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Free Legal Research Resources - United States

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This guide contains selected, free, online United States federal and state legal research materials.

Many lawyers have access to paid databases. Yet, combining paid and free resources, can help them to avoid potentially expensive searches. According to a 2020 Legal Technology Survey Report, nearly 60% of lawyers “say they regularly use free online resources to conduct legal research.”

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For researchers without access to paid databases, the following resources may be essential. Legal research is often more effective when using a local law library. To learn more about law libraries throughout the United States, visit:

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Range of Materials

The Constitution

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Case Validation

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Many states and localities publish some or all of their legislative materials on their website. Consider starting with the website of the state or locality in question. Remember, materials on official government websites, may not be the “official copy.” The National Conference of State Legislators provides a list of State Legislative Websites.

The sources below provide alternatives for hard-to-find state materials.

State Case Law and Court Documents

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For free resources on foreign and international law, see our foreign and international law guide:

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Journal Search & Legal Opinions - Google Scholar

Google Scholar offers access to many legal documents including patents, legal opinions and journals. Use the search box below and select the appropriate options from the dropdown menu at the top left of your screen.

While many journals are only available through paid databases, high-quality, open access journals are increasingly common. The following sources collect freely available journal articles.

Books, Dictionaries, Encyclopedias, Treatises, Dissertations, Etc.

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Researching Cases for Undergraduates

Most courts put their published cases online for free but it’s usually not easy to search across multiple courts or see whether a case has been overruled by later cases, so most attorneys access cases on the paid Lexis or Westlaw databases. 

Undergraduates do not have access to these databases, so your best bets are:

• Google Scholar Select the Cases radio button to search cases from across the U.S., provided by Google.
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All of the databases allow you to:

• Run keyword searches to find relevant cases on your topic.

Retrieve cases by entering the citation. For example, on Google Scholar and Nexis Uni, you can enter either 4 Cal. 5th 903 or 416 P.3d 1 to retrieve Dynamex Operations West v. Superior Court , 4 Cal. 5th 903, 416 P.3d 1 (2018). 

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Headnotes and Citators for Cases on Nexis Uni

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• Headnotes are brief notes at the beginning of a case that summarize each holding in the case and link you the relevant text of the case and similar cases.
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At the top of each case on Nexis Uni, you will find a section labeled LexisNexis Headnotes , that provides short notes that summarize each holding in the case. The headnotes are not part of the case and you should never quote or cite them. However, headnotes are useful tools for finding relevant parts of the case and finding similar cases.

Look for a headnote on a topic relevant to your research and then:

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You can also click Shepardize this document in the right sidebar to find a list of cases that cite your case on any topic.

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Shepardize also warns you if there is a problem with your case by placing a red or yellow symbol to the left of the case name and in the right hand Shepard's sidebar.

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Finding Page Numbers for Cases on Nexis Uni

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Rethinking the field of automatic prediction of court decisions

• Original Research
• Open access
• Published: 25 January 2022
• Volume 31 , pages 195–212, ( 2023 )

Cite this article

You have full access to this open access article

• Masha Medvedeva   ORCID: orcid.org/0000-0002-2972-8447 1 , 2 ,
• Martijn Wieling 1 &
• Michel Vols 2  

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In this paper, we discuss previous research in automatic prediction of court decisions. We define the difference between outcome identification, outcome-based judgement categorisation and outcome forecasting, and review how various studies fall into these categories. We discuss how important it is to understand the legal data that one works with in order to determine which task can be performed. Finally, we reflect on the needs of the legal discipline regarding the analysis of court judgements.

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1 Introduction

Automatic analysis of legal documents is a useful, if not necessary task in contemporary legal practice and research. Of course, data analysis should be conducted in a methodologically sound, transparent and thorough way. These requirements are extra important with regard to legal data. The stakes that legal professionals such as lawyers, judges and other legal decision-makers deal with and the cost of error in this field make it very important that automatic processing and analysis are done well. That means that it is essential to understand how the automated systems used in the analysis work, what legal data exactly is analysed and for what purpose.

The need for established practices and methodology is becoming more urgent with the growing availability of data. In striving for transparency, many national and international courts in Europe adhere to the directive to promote accessibility and re-use of public sector information Footnote 1 and publish their documents online (Marković and Gostojić 2018 ). This is also the case for many other courts around the world. Footnote 2 Digital access to a large amount of published case law provides a unique opportunity to process this data automatically on a large scale using natural language processing (NLP) techniques.

In this paper we review previous work on applying NLP techniques to court decisions, and discuss the methodological issues as well as good practices. While automatic legal analysis is an enormous field which has been around for some time, in this paper we focus solely on the recent development of using machine learning techniques for classifying court decisions. This sub-field has expanded drastically in the past 6 years with papers that attempt to predict decisions of various courts around the world. We subsequently discuss whether it is fair to say that they indeed succeed. Our main finding is that many of the papers under review claiming to predict decisions of the courts using machine learning actually perform one of three different tasks.

In the following section, we define the scope of review we conducted. Next, in Sect.  3 we discuss (our terminology of) different types of tasks within the field of automatic analysis of court decisions and how previous research falls within those categories. We examine the purpose of such research for each task, as well as good practices and potential pitfalls. We then discuss our survey in Sect.  4 . In Sect.  5 we summarise and conclude our work.

2 Scope of the review

We limit our review to the papers that use machine learning techniques and claim to be predicting court decisions. The publication dates range from 2015 to (June) 2021. Footnote 3 We specifically chose these years, as this is when machine learning in this field became popular. If a paper included in our review attempts multiple tasks, we only focus on the experiment(s) that focus on predicting judicial decisions. While our survey is meant to provide an exhaustive overview, we may have inadvertently missed some research in the field.

While we already mentioned that the research in the field is growing, not all courts share (all) their case law online. Furthermore, the majority of available case law is extremely varied in its outcomes, which may make it harder to set up an outcome prediction task. For this reason, research often focuses on a relatively restricted set of courts. In this paper, we surveyed publications that use machine learning approaches and focus on case-law of the US Supreme Court (Sharma et al. 2015 ; Katz et al. 2017 ; Kaufman et al. 2019 ), the French court of Cassation (Şulea et al. 2017b ; Sulea et al. 2017a ), the European Court of Human Rights (Aletras et al. 2016 ; Liu and Chen 2017 ; Chalkidis et al. 2019 ; Kaur and Bozic 2019 ; O’Sullivan and Beel 2019 ; Visentin et al. 2019 ; Chalkidis et al. 2020 ; Condevaux 2020 ; Medvedeva et al. 2020a , b ; Quemy and Wrembel 2020 ; Medvedeva et al. 2021 ), Brazilian courts (Bertalan and Ruiz 2020 ; Lage-Freitas et al. 2019 ), Indian courts (Bhilare et al. 2019 ; Shaikh et al. 2020 ; Malik et al. 2021 ), UK courts (Strickson and De La Iglesia 2020 ), German courts (Waltl et al. 2017 ), the Quebec Rental Tribunal (Salaün et al. 2020 ) (Canada), the Philippine Supreme Court (Virtucio et al. 2018 ), the Thai Supreme Court (Kowsrihawat et al. 2018 ) and the Turkish Constitutional Court (Sert et al. 2021 ). Many of these papers achieve a relatively high performance on their specific task using various machine learning techniques.

The distinction between different tasks in this paper is conditional on the data, but is not contingent on the algorithms used. Consequently, we discuss the following papers from the perspective of which data was used, how it was processed and general performance of the systems using particular data for a particular task. We do not go into detail of the algorithms used for achieving that performance. For the specifics of different systems, we therefore refer the interested reader to the papers at hand. For a more detailed explanation of machine learning classification for legal texts in general, see Medvedeva et al. ( 2020a ) and Dyevre ( 2020 ).

3 Terminology and types of judgement classification

In papers that use machine learning for classifying court decisions, different terms and types of tasks are often used interchangeably. For the field to move forward, we therefore argue for a more strict use of terminology. Consequently, in this paper, we use ‘judgement’ to mean the text of a published judgement. While the word ‘outcome’ is a very general term, for the purposes of distinguishing between different tasks in the legal context, we define outcome as a specific closed class of labels for verdicts (i.e. with a pre-defined limited number of verdicts). For example, in the context of case law concerning the European Convention on Human Rights (ECHR) the outcome will be a violation or a non-violation of a specific human right. Other examples of outcomes are eviction or non-eviction in a housing law context (Vols 2019 ) or the US Supreme Court affirming or reversing a decision of a lower court. We use ‘verdict’ and ‘decision’ as synonyms of ‘outcome’.

In this paper we will distinguish between three types of tasks: outcome identification , outcome-based judgement categorisation , and outcome forecasting . Footnote 4 In simple terms, outcome identification is the task of identifying the verdict in the full text of the published judgements, judgement categorisation is the task of categorising documents based on the outcome, and outcome forecasting is the task of predicting future decisions of a particular court. At present, these task distinctions are not clearly made in the literature, even by ourselves (Medvedeva et al. 2020a ). This is potentially problematic as the different tasks have specific uses, which we will discuss below.

The most likely reason for the ambiguity in terminology is the cross-disciplinary nature of the field, combining law with NLP. When using machine learning in the field of NLP, all three tasks are so-called classification tasks. The most commonly used approach in machine learning, and the one all of the reviewed papers have used, is supervised learning . This means that the system is trained on some input data (e.g., facts extracted from a criminal case) that is connected to the labels (outcomes), for instance whether the case was won by the defendant or the prosecution. During the training phase, the model is presented with input data together with their labels in order to infer patterns characterising the relationship between the two. To evaluate the system after training, the system is provided with similar data ( not used during the training phase), such as other criminal cases, and it then predicts the label for each document. Since the label in each task is the outcome, identifying the purpose of these systems within NLP as ‘predicting court decisions’ is appropriate. However, that meaning does not translate in the same way outside of the NLP domain. Specifically, the word predict in the legal domain suggests that one can forecast a decision (of the judge) that has not been made yet, whereas in NLP predict merely refers to the methodology and terminology of machine learning. The majority of papers on predicting court decisions published today, however, do not attempt to predict decisions of the cases that have not been judged yet. Furthermore, the majority of the work in this interdisciplinary field suggests a benefit for legal professionals, but does not explicitly specify what the models that were introduced can be used for.

To circumvent the use of the ambiguous word predict , we therefore suggest using terminology that better reflects the different tasks, and thereby also differentiates between objectives. In order to distinguish between outcome identification, outcome-based judgement categorisation and outcome forecasting it is important to carefully assess the data used in the experiments conducted.

When discussing different papers, we will also refer to their performance scores. The conventional way of reporting the performance of a classification system is by using accuracy or the F1-score. Accuracy is how many of the labels (in our case, outcomes) were classified (i.e. identified, categorised, or forecasted) correctly. The F1-score is a harmonic mean of precision and recall, where precision is the amount of judgements for which the assigned outcome is correct and recall is the percentage of cases with a specific outcome which are classified (i.e. identified, categorised, or forecasted) correctly by the system.

In the following subsections we will make the definitions of the three tasks more explicit, and then give examples from published research for each task. We also highlight the distinct uses of the different tasks for legal professionals.

