a new household on the nominative list. To track the reliability of
this household reconstruction, we created a binary variable indicating whether a given house-
hold identifier was derived from an explicit marker in the source or inferred from transitions
in the “relation to the household head” variable. However, some enumerators failed to record
this information correctly, thus making it impossible to detect household boundaries using
this method. In cases where neither of these methods was feasible, we commissioned the
scanning company that had processed the addresses to manually reconstruct the household
groupings.

**Occupations** The OCR performance for the “occupation” variable was relatively low (Appendix Table A1), largely because of the complexity of occupational strings and the wide variation in how enumerators recorded them. Indeed, the raw dataset contained about 680 thousand unique occupational strings, with a very long tail: 575 thousand of them (about 85%) appear only once. However, among individuals with a recorded occupation, concentration was rather limited—while the five most common strings cover 13% of individuals, the ten most common cover 18%; the 100 most common, 45%; the 1,000 most common, 71%; and
the 10,000 most common, 82%. To mitigate this challenge, we manually reviewed and stan-
dardized occupational strings occurring at least 30 times—representing about a thousand
occupational string. This procedure enabled us to assign a normalized occupation to about
77% of individuals with an occupational entry—we are currently working on improving the
processing of occupations.

In the final column of the nominative lists, enumerators were asked to specify whether
the individual was self-employed or, if not, the name of their employer. This variable was
left as read by the machine and was not corrected.<fnref n="11" />

4.3. *Remaining bias*

As with any historical source, nominative lists of the population census are subject to inherent biases stemming from the data collection itself. Despite administrative efforts to ensure comprehensive coverage, it is likely that some individuals were inadvertently omitted from the records.<fnref n="12" /> Other may have submitted incomplete or inaccurate information.<fnref n="13" /> Public reluctance to the census was already significant at the time—just as it remains today and prompted several poster campaigns aimed at reassuring the population about the confidentiality and intended use of their personal data (see Appendix Figure A1).<fnref n="14" />

Creating a database using artificial intelligence introduces another set of potential biases. Among them are errors produced by machine reading errors, which are likely comparable—if not slightly lower—to those encountered in manual data entry. However, unlike the human brain, which can intuitively interpret abbreviations or contextual cues and convert them into standardized categories, machines lack this level of semantic flexibility. Moreover, the correction of errors in large-scale datasets cannot match the precision achievable in smaller datasets, simply due to the volume and complexity of the data involved.

<footnote n="11">¹¹Next to this final column, enumerators added numerical codes for the purpose of compiling statistics (see Figure 2). These codes appear to correspond to an occupational classification. However, no documentation of the specific coding scheme—distinctly Parisian and divergent from the national nomenclature—has been found in the archives.</footnote>

<footnote n="12">¹²It is particularly true for historical but also contemporary census for young children and young adults (Dupâquier and Dupâquier, 1985; Héran and Toulemon, 2005; Toulemon, 2017). The non-response rate was 3.9% for the 2019 survey and was higher in cities. The introduction of a new census system, unprecedented anywhere in the world, which replaced the exhaustive census of the population with a five-yearly survey, has not improved the situation (Dumont, 2018).</footnote>

<footnote n="13">¹³In rare cases, individuals explicitly refused to return their census forms—instances that are occasionally noted directly on the nominative list—only 24 such refusals have been identified to date in the Parisian census.</footnote>

<footnote n="14">¹⁴In France today, 36% of non-responses are explicit refusals (Dumont, 2018).</footnote>
# 5. Going Further

## 5.1. *Finding Individuals from One Census to the Next*

An additional objective of the POPP project is to link individuals across all three censuses. But tracking individuals from one census to another raises complex methodological questions about which characteristics can reliably establish the uniqueness of an individual (Antonie et al., 2014; Dillon, 2002; Dillon and Roberts, 2002; Ruggles, 2002; Ruggles, Fitch and Roberts, 2018).

These challenges are compounded when working with noisy data produced through digitization processes such as the one used to construct the POPP database. To address this, we have adopted an automated approach based on identifying individuals who share a set of stable characteristics: the greater the number of shared attributes between two records, the higher the probability that they refer the same individual. Given the size of the Paris population—2.9 million inhabitants at the time—we prioritized the development of a search algorithm with limited computational complexity. The identifying variables were selected based on their temporal stability: last name, first name, date of birth, and place of birth. These were chosen specifically to minimize the risk of false matches across census years.

