- Research objective
The main objective of this project has been to collect and compile all available criminal justice statistics from Denmark, Finland, Norway and Sweden, and to provide them for use in the international criminological research community.
The project is divided into four phases with the current publication marking the beginning of the second phase.
Phase I: Data collection and compilation
In the first phase, the historical statistics were collected from the original sources and compiled into a comparative dataset.
Phase II: Online publication
Current phase. The guiding principle for this phase has been to offer the data for use by the international scientific community. This has required the polishing of the data, translations, and necessary documentation. While there is still much work to be done before the data can be considered completed, I have felt it important to publish the data for use rather sooner than later.
Phase III: Print publication
In the third phase, the complete Finnish data is published in print format. Additionally, and perhaps more importantly, a collection of important comparative tables is published alongside it. This print version will include the basic analysis of the included data and more comprehensive explanatory documentation.
Phase IV: Update and maintenance
The fourth phase will consist of the continuing updating process of the data. Further maintenance and polishing duties are carried out as well. The main objective of the fourth phase is to improve the accessibility and usability of the data.
- Content and structure
The dataset consists of four files, each containing the detailed statistics from a single country. Each file includes four primary data tables which are titled “Convictions”, “Police”, “Sentences” and “Prison”.
The Conviction table includes the statistics of convictions of specific offenses. The data is highly condensed based on the legislative analysis of the penal code. I have attempted to find a balance between the extent and the longevity of the statistics. In the ideal situation, I have been able to present the statistics on an individual offense without having to merge multiple offenses into a single category. In many cases, however, I have been forced to connect individual offenses into larger categories by the choices of the original statistics authority or the changing legislation. To mitigate the effect of my alterations, I have also provided additional tables usually divided by criminal codes. These tables are marked with C followed by the year of the enactment of each criminal code. These supplementary tables are built to include maximum detail.
Example: Finland.xlsx includes tables C1734 and C1889. C1734 includes offenses that were convicted during the validity of the Swedish Code of 1734 and C1889 offenses that were convicted 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 includes 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 includes the statistics of the criminal sanctions resulting in the conviction of the offender. While seemingly simple, these tables require somewhat more thematic knowledge to interpret than conviction or police statistics. Sanctions have always been dealt as primary or secondary punishments, and the table does not usually differentiate between the two. This means that a person may have been sentenced to imprisonment and the loss of civic confidence. The user usually needs to know beforehand whether certain penalties were sentenced as primary or secondary sanctions. I hope to be able to provide detailed documentation on the use of each type of sentence at a later phase of the project but at this point the consideration is left as the burden of the user.
The Prison table includes the statistics on prison population and intake to prisons. Of all data, these statistics are the hardest to compare due to differences in data collection and the prison system itself. 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.
The Source table includes detailed information about the original sources of which the statistics were collected.
The Translations table includes 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 4.4. of this documentation.
- Important considerations when using the data
4.1. Convicted offenses or persons
A major problem in the data is the changing way in which convictions are recorded. There are two primary ways of doing this: net and gross. In net recording the data is based on the number of convicted persons and in gross recording the number of convicted offenses. Both types are used in the data due to different choices made by statistical authorities.
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.
4.2. 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. Many times, legislative changes are the cause of 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)|
The latest criminal law reform came into force in Norway in 2015. This has been a very difficult change to incorporate into the data. I have attempted to combine the earlier statistics with the ones collected from the period of the new criminal code but have failed. The problem is that 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 to later incorporate the later statistics to my dataset.
4.3. Where did it go?
Most of the contents of the data is dictated by the statistical authorities who compiled the statistics two hundred, one hundred or fifty years ago. One of the problematic aspects of this is that the statistical categories have been in a constant state of change. An offense which has been recorded as an individual category may disappear for twenty years just to reappear without any apparent reason. Consequently, there are many instances where an offense does not appear in the tables while there still have been convictions for it. Therefore, it is important to NOT interpret a missing number to mean that there have been no convictions that year. Unfortunately, this is a case where I can not be of much help either. The milder the crime, the higher the likelihood of it being stuffed into some larger group of offenses at some point.
In the Finnish Conviction table, I have attempted to show when the offense’s first and last appearance in the original sources is. The number 0 always indicates that during that time there would potentially have been a number if there were convictions. This labour intensive method is so far the best method I have been able to invent. I can only advice users to be very careful when seeing a blank space in the dataset, especially if convictions have previously been logged for the same offense. If I ever come up with a better method to remedy the situation, I will update the data.
4.4. Vocabulary and translations
The data is provided in four languages along with indicative English translations of the primary tables. There are two major considerations when using the translations. Firstly, the translations are meant to merely help the user deduct the approximate content of the offense or sentence. The national legislations do not have official English translations and the original language is always correct when there is an unintended discrepancy between the original and the translation. Secondly, I am not a professional translator nor even particularly fluent in the Scandinavian languages. I have done my best to convey the basic concept of each statistical category title, but it is possible that I 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 would be 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 Finns would not grasp the nature of the crime by looking at the word.
- Citation 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 NAME NAME 1810–2019. Version 1.0. Published online 17.2.2021. https://blogs.helsinki.fi/criminalstatistics/.
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.