3.1 Outcome identification

Outcome identification is defined as the task of identifying the verdict within the full text of the judgement, including (references to) the verdict itself. In principle, a machine learning system is often not necessary for such a task, as keyword search (or using simple regular expressions) might suffice.

Outcome identification falls under the field of information extraction and when not confused with predicting court decisions is often also referred to as outcome extraction (e.g., Petrova et al. 2020 ). Given the growing body of published case law across the world, the automation of this task may be very useful, since many courts publish case law without any structured information (i.e. metadata) available, other than the judgements themselves, and often one may require a database where the judgements are connected to the verdicts in order to conduct research. At present and to our knowledge, most of such work is generally done manually, as a human can do this task with 100% accuracy (by simply reading the case and finding the verdict in it).

Automation of outcome identification allows one to save time when collecting this information. While the task is not necessarily always trivial for a machine and depends on how the verdict is formulated (see, for instance, Vacek and Schilder ( 2017 ), Petrova et al. ( 2020 ) and Tagny-Ngompé et al. ( 2020 )), there is nonetheless an expectation that these automated systems should achieve (almost) perfect performance to justify the automation. However, the approach to outcome identification is highly dependent on the structure of judgements in a particular legal domain or jurisdiction and the language of the case law. As a result, a system that automatically identifies a verdict in a particular set of judgements cannot be applied easily to case law of courts in other legal domains or other jurisdictions.

3.1.1 Research in outcome identification

A total of eight papers that aimed to predict court decisions (see Table  1 ) were performing the outcome identification task. These papers use the text of the final judgements published by the court that contain references to the verdict or the verdict itself.

One of the earliest papers that tried predicting court decisions using the text of the judgement is Aletras et al. ( 2016 ). The authors used a popular machine learning algorithm, a Support Vector Machine (SVM) to predict decisions of the European Court of Human Rights (ECtHR). Their model aimed to predict the court’s decision by extracting the available textual information from relevant sections of the ECtHR judgements and reached an average accuracy of 79% for three separate articles of the ECHR. While the authors did exclude the verdict itself (or the complete section containing the verdict), they still used the remaining text of the judgements, which often still included specific references to the final verdict (e.g., ‘Therefore there is a violation of Article 3’). While their work was positioned as predicting the outcome of court cases, the task they conducted was therefore restricted to outcome identification.

Other studies focusing on the ECtHR included Liu and Chen ( 2017 ), Visentin et al. ( 2019 ), and Quemy and Wrembel ( 2020 ). Since Liu and Chen ( 2017 ) and Visentin et al. ( 2019 ) used the same dataset as Aletras et al. ( 2016 ), they also conducted the task of outcome identification. Liu and Chen ( 2017 ) used similar statistical methods as Aletras et al. ( 2016 ) and achieved an 88% accuracy using an SVM, whereas Visentin et al. ( 2019 ) achieved an accuracy of 79% using an SVM ensemble. Whereas Quemy and Wrembel ( 2020 ) collected a larger dataset for the same court and performed a binary classification task (violation of any article of the ECHR vs. no violation) using neural models, they did not appear to exclude any part of the judgement, thereby restricting their task also to outcome identification (with a concomitant high accuracy of 96% using a range of statistical methods). These studies show that automatic outcome identification to a large extent is possible for the ECtHR. However, from a legal perspective this task is not very useful, as the verdict has already been categorised on the ECtHR website.

The studies on the basis of the ECtHR illustrate two broad categories of papers which aim at predicting court judgements, but instead are outcome identification tasks. The first category consists of studies which were only partially successful in removing the information about (references to) the verdict. Besides the aforementioned studies of Aletras et al. ( 2016 ), Liu and Chen ( 2017 ) and Visentin et al. ( 2019 ), the studies of Şulea et al. ( 2017a , b ) suffer from the same problem. They focus on the French Court of Cassation and reach an accuracy of up to 96%. While they masked the words containing the verdict, various words which were found to be important for the prediction of their model appeared to be closely related to the outcome description. Consequently, they were not completely successful in filtering out the information about the outcome.

The second category consists of studies which do not filter out any information out of the judgement at all (or do not mention filtering out this type of information), such as Quemy and Wrembel ( 2020 ). Virtucio et al. ( 2018 ) are explicit in not filtering out the actual court decision of the Philippine Supreme Court (due to a lack of consistent sectioning in the judgement descriptions) when predicting its judgement. Nevertheless, their accuracy was rather low at only 59%. In addition, there is a number of papers that do not specify any pre-processing steps to remove the information that may contain the verdict. Examples are Lage-Freitas et al. ( 2019 ) who deal with appeal cases of Brazilian courts (with an F1-score of 79%) and Bertalan and Ruiz ( 2020 ) who worked on second-degree murder and corruption cases tried in São Paolo Justice Court (with an F1-score of up to 98%).

3.2 Outcome-based judgement categorisation

Outcome-based judgement categorisation is defined as categorising court judgements based on their outcome by using textual or any other information published with the final judgement, but excluding (references to) the verdict in the judgement. Since the outcomes of such cases are published and no longer need to be ‘predicted’, this task is mainly useful for identifying predictors (facts, arguments, judges, etc) of court decisions within the text of judgements. To avoid the system identifying the outcome within the text of the judgement and in order for it to learn new information any references to the verdict need to be removed.

While an algorithm may perform very well on the categorisation task, the obtained categories are not useful by themselves. As the documents used by the system are only available when the judgements are made and public, the outcome categorisation does not contribute any new information (one can simply extract the verdict from the published judgement). This view is also supported by Bex and Prakken ( 2021 ) who insist that the ability to categorise decisions without explaining why the categorisation was made, does not provide any useful information and may even be misleading. The performance of a machine learning model for judgement categorisation, however, may provide useful information about how informative the characteristic features are. To enable feature extraction, it is important that the system is not a ‘black box’ (such as many of the more recent neural classification models). Therefore, rather than ‘predicting court decisions’ the main objective of the outcome-based judgement categorisation task should be to identify predictors underlying the categorisations.

As we only discuss publications that categorise judgements on the basis of the outcome of the case, we will refer to outcome-based judgement categorisation simply as judgement categorisation.

3.2.1 Research in outcome-based judgement categorisation

Most of the papers in the field categorise judgements. The papers surveyed that involve judgement categorisation can be found in Table 2 . For all fifteen papers, we indicate the paper itself, the court, whether or not the authors provide a method of analysing feature importance (FI) and consequently identify specific predictors of the outcome within the text, and the maximum performance.

Within these studies, two broad categories can be distinguished depending on which type of data they use. On the one hand, most studies use the raw text, explicitly selecting parts of the judgement which does not include (references to) the verdict. On the other hand, there are (fewer) studies which manually annotate data and use that as a basis for the categorisation.

Kowsrihawat et al. ( 2018 ) used the raw text to categorise (with an accuracy of 67%) the documents of the Thai Supreme Court on the basis of the facts of the case and the text related to the legal provisions in the cases such as murder, assault, theft, fraud and defamation using a range of statistical and neural methods. Medvedeva et al. ( 2018 ), Medvedeva et al. ( 2020a ) categorised (with an accuracy of at most 75%) decisions of the ECtHR using only the facts of the case (i.e. a separate section in each ECtHR judgement). Notably, Medvedeva et al. ( 2020a ) identified the top predictors (i.e. sequences of one or more words) for each category, which was possible due to the (support vector machine) approach they used. Strickson and De La Iglesia ( 2020 ) worked on categorising judgements of the UK Supreme Court and compared several systems trained on the raw text of the judgement (without the verdict) and reported an accuracy of 69%, while also presenting the top predictors for each class. Sert et al. ( 2021 ) categorised cases of the Turkish Constitutional Court related to public morality and freedom of expression using a traditional neural multi-layer perceptron approach with an average accuracy of 90%. Similarly to Medvedeva et al. ( 2020a ), Chalkidis et al. ( 2019 ) also investigated the ECtHR using the facts of the case, and proposed several neural methods to improve categorisation performance (up to 82%). They additionally proposed an approach (a hierarchical attention network) to identify which words and facts were most important for the classification of their systems. In their subsequent study Chalkidis et al. ( 2020 ) used a more sophisticated neural categorisation algorithm which was specifically tailored for legal data (LEGAL-BERT). Unfortunately, while their approach did show an improved performance (with an F1-score of 83%) it was not possible to determine the best predictors of the outcome due to the system’s complexity. Medvedeva et al. ( 2021 ) reproduced the algorithms in Chalkidis et al. ( 2019 ) and Chalkidis et al. ( 2020 ) in order to compare their performance for categorisation and forecasting tasks (see below) for a smaller subset of ECtHR cases, and achieved an F1-score of up to 92% for categorising judgements of 2019. The scores however varied throughout the years. For example, categorisation of cases from 2020 did not surpass 62%. Several other categorisation studies (with accuracies ranging between 69 and 88%) focused on the facts of the ECtHR, but likewise did not investigate the best predictors (Kaur and Bozic 2019 ; O’Sullivan and Beel 2019 ; Condevaux 2020 ). Malik et al. ( 2021 ) used neural methods to develop a system that categorised Indian Supreme Court Decisions achieving 77% accuracy. As their main focus was to develop an explainable system, they used an approach which allowed them to investigate the importance of their features, somewhat similar to the approach of Chalkidis et al. ( 2020 ).

Manually annotated data was used by Kaufman et al. ( 2019 ) who focused on data from the US Supreme Court (SCOTUS) Database (Spaeth et al. 2014 ) and achieved an accuracy of 75% using statistical methods (i.e. AdaBoosted decision trees). However, they did not investigate the most informative predictors. Shaikh et al. ( 2020 ) also used manually annotated data to categorise the decisions of murder-cases of the Delhi District Court with an accuracy of up to 92% using classification and regression trees. These authors manually annotated 18 features, including whether the injured is dead or alive, the type of evidence, the number of witnesses et cetera. Importantly, they analysed the impact of each type of feature for each type of outcome.

Finally, Salaün et al. ( 2020 ) essentially combined the two types of predictors, by not only extracting a number of characteristics from the cases of Rental Tribunal of Quebec (including the court location, judge, types of parties, et cetera), but also using the raw text of the facts (as well as the complete text excluding the verdict), achieving a performance of at most 85% with a French BERT model, FlauBERT.

Notably, the performance of Sert et al. ( 2021 ) was very high. Despite the high success rate of their system, however, the authors warn against using it for decision-making. Nevertheless, they do suggest that their system can potentially be used for prioritising the cases that have a higher likelihood to end up in a violation. This suggestion mirrors the proposition made by Aletras et al. ( 2016 ) for potentially using their system to prioritise cases with human rights violations. In both cases, however, the experiments were conducted using data extracted from the final judgements of the court, and the performance of these systems using data compiled before the verdict was reached (i.e. information necessary to prioritise cases) is unknown. Making these types of recommendations is therefore potentially problematic.