Still, the issue of identifying individuals by their first and last names is complex. In Paris, nominative lists were compiled based on forms filled out by individuals themselves, which minimizes the risk of potential misunderstanding between respondents and enumerators (Dillon, 2002; Dillon and Roberts, 2002). However, names of foreign origin can be more challenging to interpret in writing, especially when they were unfamiliar to the enumerator. Furthermore, although people generally reported the first name they commonly use, they may at other times have provide their maiden name, or vice versa. This inconsistency can result in individuals not being matched across censuses, rendering them untraceable over time. Finally, the recognition of foreign names is also hindered by limitations in the dictionaries provided to the machine, as they are primarily based on the names of people who died in France and lived in the 1970s (see Footnote 9). This is also true for foreign last names.

A more serious bias affecting last names concerns women who changed their names upon marriage. Unfortunately, there is no straightforward method to track women who transition from singlehood or cohabitation to marriage. A woman can only be identified by her maiden name if she remains unmarried, or by her married name if she stays married throughout the period. For those who change marital status—a population of particular interest—the probability of not matching these women across censuses is very high. These women are thus classified as “not found” not because of an error in the OCR or migration, but because of their last name change, which significantly compromises the analysis. In fact, even if it is
possible to track women whose marital status remains stable, the inability to quantify their
share in the population limits their analytical value.

The linking procedure relied on calculating the Levenshtein distance (Wagner and Fischer,
1974) across four characteristics. In order of their importance, these are:

1. **Places of birth.** We considered both départements and countries of birth. For individuals with only one place of birth recorded, we assigned either a département (if the city was in France) or a country (if abroad). The score is binary: 1 if the place of birth matches, 0 otherwise. Since the names of départements and countries were standardized, no tolerance for typographical variation was required.

2. **Years of birth.** The year had to match within one digit (i.e., an error tolerance of 1 digit out of 4, or 0.25). The score was calculated in proportion to the number of differing digits.

3. **Last names.** The last name had to match with a tolerance of 1 character in 5 (0.2). The score was calculated in proportion to the number of differing characters.

4. **First names.** The same rule as for surnames: a tolerance of 1 character in 5 (0.2). The score was likewise calculated in proportion to the number of differing characters.

The analysis was conducted for each pair of census years, going forward: between 1926 and 1931, then between 1931 and 1936, then between 1926 and 1936 and finally for the three census.

Linking 1926 to 1931, 1,175,015 individuals were matched, representing 42% of the 1926 population (47% men and 53% women). Next, linking 1931 to 1936, 1,142,517 individuals were matched, also representing 42% of the 1931 population (48% men and 52% women). Moreover, linking 1926 to 1936, 980,588 individuals were matched, representing 36% of the 1926 population (47% men and 53% women) (Table 1). Finally, linking all three censuses, 771,822 individuals were matched, accounting for 28.3% of the population in 1926 (Table 7).

These data indicate that at least 42% of the Parisian population remained in Paris five
years later, and 28% ten years later. However, they do not support the conclusion that just
under 60% of the population left Paris within five years, as we cannot distinguish between
individuals who are untraceable due recognition issues and those who are genuinely absent.
It is also important to remind that there are gaps in the nominative lists of 1931 and 1936,
so that some individuals who were in fact present may not have been identified.

However, the proportion of individuals identified between the two censuses remains con-
sistent at 42%, as does the distribution by gender. Moreover, women are less likely to be
<table><caption>Table 7. Number of individuals linked across censuses</caption><thead><tr><th>Censuses</th><th>Links (n)</th><th>Linking rate (%)</th><th>Men (% of links)</th></tr></thead><tbody><tr><td>1926–1931</td><td>1,175,015</td><td>41.8</td><td>47</td></tr><tr><td>1931–1936</td><td>1,142,517</td><td>42.3</td><td>48</td></tr><tr><td>1926–1936</td><td>980,588</td><td>36.1</td><td>48</td></tr><tr><td>1926–1931–1936</td><td>771,882</td><td>28.3</td><td>48</td></tr></tbody></table>

Source: POPP database.

Notes: the total population comprises the usual population and the population counted separately, excluding absent individuals. The proportion of men in the general population is 46%.

linked than men: although they make up 54–5% of the total population (see Table 4), they account for only 52% of linked individuals. This discrepancy is mainly due to changes in last name associated with marriage.

The method used so far has prioritized minimizing false links. However, this conservative approach likely resulted in some false links (Bailey et al., 2020). While these initial results are promising, ongoing efforts aim to improve them further—particularly by incorporating household-level characteristics to increase the number of links (Darroch, 2002).

