Documentation

  1. Introduction

The purpose of this project is to collect and compile the macro-level historical criminal justice statistics from Denmark, Finland, Norway and Sweden and to provide them for use in comparative criminological research. This documentation includes basic information of the content and usage of the provided data.

  1. Content and structure

The dataset consists of four separate excel files, each containing the statistics of a single country. Each file is divided in four primary categories titled “Convictions”, “Police”, “Sentences” and “Prison”.

The Conviction table contains the number of criminal convictions for offences. The main table is condensed based on the legislative analysis of the penal code. I have attempted to find a balance between the longevity and the detailedness of the statistics. In many cases I have had to present individual offenses as a part of a larger category of offenses. This has been caused by the choices made by the statistical authorities of the period or changes in legislation.

To provide the data in as complete form as possible, I have provided additional tables which only include convictions based on a single criminal code. These tables are marked with a C followed by the year of the enactment of the criminal code in question. These supplementary tables include the maximum level of detail, within reason.

Example: Finland.xlsx includes tables C1734 and C1889. C1734 includes convictions during the validity of the Swedish Code of 1734 and C1889 convictions during the validity of the Finnish Criminal Code of 1889.

The conviction statistics are divided by the corresponding criminal code paragraph number. In the primary table, the paragraph shown depicts the paragraph in the currently valid criminal code. Of course, these paragraphs were not valid a hundred years ago. The idea is that if such an offense were to be convicted today, it would most likely be sentenced based on the named paragraph. The supplementary tables are, when possible, divided by the paragraph numbers in each criminal code. Due to multiple alterations in legislature, there may be up to 4 different paragraph numbers, collected from different years, presented for each offense category.

The Police table contains the statistics of offenses known to the police or other authorities. These datasets are simple and usually far more condensed than conviction statistics. In these cases, most of the compactness is caused by the nature of the original data and not by any deliberate choices made by me. Police statistics are also by far the shortest of the included time series.

The Sentence table contains the statistics of the punishments sentenced for the offenders. The tables are seemingly simple but require more substance expertise to use than conviction or police statistics. Sanctions may have been sentenced as primary or secondary punishments and the table does not usually differentiate between the two options. The user needs to figure out beforehand which penalties were sentenced as primary and secondary punishments. I hope to be able to clarify the situation in the future.

The Prison table contains the prison population and intake statistics. This data will be the most difficult to compare due to the differences in the prison system and the data collection practices. However, the user should be able to deduct the total number of prisoners on December 31st each year in each country and the number of persons admitted to prisons each year. Additional data is provided when available.

The use should be able to deduct the total number of prisoners on December 31st each year in each country and the number of persons admitted to prisons during each year. Additional data is provided when available. I hope to be able to extend the prison statistics in the future.

The Source table contains detailed information about the original sources from which the data was collected.

The Translations table contains indicative English translations of the original language titles in each of the four main tables. Supplementary C-tables are not translated. There are important factors to consider when using the translations. Please refer to section 3.4. of this documentation.

  1. Important considerations when using the data

3.1. Convicted offenses or convicted persons

A major problem in the data is that statistical authorities have sometimes changed the way they have recorded convictions. There are two main ways to do that. Firstly, in net recording the data is based on the number of convicted persons. Secondly, in gross recording the data is based on the number of convicted offenses. Both types are found in the data.

Primarily, the number of convicted offenses is used. However, in Finland most of the original sources are built on the number of convicted persons and this is used throughout the data tables as well. There simply is not sufficient source material to build time series based on the number of offenses. This is unfortunate but unavoidable. In the future I hope to update the data by providing a comparative dataset from Finland including the number of convicted offenses from when such data is available.

3.2. Criminal legislation

There have been several criminal justice reforms during the period of the statistics. It is important to note their effect on offenses and punishments. Legislative changes often cause major shifts in the number of convictions, for example. Attached is a table of major criminal law reforms in the Nordic countries. The first number shows the year of enactment and in parentheses is the year when the reform came into force.

Finland 1734 (1736) 1866 (1870) 1889 (1894) 1991–
Norway 1687 1842 1902 (1905) 2005 (2015)
Sweden 1734 (1736) 1864 (1865) 1962 (1965)
Denmark 1683 1866 1930 (1933)

The latest criminal law reform came into force in Norway in 2015. It has been extremely difficult to incorporate the new statistics into the data. My attempts to combine the older and newer data have failed. The problem is the following: Statistics Norway has provided a retroactive dataset of older offenses recorded in the manner of the current statistics. However, these are not comparable to the official statistics of the same period. The numbers simply do not match and without access to the raw data I am not able to reliably combine the series. This is the reason why Norwegian conviction and police statistics end in the year 2014. I hope I can incorporate the newer data into my tables in the future.

3.3. Where did it go?

The structure of the data is mainly dictated by the statistical authorities who have compiled the statistics two hundred, one hundred, fifty or two years ago. The passing of time means that the statistical categories have undergone many changes. For example, an offense which has been recorded as an individual offense may disappear for twenty years just to reappear with no apparent reason. This means that an offense may not appear in the data even if there have been convictions of it. It is important to not interpret a missing number to mean that there has been no conviction for that offense. Unfortunately, I do not see any way to offer help on the matter. As a rule of thumb, the more minor the offense is, the higher its likelihood of being categorised into some larger group of offenses at some point.

As an attempt to remedy the situation, I have made a trial run in the Finnish conviction table and will show when was the offense’s first and last appearance in the source material. The number 0 indicated that the offense would have been recorded there if there had been convictions. The method has been extremely labour intensive but so far the best method I have been able to come up with. I advise users to be careful when seeing a blank space in the dataset if convictions have previously been recorded for the same offense.

3.4. Vocabulary and translations

The data is provided in the original language along with indicative English translations of the primary tables. The translations are meant to help the user deduct the approximate content of the offense or sentence. Whenever there is a discrepancy between the original and the translation, the original version is always correct. I am not a professional translation nor even particularly fluent in Danish, Norwegian or Swedish. I have done my best to convey the basic concept of each category title but I may have made errors.

The vocabulary used in the data requires mention as well. Especially the older offenses were titled with words that are no longer in use or have lost their original meaning during the past two hundred years. I have not even attempted to begin translating these to modern day English. This is one of the main reasons why the supplementary C-tables are not translated. One simple example of the problem is the Finnish offense called metsänhaaskaus. Literal translation would be the wasting of the forest. The current connotation for the obsolete word metsänhaaskaus has nothing to do with the offense itself as it mostly meant timber theft. Even native Finns would misunderstand the content of the offense by looking at the word.

  1. Citation and contact information

The data is free to use for any purpose, providing the source of the data is appropriately mentioned.

To cite this data, please include the following information:

Vuorela, Miikka: The Historical Criminal Statistics of the Nordic Countries 1810–2019. Version 1.0. https://blogs.helsinki.fi/criminalstatistics/.

If you have any questions or comments you may contact me by email at miikka.vuorela (at) uef.fi.

  1. Acknowledgements

I would like to thank professor Tapio Lappi-Seppälä for his valuable support and tireless efforts to guide me through this data project.

I wish to extend my gratitude to M. Soc. Sc. Tiina Malin for her help in collecting the early Finnish incarceration statistics.

The National Archive of Finland and the University of Aarhus Library have been very helpful in allowing me to gain access to some of the most elusive original sources.

This project has been fully funded by the Institute of Criminology and Legal Policy at the University of Helsinki, Finland.