Many categorisation papers shown in Table  2 claim to be useful for legal aid. However, as we argued before, categorisation as such is not a useful task, given that the verdict can simply be read in the judgement text. To be useful, it is essential that categorisation performance is supplemented with the most characteristic features (i.e. predictors). Unfortunately, only a minority of studies provides this information. And even if they do, the resulting features, especially when using the raw text (i.e. characteristic words or phrases), may not be particularly meaningful.

In an attempt to be maximally explainable, Collenette et al. ( 2020 ) suggest using Abstract Dialectical Framework instead of machine learning. They apply this framework to deducing the verdict from the text of judgements of the ECtHR regarding Article 6 of the ECHR (the right to a fair trial). The system requires the user to answer a range of questions, and on the basis of the provided answers, the model determines whether there was a violation of the right to a fair trial or not. The questions for the system were derived by legal experts, and legal expertise is also required to answer these questions (Collenette et al. 2020 ). While their system seemed to perform flawlessly when tested on ten cases, we face the same issue as with the machine learning systems. Specifically, the main input data is based on the final decision that has already been made by the judge. For instance, one of the questions that the model requires to be answered is whether the trial was independent and impartial, which is a question that has to be decided on by the judge. While this type of tool may potentially 1 day be used for judicial support, for example, as a checklist for a judge when making a specific decision, it is unable to actually forecast decisions in advance, or point to external factors that are not identified by legal experts.

3.3 Outcome forecasting

Outcome forecasting is defined as determining the verdict of a court on the basis of textual information about a court case which was available before the verdict was made (public). This textual information can, for instance, be submissions by the parties, or information (including judgements) provided by lower courts to predict the decisions of a higher court, such as the US Supreme Court. Forecasting thereby comes with the essential assumption that the input for the system was not influenced in any way by the final outcome that it forecasts. In contrast to outcome-based judgement categorisation , it is useful to evaluate how well the algorithm is able to predict the outcome of cases. For example, individuals may use such algorithms to evaluate how likely it is that they will win a court case. Similarly to judgement categorisation , determining the factors underlying a well-performing model is useful as well. While identification and categorisation tasks only allow one to extract information and analyse already made court decisions, forecasting allows one to predict future decisions that have not been made yet. Note that whether or not a model was trained on older cases than it was evaluated on (e.g., the ‘predicting the future’ experiment conducted by Medvedeva et al. 2020a ) does not affect its classification as a judgement categorisation as opposed to a judgement forecasting task. Only the type of data affects which task it is. Since Medvedeva et al. ( 2020a ) use extracted data from the court judgements, their task is still an outcome-based judgement categorisation task.

3.3.1 Research in outcome forecasting

Table  3 lists the papers that focus on forecasting court verdicts. While many publications focus on ‘predicting court decisions’, only five papers satisfy our criteria for outcome forecasting. We can observe that the performance of these studies is lower than for the categorisation and identification tasks. This is not surprising as forecasting can be expected to be a harder task. Given the small number of papers, we discuss each of these in some detail.

The advantage of working with the US Supreme Court databases is that it attracts much attention. Consequently, all data from the trials are always systematically and manually annotated by legal experts with many variables immediately after the case was tried. Sharma et al. ( 2015 ) and Katz et al. ( 2017 ) both use variables available to the public once the case was moved to the Supreme Court, but before the decision was made to forecast decisions of SCOTUS. Sharma et al. ( 2015 ) use neural methods, whereas Katz et al. ( 2017 ) use the more traditional technique of random forests. Both approaches resulted in forecasting 70% of the outcomes correctly, which was a small improvement over the 68% baseline accuracy where the petitioner always wins (suggested by Kaufman et al. 2019 ). Moreover, Sharma et al. ( 2015 ) present the importance of various variables in their model, therefore potentially enabling a more thorough legal analysis of the data. The variables used in both studies contained information about the courts and the proceedings but hardly any variables pertaining to the facts of the case.

Waltl et al. ( 2017 ) attempted to forecast decisions of the German appeal court in the matters of Tax Law (Federal Fiscal Court). The authors used the documents and meta-data of the case (e.g., year of dispute, court, chamber, duration of the case, et cetera) from the court of first instance. They extracted keywords from the facts and (lower) court reasoning to forecast decisions. They tried a range of methods, but selected the best-performing naive Bayes classifier as their final model. Their relatively low F1-score of 0.57 indicates that it may have been a rather difficult task, however.

Medvedeva et al. ( 2020b ) used raw text and facts within documents that were published by the ECtHR (sometimes years) before the final judgement. These documents are known as ‘communicated cases’. Specifically they used the facts as presented by the applicant and then communicated by the Court to the State as a potential violator of the human rights. The communicated cases reflect the side of the potential victim, and are only communicated when no similar cases have been processed by the court before. Consequently, these documents include a very diverse set of facts, and different issues (although all within the scope of the European Convention on Human Rights) are covered in them. Medvedeva et al. ( 2020b ) reported an accuracy of 75% using SVMs on their dataset (the model is re-trained and run again every month). This system is integrated in an online platform that also highlights the sentences or facts within the text of these (communicated) cases that are most important for the model’s decision. Footnote 5 Medvedeva et al. ( 2021 ) used a slightly different dataset of the same documents (i.e. only cases with the judgement in English were included, but the dataset was expanded by adding cases that resulted in inadmissibility based on merit) and retrained the model per year (as opposed to per month in Medvedeva et al. ( 2020b ). The authors compared how the state-of-the-art algorithms for this court, BERT (Chalkidis et al. 2019 ), LegalBERT (Chalkidis et al. 2020 ), and SVMs (Medvedeva et al. 2020a , b ) perform on data available before the final judgement and with the final judgement. The results showed that forecasting is indeed a much harder task, as the models achieved a maximum F1-score of 66% as opposed to 92% for categorisation of the same cases.

4 Discussion

It is clear that ‘predicting court decisions’ is not an unambiguous task. There is therefore a clear need to carefully identify the objective of the experiments before conducting them. We believe such an objective has to be rooted within the specific needs of the legal community to prevent systems being developed of which the authors believe them to be useful, whereas they do not have any meaningful application in the legal field at all. The purpose of our paper was to provide some terminology which may be helpful for this.

While researchers may believe they are ‘predicting court decisions’, very infrequently this involves actually being able to predict the outcome of future judgements. In fact, predicting court decisions sometimes (likely inadvertently, due to sub-optimal filtering or insufficient knowledge about the exact dataset) ended up not being anything other than identifying the outcome from the judgement text. While sophisticated approaches were often put forward in those cases, a simple keyword search might already have resulted in a higher performance for this identification task. Most often, however, predicting court decisions was found to be equal to the task of categorising the judgements according to the verdicts. This is not so surprising given the available legal datasets, which more often contain complete judgements than documents which were produced before the verdict was known.

In sum, to identify the exact task, and the concomitant goals which are useful from a legal perspective, it is essential that researchers are well aware of the type of data they are analysing. Unfortunately, this is frequently not the case. For example, several researchers (Chalkidis et al. 2019 ; Quemy and Wrembel 2020 ; Condevaux 2020 ) have recently started to develop (multilabel classification) systems, which are able to predict which articles were invoked in an ECtHR case. However, this task is not relevant from a legal perspective, as articles which are potentially violated have to be specified when petitioning the ECtHR.

Therefore when creating a new application, for instance, using data from another court, one should clearly determine the goal of such a system first, and then review whether the data for the established task is available. Specifically, one needs full judgements for the outcome identification task. In case of a judgement categorisation task, full judgements from which the outcomes can be removed are necessary. If the system needs to perform a forecasting task, it requires data available before the judgement is made.

For all of the above tasks, explainability (i.e. being able to determine the importance of various features when determing the model’s outcome) helps to better analyse the performance and gain insight into the workings of the system. However, explainability is essential for judgement categorisation, as this task is reliant on the ability to investigate which features are related to the outcome.

As we mentioned before, the identification task does not always require the use of machine learning techniques. This task can often be solved with a keyword search which does not require any annotated data. Using machine learning is necessary when the judgement text is not very structured, and when more complex descriptions of the outcome need to be extracted. For both the judgement categorisation task and the forecasting task, statistics may be useful to assess the relation between predetermined factors and the outcome, whereas for categorisation task machine learning techniques allow for discovering new patterns and factors within the judgements that may have not been considered previously. Similarly, machine learning can be used to forecast future court decisions by training the system on the decision that the court has made in the past. To illustrate these three tasks, their goals and requirements, a flow-chart is shown in Fig.  1 .

Flowchart illustrating the goals and requirements for the three court decision prediction tasks

Finally, we would like to emphasise that while the approaches discussed in this paper can be suitably used in legal analysis, and for example to try to understand past court decisions, none of the systems capable of solving any of the discussed tasks are appropriate for making court decisions. Judicial decision-making requires (among others) knowledge about the law, knowledge about our ever-changing world, and arguments to be weighed. This is very different from the (sometimes very sophisticated) pattern-matching capabilities of the systems discussed in this paper.

5 Conclusion

In this paper, we have proposed several definitions for analysing court decisions using computational techniques. Specifically, we discussed the difference between forecasting decisions, categorising judgements according to the verdict and identifying the outcome based on the text of the judgement. We also highlighted the specific potential goals associated with each of these tasks and illustrated that each task is strongly dependent on the type of data used.

The availability of enormous amounts of legal (textual) data in combination with the legal discipline being relatively methodologically conservative (Vols 2021 ) has enabled researchers from various other fields to attempt to analyse these data. However, to conduct meaningful tasks, we argue for more interdisciplinary collaborations, not only involving technically skilled researchers, but also legal scholars to ensure meaningful legal questions are answered, and this new and interesting field is propelled forwards.

https://digital-strategy.ec.europa.eu/en/policies/legislation-open-data , accessed on 11/10/2021.

See, for instance, case law of the Constitutional Court of South Africa available at: https://collections.concourt.org.za .

For description of earlier approaches in automatic prediction of court decision with and without using machine learning we refer to Ashley and Brüninghaus ( 2009 )

In principle, there are three additional tasks, namely charge identification , charge-based judgement categorisation and charge forecasting . These tasks involve determining the specific sentence or charge. For example, the number of years someone was sentenced to go to prison in criminal court proceedings. These tasks have most often been investigated for various courts in China (Luo et al. 2017 ; Ye et al. 2018 ; Jiang et al. 2018 ; Liu and Chen 2018 ; Zhong et al. 2018a , b ; Li et al. 2019 ; Chen et al. 2019 ; Long et al. 2019 ; Chao et al. 2019 ; Fan et al. 2020 ; Cheng et al. 2020 ; Tan et al. 2020 ; Huang et al. 2020 ). The distinction we make between identification, categorisation and forecasting (and the pitfalls and suggestions regarding this distinction) in this paper, however, hold for these cases as well.

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Medvedeva, M., Wieling, M. & Vols, M. Rethinking the field of automatic prediction of court decisions. Artif Intell Law 31 , 195–212 (2023). https://doi.org/10.1007/s10506-021-09306-3

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A model for predicting court decisions on child custody

José félix muñoz soro.