## 5.2. Linking POPP to Other Databases

In the longer term, the POPP database will be linked to the M-POPP and N-POPP databases, which are currently under development as part of the EXO-POPP project led by Sandra Brée.<fnref n="15" /> These complementary databases are based on marriage (M-POPP) and birth (N-POPP) certificates from Paris and its suburbs between 1870 and 1940. Once completed, individuals who appear in the Parisian censuses of 1926, 1931 and 1936 will be matched with their birth and marriage certificates following standard approaches used in other contexts (Bailey et al., 2023; Boudjaaba, Gourdon and Rathier, 2010; Garrett and Reid, 2015; Reid, Davies and Garrett, 2002). Linking these databases holds significant potential for research on migration in and out of Paris. They will also make it possible to reconstruct individual and family trajectories—particularly to explore whether families left Paris upon having children.

We will also link the POPP database to the nominative census records of the Socface database (Boillet et al., 2024), which will bring together all nominative lists for all French communes from 1836 to 1936—except for Paris. This linkage will be particularly valuable because 70% of the Parisian population was not born in Paris, and 50% was born in another département, so that this linking will enable more precise tracking of migration flows into the

<footnote n="15">¹⁵For more information on the EXO-POPP Project, see https://exopopp.hypotheses.org.</footnote>
capital. It will also be valuable for tracing outward migration, as many individuals resided
in Paris only for a brief period of their lives.

5.3. Geographic Information System

Finally, the POPP database will be integrated into a Geographic Information System (GIS), in collaboration with the Paris-Time Machine team.<fnref n="16" /> The GIS will allow for the precise geolocation of every building in every street in Paris. As a result, individuals will thus be located at their exact addresses, enabling detailed spatial analyses at a very fine scale.

6. Data Availability

The POPP database will be openly available on the Progedo platform at the end of 2026 at the DOI: 10.13144/lil-1719.

It has also been transferred to the Archives of Paris to enable name-based searches by
census images that have not yet been indexed.<fnref n="17" /> This new feature opens up a host of
possibilities for both researchers and genealogists—it will be introduced and made available
in October 2025, thanks to collaboration with the Paris archives. This new tool will, in fact,
be offered at the time of the opening of the exhibition “People of Paris (1926–1936)” created
around the POPP base at the Carnavalet Museum - Histoire de Paris (from October 8, 2025
to February 8, 2026).<fnref n="18" />

7. Conclusion

Historical demographers have long embraced advances in information technology—not
only for statistical analysis, but also to construct larger databases more efficiently. The
development of the POPP database illustrate how recent developments in machine learning
technologies are opening up important possibilities for collecting historical data at an un-
precedented scale and level of detail. The POPP database, for instance, contains about 9
million lines, each with more than 30 variables.

The POPP project also highlights the limitations of machine learning technologies for
large-scale historical data. While AI-generated databases share many of the same biases as
those created through more traditional methods, they also introduce specific issues. Indeed,
as with manual methods, machines can misread or misclassify words, leading to errors.

<footnote n="16">¹⁶For more details on the Paris-Time Machine, see https://ptm.huma-num.fr/.</footnote>

<footnote n="17">¹⁷This feature is available at https://archives.paris.fr/s/11/denombrements-de-population.</footnote>

<footnote n="18">¹⁸For more details, see https://www.carnavalet.paris.fr/expositions/les-gens-de-paris-1926-1936.</footnote>
Similarly, the difficulties associated with the managing and analyzing large-scale datasets are not unique to AI-based approaches and are well-documented in other projects (Ruggles, 2002; Ruggles, Fitch and Roberts, 2018; Bailey et al., 2020; Bailey et al., 2023). Of course, corrections and census linking cannot be carried out manually and must be automated. It is clear that a small database with a few hundred or thousand individuals can be corrected or linked much more finely than a database with several million individuals. One of the advantages of creating databases using AI is that it saves time, so it is neither possible nor desirable to correct them manually. However, it is possible and desirable to do so for certain populations if specific biases are identified—for example, in the case of a lack of gender matching of individuals of foreign origin, and in particular men aged 20–29 in the case of the POPP database.

Taking all these factors into account, the resulting database contains at most 3% of errors or missing data. As measurement error remains relatively limited, the resulting dataset offers significant research opportunities to improve our knowledge of the Parisian population during the interwar period. More broadly, the specific case of the creation of the POPP database demonstrates the value of creating historical demographic databases using AI.

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Appendix: Supplementary Figures and Tables

<figure><img src="image_8.png" /><figcaption>Figure A1. Notice on the 1926 census</figcaption></figure>

Translation of the *Nota*: “The answers that each resident gives, in all sincerity, to the questions asked can in no way and at no time cause them any trouble or harm whatsoever. The information collected will be used solely for the preparation of statistical tables and for no other purpose.”

<table><caption>Table A1. Error and recognition rates across column (%)</caption><thead><tr><th rowspan="2">Column</th><th colspan="2">Character</th><th colspan="2">Word</th></tr><tr><th>ER</th><th>RR</th><th>ER</th><th>RR</th></tr></thead><tbody><tr><td>Last names</td><td>4.47</td><td>95.53</td><td>14.23</td><td>85.77</td></tr><tr><td>First names</td><td>2.25</td><td>97.75</td><td>11.43</td><td>88.57</td></tr><tr><td>Years of birth</td><td>2.31</td><td>97.69</td><td>4.53</td><td>95.47</td></tr><tr><td>Places of births</td><td>11.07</td><td>88.93</td><td>20.29</td><td>79.71</td></tr><tr><td>Nationalities</td><td>2.28</td><td>97.72</td><td>1.42</td><td>98.58</td></tr><tr><td>Marital status</td><td>10.67</td><td>89.33</td><td>7.08</td><td>92.92</td></tr><tr><td>Level of education</td><td>4.51</td><td>93.11</td><td>2.91</td><td>97.09</td></tr><tr><td>Relationship to the household head</td><td>6.89</td><td>95.49</td><td>8.26</td><td>91.74</td></tr><tr><td>Occupations</td><td>6.23</td><td>93.77</td><td>16.76</td><td>83.24</td></tr></tbody></table>

Source: POPP database. Pooled censuses. ER: error rate; RR: recognition rate.
<table><caption>Table A2. Most frequent last names (%)</caption><thead><tr><th>Last name</th><th>1926</th><th>1931</th><th>1936</th></tr></thead><tbody><tr><td>Martin</td><td>0.35</td><td>0.30</td><td>0.30</td></tr><tr><td>Petit</td><td>0.18</td><td>0.18</td><td>0.18</td></tr><tr><td>Bernard</td><td>0.17</td><td>0.16</td><td>0.16</td></tr><tr><td>Moreau</td><td>0.16</td><td>0.16</td><td>0.15</td></tr><tr><td>Thomas*</td><td>0.15</td><td>0.15</td><td>0.15</td></tr><tr><td>Missing family names</td><td>0.14</td><td>0.33</td><td>0.39</td></tr></tbody></table>

Source: POPP database.