1 Aragonese Foundation for Research & Development (ARAID), Zaragoza, Spain

Carlos Serrano-Cinca

2 Faculty of Economy and Business, University of Zaragoza, Zaragoza, Spain

Associated Data

Our database is available at GitHub: https://github.com/labje/bidaraciv/blob/main/Predict-Court-Decision_Data.xlsx The data were taken from the Spanish Judicial Authority Documentation Center (CENDOJ), a body that compiles and disseminates the jurisprudence of the Supreme Court and other Spanish courts. This database is freely accessible to the public. https://www.poderjudicial.es/portal/site/cgpj/menuitem.87fc234e64fd592b3305d5a7dc432ea0/?anio=2020&vgnextoid=d9df2bddeb0b6510VgnVCM1000006f48ac0aRCRD&trimestre=4&vgnextlocale=en&vgnextfmt=formato2&comunidad=11&lang_choosen=en .

Awarding joint or sole custody is of crucial importance for the lives of both the child and the parents. This paper first models the factors explaining a court’s decision to grant child custody and later tests the predictive capacity of the proposed model. We conducted an empirical study using data from 1,884 court rulings, identifying and labeling factual elements, legal principles, and other relevant information. We developed a neural network model that includes eight factual findings, such as the relationship between the parents and their economic resources, the child’s opinion, and the psychological report on the type of custody. We performed a temporal validation using cases later in time than those in the training sample for prediction. Our system predicted the court’s decisions with an accuracy exceeding 85%. We obtained easy-to-apply decision rules with the decision tree technique. The paper contributes by identifying the factors that best predict joint custody, which is useful for parents, lawyers, and prosecutors. Parents would do well to know these findings before venturing into a courtroom.

Introduction

The most recent data in the US accounted for 2,015,603 marriages and 746,971 divorces annually [ 1 ], while in Europe, there were 1,950,935 marriages and 834,068 divorces [ 2 ]. After separation or divorce, joint physical custody is increasingly common in many Western societies. This is a parental care arrangement in which a child lives with each parent 25–50% of the time [ 3 ]. Joint custody is likely to be beneficial to children on average, which justifies recommending it [ 4 ], although the economic repercussions are not negligible and some parents fight for custody to avoid paying child support [ 5 ]. However, before engaging in expensive litigation, it would be good for parents to have an idea of how likely it is that they will win the lawsuit. Our paper aims to identify the factual elements that determine a court’s decision to choose joint or sole custody, relate them to the legal principles applied in the judgments, and develop a predictive model capable of forecasting judicial decisions from a set of factual findings.

Our first research question aims to find the factors that explain court rulings on child custody. The best interest of the child principle stands out among the legal principles that influence a court’s decision [ 6 ]. Family systems theory argues that a proper custody decision requires an evaluation of the entire family and its relationships [ 7 ]. Therefore, legal principles concerning parents, such as the principle of equality between parents and proportionality in responsibility, should be relevant [ 8 ]. It is therefore important to know the parents’ relationship and attitude, the parents’ readiness, including the economic resources of both parents, and their previous dedication to childcare. One fact that seems crucial is the parents’ agreement on the type of custody, if any. Among other theories, therapeutic justice justifies support for joint or sole custody depending on what is in the best interest of the child. This theory focuses on the impact of the law on the psychological well-being of individuals, but without privileging therapeutic outcomes over due process or other constitutional and related values [ 9 ]. Thus, it is frequent that the child is asked for his or her opinion, while the child’s circumstances and the child’s background are also considered as factual findings that influence the decision on the type of custody [ 9 ]. Furthermore, the judge can rely on a psychological report [ 10 ]. This research question goes beyond identifying the factual findings and tries to understand the judge’s reasoning and its relationship to the facts. To this end, we developed explanatory models using linear and logistic multivariate regressions.

Already in 1897, Oliver Wendell Holmes, the father of legal realism, claimed that law must be predictive, but one may still wonder to what extent justice is predictable in practice [ 11 ]. Our second research question aims to develop a forecasting model. There is extensive literature on legal judgment prediction, for example, on forecasting criminal sentencing decisions [ 12 ] and on the prediction of decisions made by the Supreme Court of the United States [ 13 – 16 ]. Explanatory factors for child custody decisions were also studied [ 10 , 17 , 18 ], but as far as we know, no predictive models have yet been developed to forecast child custody decisions. We applied logistic regression models as benchmarks and other data mining tools with high predictive capacity, such as neural networks. In this research question, we also aim to obtain decisional rules through decision trees. This technique can be very useful not only in predicting court sentences but also in explaining them [ 15 ].

We conducted an empirical study with 1,884 Spanish court rulings on child custody. Our models predict the court’s decision with an accuracy exceeding 85%. In the case under analysis (second instance appeals), the justice system agrees with the petitioner only 17.8% of the time. Interestingly, the decision tree detected situations with both extremely high and low probabilities of winning at trial. The latter is a sure loss of money for the litigants and a time-consuming process for the overburdened court system. Widespread use of legal decision support systems would help minimize the asymmetry of information, which is so negative for the justice system [ 19 ]. Decision systems such as the one presented in the paper could help alleviate pressure on the justice system, as many parents would avoid going to court and opt for out-of-court settlements.

This study contributes to the literature in many ways. The factors that explain child custody decisions were studied by other authors [ 17 , 18 , 20 ], but we developed a predictive model, which was tested using temporal validation. This means that the test sample comes from a period after the training sample: this is the appropriate validation method to test predictive results over time [ 21 ]. It recalls a real-world situation in which a lawyer estimates the model from the most recent information available and tests the model with new cases. We provide performance measures such as accuracy, sensitivity, and specificity. We provide heuristics in the form of easy-to-apply decision rules, which is a significant practical tool for a lawyer to use to prepare their case for trial. Although court decisions have been analyzed using decision trees [ 15 ], no previous research applied decision trees to the study of child custody to the best of our knowledge. Finally, our paper is not limited to an empirical exercise but identifies the underlying legal principles, deepening the causal reasoning behind a judicial decision.

Literature review

Legal judgment prediction.

Decision-making support systems in the field of justice may take many forms, including computer-assisted legal research [ 22 ], expert systems that explain court decisions based on argumentation mining [ 23 ], systems for predicting crime [ 24 ] and recidivism of juvenile offenders [ 25 ], big data tools to help regulators pass appropriate laws by predicting their outcomes [ 26 ], and systems that predict judicial decisions [ 27 ]. Legal judgment prediction tries to identify factual elements that influenced past court decisions to correctly predict the decisions of new cases for a specified legal problem. As a research field, it has been active since the late 1950s [ 28 ], and today, it has great potential due to advances in the natural language processing of judgments [ 27 ] and data mining techniques applied to court decision forecasting [ 15 ].

Holmes was a pioneer in proposing the predictive theory of law in 1987 [ 11 ]. Since then, there have been numerous attempts to predict legal rulings [ 13 – 16 , 28 – 30 ] and also outcomes, such as crime outcomes for released defendants [ 31 ] and the recidivism of convicts [ 32 ]. The most studied topic is that of predicting U.S. Supreme Court rulings [ 14 – 16 , 33 ].

The predictor variables depend on the chosen theoretical framework. Up to nine judicial decision-making theories were described [ 34 ], the most extreme of which are legalism and attitudinalism. Legalism is often regarded as the ‘official theory’ of judicial behavior: judges make decisions exclusively applying the so-called ‘rule of law’ with intellectual rigor. Hence, advocates of legalism use factual findings as explanatory variables, because the judge bases his decision on them [ 28 ]. On the other side, advocates of the attitudinal theory argue that judges’ decisions are best explained by personal factors such as emotions, opinions, and political preferences. Some empirical studies incorporated the political preferences of the court and the pressures of interest groups as predictor variables [ 30 ]. Other studies derive from psychological theories and even used emotional arousal (measured by a vocal pitch) to predict a jury’s vote [ 13 ]. Katz et al. [ 14 ] used as many as 240 variables, including chronological variables, case history variables, justice-specific variables, and outcome variables. These last authors used data from the United States Supreme Court Database to forecast 240,000 justice votes over nearly two centuries (1816–2015). Accuracy rates ranged from 71.9% [ 14 ] to 75.0% [ 15 , 16 ].

As for the techniques used to predict judicial decisions, the first studies used logistic regression and other multivariate statistical techniques [ 29 ]. More recently, studies have used neural network models [ 35 ] and techniques based on decision trees [ 16 ], including AdaBoosted decision trees [ 15 ] and random forest classifiers [ 14 ]. Ruger et al. [ 16 ] forecasted Supreme Court decisions by comparing two methods: the first was a decision tree model that relied on general case characteristics, while the second was based on a set of predictions made by legal specialists. The statistical model outperformed the experts and was particularly good at forecasting economic activity cases, while the experts did comparatively better in the judicial power cases. It is common to compare the results of the most advanced techniques with those obtained with logistic regression [ 15 ] or to compare several techniques with each other [ 35 ].

Joint custody of a child

The Declaration of the Rights of the Child [ 36 ] marked the point at which children began to be seen as holders of rights independent of those of their parents, giving substance to the principle of the best interests of the child. This principle governs decisions on custody in cases of separation or divorce, providing the solution that is least traumatic for the child and ensuring that the best conditions for its development should always be sought [ 6 ]. However, the principle of the interest of the child is highly indeterminate, and its application depends on multiple factors. Until the 1960s, the ‘tender years’ doctrine predominated in jurisprudence, which considered that there was a biological superiority of the mother over the father, making her preferable for the custody of children [ 37 ]. Gradually, positions in favor of shared custody gained acceptance, arguing that it was the modality that, for the child, most resembled the situation before the separation. Joint custody is now a global trend whose antecedent is gender equality, which can be seen in women’s labor participation and fathers’ involvement in childcare [ 3 ]. Joint custody currently represents between 10% and 30% of arrangements in Western countries, reaching 35% in Sweden [ 38 ] and 38% in Spain [ 39 ].

The effects of joint custody on children and parents have been widely studied [ 40 , 41 ]. A recent meta-analysis on 60 studies found that joint custody had better outcomes according to all measures in 34 studies, equal outcomes on some measures, and better outcomes on other measures in 14 studies, with very few cases having worse outcomes [ 41 ]. However, these studies have a methodological flaw: they do not come from a randomized control trial, because there will never be an instance of judges assigning the custody of children at random [ 4 ]. The expected advantages of shared custody are explained by bonding and monitoring theories [ 42 ]. The former argues that parents allow themselves to grow more attached to their children when they do not fear a complete break with them in case of divorce. Monitoring theories ensure that the parents minimize the problem of agency costs, because they know how their financial contributions are spent and assume their commitments responsibly. The type of custody not only impacts the child and the parents but also affects society as a whole. The latter is because the effects of adopting joint custody may include changes in marriage rates, overall fertility, and divorce rates [ 5 ].

Empirical study

Sample and data.