Notes: the total population comprises the usual population and the population counted separately, excluding absent individuals. *In 1926, Dubois is the fifth most frequent last name with 0.16%.
<table><caption>Table A3. Most frequent first names by gender (%)</caption><thead><tr><th rowspan="2"></th><th rowspan="2">First name</th><th colspan="2">1926</th><th colspan="2">1931</th><th colspan="2">1936</th></tr><tr><th>Share</th><th>Share men</th><th>Share</th><th>Share men</th><th>Share</th><th>Share men</th></tr></thead><tbody><tr><td rowspan="5">First names identified as male</td><td>Jean</td><td>6.78</td><td>99.94</td><td>7.14</td><td>99.94</td><td>7.56</td><td>99.94</td></tr><tr><td>Louis</td><td>5.08</td><td>99.92</td><td>4.67</td><td>99.92</td><td>4.24</td><td>99.92</td></tr><tr><td>Georges</td><td>4.18</td><td>99.86</td><td>4.08</td><td>99.86</td><td>4.02</td><td>99.86</td></tr><tr><td>Henri</td><td>4.16</td><td>99.93</td><td>3.94</td><td>99.93</td><td>3.90</td><td>99.93</td></tr><tr><td>Pierre</td><td>4.12</td><td>99.88</td><td>4.23</td><td>99.88</td><td>4.42</td><td>99.88</td></tr><tr><td>Total male (n)</td><td></td><td colspan="2">1,169,909</td><td colspan="2">1,173,474</td><td colspan="2">1,155,789</td></tr><tr><td rowspan="5">First names identified as female</td><td>Marie</td><td>13.59</td><td>1.42</td><td>12.49</td><td>1.42</td><td>11.83</td><td>1.42</td></tr><tr><td>Jeanne</td><td>5.67</td><td>0.06</td><td>5.48</td><td>0.06</td><td>5.31</td><td>0.06</td></tr><tr><td>Louise</td><td>4.04</td><td>0.07</td><td>3.61</td><td>0.07</td><td>3.25</td><td>0.07</td></tr><tr><td>Marguerite</td><td>3.39</td><td>0.09</td><td>3.29</td><td>0.09</td><td>3.20</td><td>0.09</td></tr><tr><td>Suzanne</td><td>2.65</td><td>0.07</td><td>2.70</td><td>0.07</td><td>2.77</td><td>0.07</td></tr><tr><td>Total female (n)</td><td></td><td colspan="2">1,417,486</td><td colspan="2">1,410,668</td><td colspan="2">1,400,468</td></tr><tr><td rowspan="5">Undefined gender</td><td>Camille</td><td>60.70</td><td>46.64</td><td>57.32</td><td>46.64</td><td>57.08</td><td>46.64</td></tr><tr><td>Dominique</td><td>6.74</td><td>67.26</td><td>7.01</td><td>67.26</td><td>7.74</td><td>67.26</td></tr><tr><td>Alix</td><td>3.06</td><td>29.85</td><td>2.81</td><td>29.85</td><td>3.0</td><td>29.85</td></tr><tr><td>Modeste</td><td>1.45</td><td>60.44</td><td>1.24</td><td>60.44</td><td>1.28</td><td>60.44</td></tr><tr><td>Irénée</td><td>1.11</td><td>70.60</td><td>1.22</td><td>70.60</td><td>1.16</td><td>70.60</td></tr><tr><td>Total undefined gender (n)</td><td></td><td colspan="2">18,567</td><td colspan="2">17,649</td><td colspan="2">15,992</td></tr><tr><td>No first name (n)</td><td></td><td colspan="2">10,507</td><td colspan="2">13,345</td><td colspan="2">18,575</td></tr><tr><td>First name not found* (n)</td><td></td><td colspan="2">71,901</td><td colspan="2">84,935</td><td colspan="2">77,670</td></tr><tr><td>Total (n)</td><td></td><td colspan="2">2,688,370</td><td colspan="2">2,700,071</td><td colspan="2">2,668,494</td></tr></tbody></table>

Sources: POPP database, INSEE *Base des prénoms* database.

Notes: the total population comprises the usual population and the population counted separately, excluding absent individuals. *Share*: share of the population with a given first name; *Share men*: share of men among the population with a given first name in the INSEE *Base des prénoms* database. A high or a low proportion of male individuals unambiguously identified a first name as a “male” or a “female” first name. *First name not found* relates to first names not found in the INSEE *Base des prénoms* database. In 1936, André is the third most frequent first name among men with 4.26%.
<table><caption>Table A4. Distribution of place of birth by gender (%, 1926 census)</caption><thead><tr><th>Place of birth</th><th>Men</th><th>Women</th><th>Undeclared first name</th><th>First name not found*</th><th>Unclear gender</th><th>Total</th></tr></thead><tbody><tr><td>Seine département (incl. Paris)</td><td>37.07</td><td>36.02</td><td>24.79</td><td>14.68</td><td>27.43</td><td>35.8</td></tr><tr><td>France (mainland, excl. Seine)</td><td>47.37</td><td>52.14</td><td>35.8</td><td>30.42</td><td>50.54</td><td>49.41</td></tr><tr><td>Other country</td><td>11.33</td><td>7.70</td><td>11.88</td><td>47.57</td><td>16.05</td><td>10.42</td></tr><tr><td><em>Total non-missing</em></td><td>95.77</td><td>95.86</td><td>72.47</td><td>92.67</td><td>94.02</td><td>95.63</td></tr><tr><td>Undefined</td><td>3.43</td><td>3.33</td><td>6.35</td><td>5.56</td><td>4.67</td><td>3.46</td></tr><tr><td>Undeclared</td><td>0.80</td><td>0.81</td><td>21.19</td><td>1.77</td><td>1.31</td><td>0.91</td></tr><tr><td><em>Total missing</em></td><td>4.23</td><td>4.14</td><td>27.54</td><td>7.33</td><td>5.98</td><td>4.37</td></tr><tr><td><em>Total</em></td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td></tr><tr><td>Total (n)</td><td>1,169,909</td><td>1,417,486</td><td>10,507</td><td>71,901</td><td>18,567</td><td>2,688,370</td></tr></tbody></table>

Source: POPP database, 1926 census.