Table 1 describes the variables used in the present study. The dependent variable is the decision made by the court (DEC_JOINT). This is a dummy variable that takes the value 1 if joint custody was decided and 0 in the case of sole custody. What the plaintiff asked for can be considered another dummy dependent variable (RQ_JOINT). The third dependent variable is also a dummy variable that measures whether or not the plaintiff won the lawsuit (WINNER). The independent variables are the factual elements, which deal with the circumstances of the child, the parents, and their relationships [ 6 , 8 , 10 , 43 ]. Among the child’s circumstances, the opinions and wishes expressed by the child are usually taken into account (CHILD_OPIN), as well as the psychophysical circumstances of the child (CHILD_PSY) and the child’s adjustment or background (CHILD_ROOT). Concerning the parents, we considered their relationship and attitude towards their obligations (PAR_RELAT), their availability, including schedules and financial resources (PAR_RDNS), their dedication during cohabitation (PAR_DED), and the agreements and conventions between parents (PAR_AGREEM). Finally, we considered the content of the psychosocial report, if any (PSY_REP).

We considered four legal principles used to understand the judge’s reasoning and its relationship to the aforementioned factual elements [ 6 , 8 , 10 , 43 ]. The best interest of the child (BEST_INT) stands out, which, depending on the circumstances of the case, can operate in favor of both sole and joint custody [ 6 ]. Other principles include the principle of equality between parents (PAR_EQL) and the principle of proportionality in the assumption of burdens (PROP_RESP), which concerns how each parent should bear the expenses of the children in proportion to their ability to do so [ 8 ]. Finally, the principle of res judicata (RES_JUD) was included, which the judge applies when the intention is to modify previously established conditions without a substantial change in circumstances.

The sex of the plaintiff was included as an independent variable (PLAINTIFF_SEX), because this variable was found to be relevant in child custody decisions [ 44 ]. Currently, in Spain, mothers obtain the majority of sole custody arrangements, obtaining child custody 58% of the time, while men obtain sole custody 4% of the time and shared custody occurs in the other 38% of cases [ 39 ]. The sex of the judge was also included (JUDGE_SEX), because statistically significant differences were found in other contexts [ 45 ]; however, we do not expect them to occur in our study.

We also studied the role of territorial legislation. Spain is divided into seventeen autonomous communities, four of which have adopted their own civil legislation on custody. We created a dummy variable (FAVOR_JOINT) that assigns a 1 to rulings issued by courts in territories that established joint custody as preferential (Aragon, Basque Country and Catalonia) and a 0 to the rest of Spain. In Aragon, Law 2/2010 established joint custody as a preferential modality, but Law 6/2019 eliminated this preference and equated both modalities. In the Basque Country, Law 7/2015 established the preference for joint custody, which is still in force. The Civil Code of Catalonia (Law 25/2010) also established the preference for shared custody, although it was more ambiguous in its pronouncements.

The court sentences were taken from the Spanish Judicial Authority Documentation Center (CENDOJ), a body that compiles and disseminates the jurisprudence of the Supreme Court and other Spanish courts. This database is freely accessible to the public. We downloaded 1,884 child custody rulings from June 2016 to June 2020, of which 1,134 (60.2%) were sole custody and 750 were joint custody (39.8%). They are second instance appeal judgments. That is, after a divorce without an agreement, or when one of the parents requested a change in the pre-existing custody modality, the first verdict issued by a judge was not accepted by one of the parties and was appealed. Fig 1 shows a flow diagram of the divorce procedure and the content of the second instance sentence, which is common in both cases.

Method and procedure

The research team read and labeled the contents of each court sentence, identifying the factual elements and legal principles. Understanding legal language requires expertise in legal matters and two researchers (law graduates) were chosen to label each of the court sentences. This task can be subjective, and the two researchers independently labeled each of the court sentences to minimize bias. Although the criteria were previously agreed upon and the degree of coincidence was high, numerous disagreements arose in the identification of the factual elements and legal principles. Therefore, a third person, the leading researcher, solved the dubious cases. This ensured the quality of the process.

The labeling process was time-consuming and difficult. On average, each court sentence had 2,093 words. On average, we identified 11.46 factual findings and 1.98 legal principles in each sentence. It took about 24 minutes to label each court sentence. Fig 1 also shows the contents of a court sentence on child custody. A court sentence is made up of four parts: (1) the header contains the details of the court, the parties involved, and the professionals who represent and defend them; (2) the factual background explains the factual basis of the decision; (3) the legal grounds contains the legal argument; and (4) the verdict contains the court’s decision. It is important to note that the court may mention some facts alleged by the parties in the factual background, but they may not be taken into account by the court, and hence they must not be labeled. That is, a fact is relevant to the analysis of the argument when it is mentioned in the legal grounds and used as part of the legal argument.

A court sentence may refer to the same factual element several times, sometimes with arguments in favor and sometimes with arguments against. Table 2 shows two examples of phrases for each of the factual elements and legal principles, one favoring joint custody and the other favoring sole custody. Therefore, when labeling each phrase, the meaning was taken into account: if it spoke in favor of joint custody, it was recorded with a positive value, while if it spoke in favor of sole custody, it was recorded with a negative value. Thus, the final value of each variable was obtained by adding up the number of occurrences in favor of joint custody and subtracting the number of occurrences in favor of sole custody. Therefore, the factual findings and legal principles in Table 1 are not dummy variables but quantitative variables.

In italics, the original in Spanish.

Factual elements and legal principals associated with each court decision

Table 3 provides an overview of the summary statistics for each independent variable for the two groups of court sentences (joint and sole custody) according to the judge’s decision (DEC_JOINT). The table reports the mean, standard deviation, and minimum and maximum values for both groups. An independent samples t-test was conducted to determine if the composition of the factual elements and legal principles differed for the two possible decisions of the court (joint and sole). As expected, a court sentence that decides on joint custody includes on average more phrases that refer to factual elements that favor joint custody than phrases that favor sole custody. The differences are statistically significant for all factual elements and legal principles.

The two groups differentiate the type of custody decided by the court (DEC_JOINT).

*** significant at the 1% level.

Table 4 is similar to the previous table but uses the request for joint custody and sole custody as a grouping variable (RQ_JOINT). It should be remembered that these are second instance appeal proceedings; therefore, they were already subject to a judgment, and the plaintiff is asking for a new trial. The results show a negative relationship between the petition and the factual elements. That means that petitions for joint custody include on average more labels referring to factual elements that favor sole custody than labels favoring joint custody. Again, the differences are statistically significant for all variables.

The two groups differentiate the type of custody requested by the plaintiff (RQ_JOINT).

Relationships between requests, court decisions, sex of the plaintiff and sex of the judge

Table 5 presents contingency tables that show the relationships between requests, court decisions, the sex of the plaintiff and the sex of the judge. Contingency tables were analyzed statistically using Pearson’s Chi-square test statistics. The association was estimated with odds ratios (OR) and their respective 95% confidence intervals. Panel A in Table 5 presents the relationship between what was demanded and success in winning or losing the trial. Plaintiffs only win an average of 17.8% of child custody trials. Those who request joint custody are 1.74 times more likely to win than those who request sole custody. The results are statistically significant.

Panel B in Table 5 presents the relationship between the sex of the appellant and the claims. Most men request joint custody, while most women request sole custody. Female plaintiffs are 29.35 times more likely to request sole custody than joint custody. The differences are very large and statistically significant. In the first instance in Spanish courts, the most frequent rulings are those that grant joint custody to the mother, which partly explains why it is men who appeal in the second instance, and why they are asking for joint physical custody. Panel C in Table 5 presents the relationship between the sex of the appellant and winning or losing the trial. Male plaintiffs are 1.5 times more likely to win than female plaintiffs. The differences are statistically significant. It is, therefore, appropriate to differentiate between the requests made by men and women, so we split the sample into two subsamples, according to the sex of the applicant. Panel D in Table 5 presents the relationship between petitions and winning or losing the trial when the plaintiff is a man. The most common situation is for a man to ask for shared custody, which accounted for 929 cases out of the full sample of 1,884 court sentences (49.3%). It is unusual for a man to ask for individual custody; they only accounted for 10.7% of the full sample. Males requesting joint custody are 2.19 times more likely to win than those requesting sole custody. The differences are statistically significant. Panel E in Table 5 presents the relationship between petitions and winning or losing the trial when the plaintiff is a woman. There is no significant relationship between petitions and winning or losing the trial when the plaintiff is a woman. Panel F in Table 5 presents the relationship between the sex of the judge and court decisions. There were no statistically significant differences between court decisions made by judges of different sexes.

Panel G relates the courts’ decision (DEC_JOINT) to the existence or not of territorial legislation in favor of joint custody (FAVOR_JOINT). There are no statistically significant differences. Panel H relates the plaintiff’s winning the case (WINNER) to the existence or not of territorial legislation in favor of joint custody (FAVOR_JOINT). Plaintiffs from territories whose legislation favors joint custody are 1.54 times more likely to win the trial than those from territories whose legislation does not favor joint custody. The differences are statistically significant. The rationale is that establishing by law a preferential modality increases legal certainty, hence the chances for success are low in second appeals.

Relationships between legal principles and factual findings

In the following, we seek to relate the factual findings to legal principles. Legal reasoning follows several steps that can be simplified as follows: identify the issue and the applicable law; analyze and synthesize the legal rules and principles governing the issue; investigate the relevant facts and apply the rule to the facts to obtain the outcome [ 46 ]. Legal argumentation encompasses the justification of legal decisions–that is, how conclusions can be reached through logical reasoning. The doctrine of stare decisis states that cases that have similar facts should receive similar decisions, thus legal practitioners need to identify such facts and principles in precedent cases, which is a time-consuming task [ 47 ]. Table 6 relates factual elements to legal principles through multivariate linear regressions.

VIF stands for variance inflation factor.

* significant at 10% level

** significant at 5% level

*** significant at 1% level.

The assumptions of multicollinearity [ 48 ], linearity, no auto-correlation [ 49 ], and homoscedasticity [ 50 ] were all checked and found to be within acceptable thresholds. While there was some deviation from normality, the sample size is large enough to consider the deviation not to have a serious effect on the results [ 51 – 53 ].

The first model uses PSY_REP as a dependent variable and the four legal principles as independent variables. A significant regression equation was found (F(4,1879) = 14.390, p < 0.001), but it had a low adjusted R 2 of 0.028. The other models also found significant regression equations, with similar values of goodness-of-fit, ranging from 0.010 to 0.158. The only principle with significant coefficients in all eight models is the best interest of the child (BEST_INT). Let us focus on model 6. When the parents’ readiness (PAR_RDNS) factual finding arises, the most frequent argumentation refers to the proportionality in the responsibilities principle (PROP_RESP) and the best interest principle (BEST_INT). Given a fact related to parents’ readiness (i.e. financial resources of both parents, their previous dedication to childcare, the proximity of the domiciles of the litigants, or their professional activity) the judgment refers particularly to the proportionality in the responsibilities principle in addition to the omnipresent best interest of the child principle. Both legal principles were significant determinants of parents’ readiness, but the beta coefficient for the proportionality in the responsibilities principle (1.376) was notably larger than that for the best interests of the child principle (0.413). For example, if the litigants live nearby, it seems quite reasonable for the party seeking shared custody to argue that this factual finding (on parents’ readiness) favors joint custody, alluding to the principle of shared responsibility–always in the best interests of the child. Similar associations can be identified in the remaining facts and legal principles. The parents’ equality principle (PAR_EQL) exhibits a significant relationship with the child’s psychological circumstances (CHILD_PSY), and the res judicata principle (RES_JUD) has a significant relationship with the parents’ agreements (PAR_AGREEM).