Notes: the total population comprises the usual population and the population counted separately, excluding absent individuals. *First name not found relates to first names not found in the INSEE *Base des prénoms* database.

<table><caption>Table A5. Age groups: comparison with official statistics (%, 1926 census)</caption><thead><tr><th rowspan="2">Age group</th><th colspan="2">Official statistics</th><th colspan="2">POPP database</th></tr><tr><th>Men</th><th>Women</th><th>Men</th><th>Women</th></tr></thead><tbody><tr><td>0-9</td><td>10.05</td><td>8.38</td><td>10.52</td><td>8.42</td></tr><tr><td>10-19</td><td>12.94</td><td>11.81</td><td>13.55</td><td>11.89</td></tr><tr><td>20-39</td><td>41.63</td><td>41.38</td><td>40.67</td><td>41.08</td></tr><tr><td>40-59</td><td>27.59</td><td>27.26</td><td>27.34</td><td>27.41</td></tr><tr><td>60+</td><td>7.79</td><td>11.16</td><td>7.92</td><td>11.20</td></tr><tr><td>Total</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td></tr></tbody></table>

Source: POPP database, 1926 census and *Résultats statistiques du recensement de la population de 1926*.

Notes: the total population comprises the usual population and the population counted separately, excluding absent individuals.
<table><caption>Table A6. Places of birth: comparison with official statistics (%, 1926 census)</caption><thead><tr><th rowspan="2">Place of birth</th><th colspan="2">Official statistics</th><th colspan="2">POPP database</th></tr><tr><th>Men</th><th>Women</th><th>Men</th><th>Women</th></tr></thead><tbody><tr><td>Seine département (incl. Paris)</td><td>36.91</td><td>35.40</td><td>37.07</td><td>36.02</td></tr><tr><td>France (mainland, excl. Seine)</td><td>46.86</td><td>53.12</td><td>47.37</td><td>52.14</td></tr><tr><td>Other country</td><td>13.33</td><td>8.30</td><td>11.33</td><td>7.70</td></tr><tr><td>Total non missing</td><td>97.10</td><td>96.83</td><td>95.77</td><td>95.86</td></tr><tr><td>Total missing</td><td>2.90</td><td>3.17</td><td>4.23</td><td>4.14</td></tr><tr><td>Total</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td></tr></tbody></table>

Source: POPP database, 1926 census and *Résultats statistiques du recensement de la population de 1926*.

Notes: the total population comprises the usual population and the population counted separately, excluding absent individuals.

<table><caption>Table A7. Marital statuses: comparison with official statistics (%, 1926 census)</caption><thead><tr><th rowspan="3"></th><th colspan="6">Official statistics</th><th colspan="6">POPP database</th></tr><tr><th colspan="3">Men</th><th colspan="3">Women</th><th colspan="3">Men</th><th colspan="3">Women</th></tr><tr><th>1926</th><th>1931</th><th>1936</th><th>1926</th><th>1931</th><th>1936</th><th>1926</th><th>1931</th><th>1936</th><th>1926</th><th>1931</th><th>1936</th></tr></thead><tbody><tr><th>Married</th><td>61.3</td><td>61.2</td><td>64.4</td><td>48.8</td><td>49.6</td><td>51.1</td><td>60.5</td><td>61.0</td><td>63.0</td><td>48.7</td><td>49.8</td><td>50.3</td></tr><tr><th>Divorced</th><td>1.6</td><td>1.7</td><td>2.0</td><td>2.6</td><td>2.9</td><td>3.4</td><td>1.3</td><td>1.7</td><td>2.0</td><td>2.2</td><td>2.9</td><td>3.3</td></tr><tr><th>Widowed</th><td>4.2</td><td>4.1</td><td>4.2</td><td>16.6</td><td>16.4</td><td>16.8</td><td>3.8</td><td>3.9</td><td>4.1</td><td>14.7</td><td>15.3</td><td>15.8</td></tr><tr><th>Never married</th><td>32.9</td><td>32.9</td><td>29.3</td><td>32.0</td><td>31.0</td><td>28.7</td><td>34.3</td><td>33.3</td><td>30.9</td><td>34.3</td><td>32.1</td><td>30.6</td></tr><tr><th>Total</th><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td><td>100.00</td></tr></tbody></table>

Source: POPP database, 1926 census and *Résultats statistiques du recensement de la population de 1926*.

Notes: the total population comprises the usual population and the population counted separately, excluding absent individuals.