Predicting court decisions

Our second research question was about predicting court decisions. Table 7 shows the results of a logistic regression whose dependent variable is the court decision on custody type (DEC_JOINT). When developing a predictive model, it is necessary to validate it–that is, to evaluate its performance by testing how well the model predicts the dependent variable in the presence of new cases [ 54 , 55 ]. External validation in which the forecasting capacity is tested using a different sample from another time period (temporal validation) was applied. The training sample includes 942 court rulings from June 2016 to May 2019, and the test sample includes 942 court rulings from May 2019 to June 2020.

Training sample comprises 942 court sentences, where 375 are joint custody and 567 are sole custody, using data from June 2016 to mid-May 2019. Test sample comprises 942 sentences, where 375 are joint custody and 567 are sole custody, using data from mid-May 2019 to June 2020. True negative rate = 1—Type 1 error rate. True positive rate = 1—Type II error rate.

Models 1 through 9 are univariate logistic regression analysis, showing B coefficients and significance levels. Model 10 is a multivariate logistic regression. Model 11 is a multilayer perceptron neural network. Model 12 is a radial basis function neural network. The standardized importance of each variable is shown in both neural network models.

We designed the research to be a real-world study in which a law firm wants to develop a predictive model, taking May 2019 as a starting point. The team of lawyers would take the information available on that date, develop the model, and make predictions as new sentences appear, while being able to calculate various performance measures for the model. We used accuracy, true positive rate (sensitivity), true negative rate (specificity), and the area under the receiver operating characteristic curve (AUC) as performance measures. The first eight models are univariate logistic regressions that use each of the factual elements as an explanatory variable. Model 9 uses the sex of the plaintiff as an explanatory variable. The accuracy of univariate models ranges from 58.8% to 75.9%. The independent variable that shows the greatest predictive ability is the relationship between parents (PAR_RELAT). Although the predictive power of this variable is remarkable, it presents slightly unbalanced values in terms of the percentages of type I and type II errors. Model 10 is a full model with all nine independent variables and has an accuracy rate of 83.3%. The percentages of type I and type II errors are fairly balanced.

Table 7 also shows the results of training two neural network models. Model 11 is a multilayer perceptron with a hidden layer and sigmoid activation functions. Model 12 is a radial base function network. The accuracy increased to 86.4% and 84.6%, respectively. The percentages of type I and type II errors are fully balanced. The normalized importance of each variable is shown in both neural network models, and the results coincide with those obtained in the logistic regression.

Table 5 showed that the probability of losing the trial was much higher than that of winning it. This encourages research into decisional rules that may be valuable, which was also the subject of the second research question. We used the Chi-squared Automatic Interaction Detection (CHAID) decision tree to get rules [ 56 ]. Table 8 summarizes the results. Again, we divided the sample into training data for the development of the model (n = 942) and testing data for the validation of the model (n = 942). Node 1 of Table 8 shows that 60% of the cases are men who go to trial, and the test sample indicates that they have a probability of winning of 19.6%. Specifically, there are 108 winning cases out of 942, which is 11.5% of the test sample. The probability of winning rises to 21.5% if the man requests shared custody (Node 2). Node 3 provides a winning strategy, as it represents men who request joint custody and have an excellent relationship with their partner (PAR_RELAT); sentences refer to this positive relationship more than 4 times on average. Then, the probability of winning rises to 89.1%. 4.9% of cases meet these characteristics. The probability of winning even increases to 91.3% if the psychophysical circumstances of the child (CHILD_PSY) favor shared custody (Node 4). On the contrary, a man who applies for joint custody and has a bad relationship accompanied by child circumstances that are unfavorable for joint custody has a 99.4% chance of losing at trial (Node 5). This is a frequent scenario, with 349 cases (18.5% of the sample), and only on 2 occasions did the judge grant joint custody. Women who file for joint custody have a small (16.9%) chance of getting it (Node 10), which increases to 80% if the parental relationship is good and the child’s circumstances recommend it, although this only happens 0.9% of the time. If a woman requests individual custody, she will usually lose her case, with a probability of 86.7% (Node 13). This situation accounts for 34.6% of all trials.

Growing method: Exhaustive CHAID. Accuracy of the test (90.7%), true negative rate (93.9%), and true positive rate (77.8%).

Discussion and conclusion

This paper shows that it is possible to explain and predict court decisions on child custody from a set of factual findings, with a success rate of over 85%. The first research question studied the factors that explain the decision to grant sole or joint custody. Our empirical study was conducted with second instance appeal judgments. We found that all factual elements hypothesized in our model are considered by the judge, whether they are related to the child, the parents, or the psychological report. The relationship and attitudes of the parents and the psychophysical circumstances of the child are the two factual elements that the judge takes most into account when deciding the type of custody. This is very much in line with the family systems theory [ 7 ]. The best interest of the child is the only principle with significant coefficients in all regression models. This principle stands out among the legal principles that justify the decision [ 44 ], which is supported by the theory of therapeutic justice and most of the explanatory theories of court decisions. In the cases analyzed, the sex of the applicant is very important, which is in line with previous studies [ 44 ]. Women win 14.3% of the trials, while men win 20.1%, and the differences are statistically significant. This can be explained because 82.1% of men request joint custody, while 86.5% of women request sole custody. Then, it must be taken into account that the justice grants the appellant individual custody 13.5% of the time and joint custody 21.4% of the time. The rulings of the Spanish Supreme Court have a significant influence on the decisions of the second instance courts and their tendency is in favor of joint physical custody. This may explain why second instance court rulings are increasingly favorable to joint physical custody. As expected, we found no statistically significant differences in the sex of the judge when granting sole or shared custody, which speaks for the neutrality of Spanish judges.

The second research question addresses whether court decisions can be predicted with a reasonable success rate. We used temporal validation; that is, the test samples come from data collected after the training data, as in a real-world application. We developed the models using logistic and neural network regression, achieving satisfactory performance measures. Our study highlights that justice is predictable in the case of child custody, which is a contribution because papers predicting judicial decisions addressed other legal concerns [ 13 – 16 , 30 ]. Holmes [ 11 ] claimed that for the rational study of the law, the man of the future must be the man of statistics, and law must be predictive. We can conclude from our analysis that it is possible to forecast court decisions in the context of child custody from a modest set of factual elements.

Practical implications

The paper provides practical implications affecting parents, lawyers, and even the judicial system. We found statistically significant differences by considering the factual elements to be independent variables and by, rather than considering the decisions to be dependent variables, considering the requests (joint custody vs sole custody). However, interestingly, the facts influence in the opposite direction: that is, those who complain the most have the least reason to complain. This is because the courts rule in favor the plaintiff in the case of second appeal judgments on child custody only 17.8% of the time. Perhaps many parents decide to go to trial without being aware of their chances of success, leaping in the dark. We obtained useful rules for decision making using the CHAID decision tree technique. Some of the rules allow the recognition of judicial patterns with an accuracy above 90%. For example, if a couple has a very bad relationship, it is almost impossible for the judge to award joint custody (less than a 0.5% chance of winning). However, this is a very common case (about a quarter of the cases), and lawyers would probably do well to advise their client to avoid a trial under these circumstances. The practical implications of these rules are clear as they allow the preparation and filing of a lawsuit with information on the probability of success or failure. Many parents who go to court would be willing to negotiate if they knew that their chance of success is low according to the rules obtained in our empirical study. This is very much in line with the therapeutic justice process, which aims at families resolving their own disputes and encourages the use of mediators [ 57 ]. The use of legal decisional systems would make it possible to know the probabilities of success, contributing to reducing the asymmetry of information in the legal domain, which has pernicious effects [ 9 ]. If the use of these predictive models becomes widespread, there may even be effects on the judicial system. It is possible that the number of trials would decrease, and the saturation and slowness in the judicial system would be alleviated. It can also affect the work of lawyers and the way they approach a trial. The lawyer’s experience is a determining factor when it comes to winning lawsuits [ 58 ]. Studies such as the one presented can supplement experience by providing insight into the factors that judges take into account in their decisions. It could also be the case that a lawyer who wants to improve his record by winning a high percentage of cases could accept only those that a priori are more likely to be won.

Limitations and future research

Reading and labeling 1,884 court sentences is laborious and has a certain amount of subjectivity, which is a limitation of the work. Much progress has been made in argumentation mining in the legal domain through the application of natural language processing [ 59 ]. We made several attempts to automate labeling, with qualitative text analysis software and training a neural network model for argument mining. The results were promising, and there was a high degree of agreement with the human experts, but manual labeling was chosen, because given the objective of the study, no level of discrepancy was acceptable. It is proposed as a future line of research because it would facilitate extending this type of study to other decisions in the legal domain. We identified a set of factual elements that explain court decisions, but other factual findings or even external variables, such the experience of lawyers, can be relevant as factors of success in trials [ 58 ]. It would be positive to extend the study using first instance court decisions, as we only analyzed second instance court decisions, which would increase the validity of the study. Another limitation is that the results are valid for cases in Spain; the results could be generalized to other countries, but only those with similar development indices. To overcome these limitations, it would be necessary to extend the study to other contexts, which we propose as a future line of research.

Funding Statement

This study was funded by the Spanish Ministry of Education and Science (RTI2018-093483-B-I00; RTI2018-095843-B-I00), the Government of Aragon (S38_17R; S12_20R; LMP118_18) and the European Regional Development Fund (ERDF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Reproductive rights in America

Research at the heart of a federal case against the abortion pill has been retracted.

Selena Simmons-Duffin

The Supreme Court will hear the case against the abortion pill mifepristone on March 26. It's part of a two-drug regimen with misoprostol for abortions in the first 10 weeks of pregnancy. Anna Moneymaker/Getty Images hide caption

The Supreme Court will hear the case against the abortion pill mifepristone on March 26. It's part of a two-drug regimen with misoprostol for abortions in the first 10 weeks of pregnancy.

A scientific paper that raised concerns about the safety of the abortion pill mifepristone was retracted by its publisher this week. The study was cited three times by a federal judge who ruled against mifepristone last spring. That case, which could limit access to mifepristone throughout the country, will soon be heard in the Supreme Court.

The now retracted study used Medicaid claims data to track E.R. visits by patients in the month after having an abortion. The study found a much higher rate of complications than similar studies that have examined abortion safety.

Sage, the publisher of the journal, retracted the study on Monday along with two other papers, explaining in a statement that "expert reviewers found that the studies demonstrate a lack of scientific rigor that invalidates or renders unreliable the authors' conclusions."

It also noted that most of the authors on the paper worked for the Charlotte Lozier Institute, the research arm of anti-abortion lobbying group Susan B. Anthony Pro-Life America, and that one of the original peer reviewers had also worked for the Lozier Institute.

The Sage journal, Health Services Research and Managerial Epidemiology , published all three research articles, which are still available online along with the retraction notice. In an email to NPR, a spokesperson for Sage wrote that the process leading to the retractions "was thorough, fair, and careful."

The lead author on the paper, James Studnicki, fiercely defends his work. "Sage is targeting us because we have been successful for a long period of time," he says on a video posted online this week . He asserts that the retraction has "nothing to do with real science and has everything to do with a political assassination of science."

He says that because the study's findings have been cited in legal cases like the one challenging the abortion pill, "we have become visible – people are quoting us. And for that reason, we are dangerous, and for that reason, they want to cancel our work," Studnicki says in the video.

In an email to NPR, a spokesperson for the Charlotte Lozier Institute said that they "will be taking appropriate legal action."

Role in abortion pill legal case

Anti-abortion rights groups, including a group of doctors, sued the federal Food and Drug Administration in 2022 over the approval of mifepristone, which is part of a two-drug regimen used in most medication abortions. The pill has been on the market for over 20 years, and is used in more than half abortions nationally. The FDA stands by its research that finds adverse events from mifepristone are extremely rare.

Judge Matthew Kacsmaryk, the district court judge who initially ruled on the case, pointed to the now-retracted study to support the idea that the anti-abortion rights physicians suing the FDA had the right to do so. "The associations' members have standing because they allege adverse events from chemical abortion drugs can overwhelm the medical system and place 'enormous pressure and stress' on doctors during emergencies and complications," he wrote in his decision, citing Studnicki. He ruled that mifepristone should be pulled from the market nationwide, although his decision never took effect.

Matthew Kacsmaryk at his confirmation hearing for the federal bench in 2017. AP hide caption

Matthew Kacsmaryk at his confirmation hearing for the federal bench in 2017.

Kacsmaryk is a Trump appointee who was a vocal abortion opponent before becoming a federal judge.

"I don't think he would view the retraction as delegitimizing the research," says Mary Ziegler , a law professor and expert on the legal history of abortion at U.C. Davis. "There's been so much polarization about what the reality of abortion is on the right that I'm not sure how much a retraction would affect his reasoning."

Ziegler also doubts the retractions will alter much in the Supreme Court case, given its conservative majority. "We've already seen, when it comes to abortion, that the court has a propensity to look at the views of experts that support the results it wants," she says. The decision that overturned Roe v. Wade is an example, she says. "The majority [opinion] relied pretty much exclusively on scholars with some ties to pro-life activism and didn't really cite anybody else even or really even acknowledge that there was a majority scholarly position or even that there was meaningful disagreement on the subject."

In the mifepristone case, "there's a lot of supposition and speculation" in the argument about who has standing to sue, she explains. "There's a probability that people will take mifepristone and then there's a probability that they'll get complications and then there's a probability that they'll get treatment in the E.R. and then there's a probability that they'll encounter physicians with certain objections to mifepristone. So the question is, if this [retraction] knocks out one leg of the stool, does that somehow affect how the court is going to view standing? I imagine not."

It's impossible to know who will win the Supreme Court case, but Ziegler thinks that this retraction probably won't sway the outcome either way. "If the court is skeptical of standing because of all these aforementioned weaknesses, this is just more fuel to that fire," she says. "It's not as if this were an airtight case for standing and this was a potentially game-changing development."

Oral arguments for the case, Alliance for Hippocratic Medicine v. FDA , are scheduled for March 26 at the Supreme Court. A decision is expected by summer. Mifepristone remains available while the legal process continues.

• Abortion policy
• abortion pill
• judge matthew kacsmaryk
• mifepristone
• retractions
• Abortion rights
• Supreme Court

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Accessing Cases in Print

Please note that the Law Library is no longer updating any state law reporters in print. The Law Library is retaining reporters from states besides Illinois on the 5th floor of the Library. Regional reporters and digests are located from call numbers KF 35 .A7 ( Atlantic Reporter ) through KF 135.S8 ( Southern Reporter ); state reporters and digests (if we own them) can be found with the non-Illinois state law materials (call numbers KFA through KFW ). See the map below for the location of the regional reporters and digests and state reporters in relation to the stairwell.

Federal case law reporters and the Federal Practice Digest are still being updated in print; all sets can be found on the 4th floor of the Law Library. See the map below for their location in relation to the stairwell.

About the National Reporter System

Cases are published in chronological order based on the date they were decided, and not by subject. Unfortunately, however, the research projects you receive usually will be asking you to research a legal issue (i.e., a subject). Therefore, you need an index to the reporter system. This is where the West American Digest System  and the  Topic and Key Number system come in handy. West publishes unofficial reporters as part of its National Reporter System  with the goal of including every reported case from every jurisdiction. For state cases, West publishes seven regional reporters, which each include cases from several states. West publishes a map that shows which states are covered in each regional reporter. Note that these do not always make geographic sense; for instance, Oklahoma and Kansas are listed as "Pacific" states.

About the West Key Number System

• Searching with Key Numbers in Westlaw Edge - Quick Reference Guide

Cases are published in chronological order based on the date they were decided and not by subject. However, the research projects you receive usually will be asking you to research a legal issue (i.e., a subject). Therefore, you need an index to the reporter system. The West Key Number System  provides an indexing function for all published cases, enabling a researcher to locate cases by subject from any court in any jurisdiction. The system also acts as an abstracting service, providing researchers with short summaries of the points of law discussed in the indexed opinions. 

The Key Number System subdivides the law into over 450 broad legal  Topics , which are then further subdivided into Key Numbers , which are assigned to specific legal issues within the broader Topics. Topics and Key Numbers are consistent throughout West's System; the same subject arrangement is applied to all cases from every jurisdiction, so that if you find a case from one jurisdiction that discusses a legal issue you are researching, you can use the Topic and Key Number for that issue to find other cases from other jurisdictions that discuss the same issue. The "Searching with Key Numbers" guide (above) provides more detail.

West employs attorneys who read each case, identify the point(s) of law discussed, summarize the point(s) of law in a headnote (a paragraph summary of the point of law) using consistent legal terminology. At least one Topic and Key Number are assigned to each headnote. These headnotes will appear in a West  digest under the appropriate Topic and Key Number and at the beginning of each case as it appears in a West reporter. 

Therefore, headnotes act as the bridge between the Key Number System and cases: you can use the headnotes from a case you've identified and look for similar cases on the same topic. Alternatively, you can start with the Key Number System, look up your subject by Topic and Key Number, and then find cases by reviewing the language of the headnotes you find.

About Cases

Cases are opinions written by judges that resolve disputed legal issues (as opposed to disputed factual issues ). Researching cases is important because of the doctrine of stare decisis  or precedent . Under the principle of stare decisis , courts are bound to follow the rulings of law from previously decided cases in the same jurisdiction when facing similar cases currently before the court. For the legal researcher, reviewing cases from a court will help determine how the court will rule if given a similar factual situation. In the U.S. system, the only published cases you will find are from intermediate appellate courts and supreme courts. Most trial court decisions are not published.

Cases are published in chronological order in reporters . There are two different categories of reporters that you need to know:  official reporters , which are usually published by a government entity, and unofficial reporters , which are published commercially (usually by either West or Lexis). 

To find a case in a reporter, you will need to know its citation . A case citation includes the name of the case, the volume number of the reporter containing the case, the abbreviation for the reporter, the first page of the case, and the year of the decision. For example:

P.G.A. v. Martin , 532 U.S. 661 (2001).

To find the text of this case in print, you would go to volume 532 of the United States Reports (the official reporter of the U.S. Supreme Court) , then page 661. The U.S. Supreme Court decided the case in the year 2001. To find it online, you would search by the citation.

You will often see multiple citations to the same case when the case is reported in multiple reporters (called  parallel citations ).  Table T.1  of  The Bluebook  lists the reporters (both official and unofficial) for every jurisdiction in the United States. For example, the P.G.A. v. Martin  case below is reported in three reporters in the following order: the United States Reports (the official reporter of the U.S. Supreme Court),  the  Supreme Court Reporter   (West's unofficial reporter), and the  Lawyers' Edition   (Lexis's unofficial reporter).  

P.G.A. v. Martin , 532 U.S. 661, 121 S.Ct. 1879, 149 L. Ed. 2d 904 (2001).

How to Read a Case

At the top of the reporter page (see below), you will see the citation to the case, including any parallel citations (citations to the same case in other reporters). Then you will see a short summary of the case (the syllabus ), which includes an indication of how it got to the court that has issued the opinion, and the holding(s) on the point(s) of law. After the syllabus, you will see the headnotes discussing the point(s) of law in the case in the order in which they appear in the opinion, as well as the Topics and Key Numbers that can be used to find other cases on the same legal issue(s). Next is the actual text of the opinion.

One important note: The headnotes and summary are NOT written by the court; they are written by West editors. Therefore, you should never cite to a case based on what you've read in the syllabus and headnotes; you must read the case and cite to the language written by the court.

For additional insights, Prof. Orin Kerr's article " How to Read a Legal Opinion: a Guide for New Law Students " explains what judicial opinions are, how they are structured, and what to look for when reading them.

Using Citators

Citators  perform three functions for legal researchers. A citator will tell you if the case you've found is still "good law," or if subsequent decisions have weakened its authority or overruled it altogether. A citator can also provide citations to other cases and secondary sources that discuss the same legal issue as in the case you've found. Finally, a citator can provide you with case history (references to other proceedings in the same case).

Each subscription research service has its own citator. In Lexis, the citator is called Shepard's . Westlaw's citator is called KeyCite . Bloomberg Law's citator is called BCite . The following presentations will show you how each citator works. Westlaw also has a "Quick Reference Guide" (PDF) for using KeyCite (see below).

If you have access to multiple citators, it's a good idea to check your case in each citator, because citator results will often include different cases, different secondary sources, and different interpretations of subsequent cases that have cited to your case.

Lawyer's Nightmare: DOL Blames Westlaw for Skimpy Brief - this Law360 article demonstrates the hazards of relying on the results of just one research platform/citator.

Below are vendor-produced materials on citators: a PDF Guide to using KeyCite in Westlaw, and video tutorials on using Shepard's in Lexis and BCite in Bloomberg Law.

• Checking Cases in KeyCite - Quick Reference Guide

BCite from Bloomberg Law - Law School Team on Vimeo .

Subject Guide

CALI LibTour - West's Digests

As part of its LibTour podcast series, CALI created this brief introduction to the West American Digest System . Follow the link below to the podcast.

• CALI LibTour Podcast - West's Digests

How to Cite Cases - Examples

Rule B10 (Bluepages) of The Bluebook (21st edition) covers how to cite cases in non-academic legal documents.  The Bluebook , however, notes that practitioners should be aware of local court rules and specifies on p. 3,"Make sure to abide by any citation requirements of the court to which you are submitting your documents."  Table BT2 of the Bluepages contains an index of jurisdiction-specific citation rules. See the " Illinois Resources " tab of this guide for specific information on citing cases, statutes, and regulations in Illinois court documents.

There are many variables involved in citing cases, so you will probably have to check one of the sub-parts of a rule to determine how to cite your particular case. The following example is the first one included in rule B10 (rule B10.1) (Bluepages) (see p. 11)

Engel v. Vitale , 370 U.S. 421, 430 (1962) ("The Establishment Clause, unlike the Free Exercise Clause, does not depend upon any showing of direct governmental compulsion . . . .").

The components of the citation are, in order: the name of the case; the published or unpublished source in which the case can be found; a parenthetical indicating the court and year of decision; other parenthetical information, if any; the case's subsequent history, if any. For the case name, omit all parties other than the first party listed on each side of the "v." per rule B10.1.1 (Bluepages) . In the above citation, note that the first page of the case is listed, as well as the specific page referred to (pinpoint citation) per rule B10.1.2 (Bluepages) . Per rule B10.1.3 (Bluepages) , when citing decisions of the U.S. Supreme Court, or a state's highest court, do not include the name of the deciding court, as in the above example.

Table T6  (p. 304) lists abbreviations for case names and institutional authors in citations.

This example for a federal appellate court case is listed in rule B10.1.3 (Bluepages) (see p. 13):

Envtl. Def. Fund v. EPA , 465 F.2d 528, 533 (D.C. Cir. 1972).

The Bluebook  notes that the Bluepages retain the tradition of underlining certain text, although italics are equivalent (see p. 6).

Rule 10 (Whitepages) (see p. 95) contains the rules for citing cases in academic works. Some examples The Bluebook provides are:

Meritor Sav. Bank v. Vinson, 477 U.S. 57, 60 (1986).

In this example, the case was published in volume 477 of the U.S. Reports (the official reporter of the U.S. Supreme  Court). The case starts on p. 57, but the cited information appears on p. 60. The case was decided in 1986.

Ward v. Reddy, 727 F. Supp. 1407, 1412 (D. Mass 1990). 

In this example, the case was published in volume 727 of the Federal Supplement (a West reporter that contains selected decisions of the U.S. District Courts). The case starts on p. 1407, and the cited information appears on p. 1412. The case was decided by the District Court of Massachusetts in 1990. 

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APA Court Case Citation — General Format with Examples

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APA court citation is used to cite legal proceedings in a research paper , and understanding this format can be aided by looking at APA examples . Court proceedings may be cited as primary or secondary research sources, although their format differs from other sources such as APA book citation or APA journal article citation . Landmark rulings are extensively documented, as they outline legal procedures that inform changes in legislation.

Inhaltsverzeichnis

• 1 APA Court Case Citation – In a Nutshell
• 2 Definition: APA court case citation
• 3 APA court case citation: Abbreviations
• 4 APA court case citation: Federal court cases
• 5 APA court case citation: State court cases

APA Court Case Citation – In a Nutshell

• APA court case citation is used to cite legal documents and proceedings in research publications.
• A bbreviations are often used to stand for words in APA court case citation.
• Researchers can cite documents from the district , supreme and appellate courts .
• APA court case citation defines in-text citation and reference page formats for individual legal cases.

Definition: APA court case citation

APA court citation refers to the standard format of citing court case proceedings as research sources. It does not list authors like conventional sources; instead, abbreviations are used to make them more precise.

In APA court case citation, the reference refers to the reporters or the publications that document the cases. When citing a court case, general APA court case citation requires that you indicate the name of the case, volume number, and the reporter’s name in abbreviated form. You should also include the page number, name of the court, year, and an optional URL.

The name of the case is written in italics in the in-text citation. The reference page indicates only a page number rather than a full-page range.

APA reference entry: Wade v. Thomas, 65 (L.A. Sup. Ct. 1905).

APA in-text citation: ( Wade v. Thomas , 1975) [2]

APA court case citation: Abbreviations

Most words in APA court case citation are abbreviated. There is a large catalog of standard abbreviations used to represent most words

APA court case citation: Federal court cases

Federal cases involve violations of federal law or the constitution. APA court case citation of federal cases guides the usage of legal proceedings at the national level as research sources.

U.S. Supreme Court

The Supreme Court is the highest federal court in the U.S. Verdicts and decisions of the Supreme Court are documented in the United States Reports , abbreviated as “U.S.” in the APA court case citation. Including a parenthesis is not necessary for this reference, as the reporter’s name is already mentioned.

Circuit court

Circuit courts sit in multiple places in a judicial district. They are referenced from the Federal Reporter in APA court case citation, which has appeared in three series labeled “F,” “ F.2d ”, and the most recent series, “ F.3d ”. The U.S. has 13 circuit courts cited differently in parenthesis, for example, “ 6th Cir .”

APA court citation for circuit courts are cited as follows:

District court

U.S. District court decisions are recorded in the Federal Supplements . APA court citation of district court files also uses abbreviations according to three series, namely “F. Supp ,” “ F. Supp. 2d ”, “ F. Supp. 3d ”.

Researchers should specify the district court cited in their APA court case citation, as several district courts exist. Use parenthesis to distinguish, e.g., “N.D. Massachusetts.”

APA court case citation for district courts is formatted as follows:

APA court case citation: State court cases

State courts, including appellate and Supreme Courts, are held in specific states and can be included in APA case citation. They are cited similarly as follows:

How are court cases cited that have no page number?

In APA court case citation, recent court cases may not have print pages; use three underscores as a series to illustrate this. For example, Newark v. Chalton, 765 U. S. ___ (2019).

How do you cite district court proceedings in APA court case citation?

Include the names of the opposing parties, page number, and year. Specify the district court you are referring to in parentheses.

When should you include a URL in APA court case citation?

Indicating a URL in APA court case citation is optional . It may assist the reader in locating the source, but it is not a requirement.

How are abbreviations used in APA court case citation?

Standard abbreviations are used in place of most words in legal citations. Several resources outline the appropriate abbreviations for each case released by legal institutes.

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138 Court Essay Topics

🏆 best essay topics on court, ✍️ court essay topics for college, 👍 good court research topics & essay examples, 📌 easy court essay topics, 🎓 most interesting court research titles, 💡 simple court essay ideas.

• Dual Court System: Advantages and Disadvantages
• Case Notes on Court: BGP Properties Pty Limited v Lake Macquarie City Council
• The Lucy v. Zehmer Court Case Review
• Crimes Against Humanity by International Criminal Court
• The Courtroom: The Role Participants in a Court
• Geriatric Court: Pros and Cons
• Case Notes on Court: Stockland Development Pty Ltd v Manly Council
• Court Cases that Address Prosecutors Ethics A prosecutor is a person who represents a state in criminal cases in courts of law, he or she is responsible for executing a criminal who has broken a law.
• Texas vs. Johnson: Landmark of US Supreme Court The Texas v Johnson case, considered by the US Supreme Court thirty years ago, is on the list of Supreme Court decisions that have had the greatest impact on American society.
• Dual Court System in the United States The purpose of this article is to consider the principle of the functioning of the dual judicial system in the United States.
• Attending Court: Personal Experiences On 23rd February 2011, I attended a bail hearing of an accused police constable who shot and killed a 26-year-old man. The hearing took place in Ontario.
• Analysis of Roe v. Wade Supreme Court Case Study Since the Supreme Court’s Roe V. Wade ruling in 1973, around one out of every three pregnancies has resulted in abortion that’s why it is one of difficult and contentious problems.
• The Case of Camara vs. Municipal Court This paper analyzes the case of Camara vs. Municipal court and whether the fourth amendment applies in this case.
• The International Criminal Court Jurisdiction The present position of the US towards joining the international criminal courts remains negative, and there are prospects that it may change with the new administration.
• The Court of Law: Merits and Difficulties The court of law is one of the pillars of the democratic system. This is why it is very important that the judges and other court members deliver the most justice they can.
• The Riley v. California Supreme Court Case Brief The case Riley v. California investigated by the Supreme Court in 2014 is an excellent example of the unacceptable actions of police officers in investigating crimes.
• Supreme Court Case Matal v. Tam and The First Amendment The First Amendment is an important normative act that helps attribute trademarks to the objects of cultural or commercial expression but not to official state messages.
• The Hierarchy of the Court System in England and Wales This study assesses the hierarchy of the court system in England and Wales. It also seeks to determine how the common law doctrine of binding precedent interacts with the order.
• Life Terms and Limits of the Supreme Court Justice The paper concludes that the life terms practice should not be changed as it is integral to the existing political system and corresponds with the purpose of the Supreme Court.
• Landmark Court Cases in Digital Communication The paper examines three landmark cases in digital communication comprising United States v. Anderson (2014), Fort Wayne Books v. Indiana (1989), and California v. FCC (1988).
• Disempowerment of Women in King Arthur’s Court Thomas Malory belongs to those writers who managed to depict the disempowerment of women without focusing on the problem of racial discrimination alone.
• Supreme Court Decision Research Paper The Supreme Court’s decisions have great importance in legal practice and often become precedents determining how future cases are to be decided.
• The United States vs. Trump Supreme Court Case Investigations on Trump’s conduct have led to numerous cases in court, including stealing classified documents from the White House and storing them in Mar-a-Lago.
• Breyer on the Supreme Court’s Fundamental Role In “Making Our Democracy Work: A Judge’s Views, ” Stephen Breyer, a Supreme Court justice, outlines how the justice system’s US Constitution should work.
• Family Court-Ordered Mediation: Violence Family Court-Ordered Mediation is not suitable for the victims of domestic abuse, parents with mental illnesses or addictions, and in cases of child neglect.
• The Milbanke Court vs. Grantley House Lease Agreement The article delves into the complexities of lease agreements and the legal aspects related to various properties to provide a comprehensive overview of leasing practices.
• The Impact of Supreme Court Decisions on the US Economy High-profile U.S. Supreme Court decisions have underscored the role and importance of the Constitution and underpinned many economic tendencies.
• Court Cases That Impacted to Death Penalty Daryl Atkins, who has an IQ of 59, was found guilty of murdering an Air Force enlisted man inside a convenience shop and was sentenced to death for his crime.
• The U.S. Supreme Court Election System The elections of the US Supreme Court members are politicized and ideologized, which is incompatible with democratic values and contrary to the Constitution.
• The Loving v. Virginia Supreme Court Case Analysis Loving v. Virginia was a case decided by the Supreme Court of the United States that legalized interracial marriage nationwide.
• Ricci v. DeStefano: Supreme Court Case The paper discusses the Ricci v. DeStefano court matter. A group of African Americans could not get a promotion which they filed as racism in the workplace.
• International Criminal Court and Its Importance International Criminal Court presents a permanent international court with jurisdiction to prosecute individuals for various international crimes.
• Supreme Court’s Abortion Ruling Sets Off New Court Fights The article discusses the Supreme Court’s decision to ban abortions and give states the right to decide on their local level whether they want to prohibit it or not.
• The Supreme Court in Lifetime Appointment A lifetime appointment to the Supreme Court is not a beneficial approach for the modern age, and it cannot effectively prevent undue influence over the Justices.
• How the US Supreme Court Affected Japanese Americans Some Japanese Americans refused to relocate to internment camps during World War II, and they also challenged government orders to do so in court.
• The Supreme Court and the American Constitution The paper looks upon Constitutional provisions and Supreme Court’s rulings and discusses the impact they had on American life.
• Court Systems of the United States The US has two primary court systems: State and Federal courts. The dual court system has a significant impact on the process of trial and diverse aspects of the legislation.
• A Court Case That Influenced Federal Decision on Gun Control The case of plaintiff George Young, who sues the state and county for denying him an application for a license for carrying a handgun in public for personal defense.
• Gun-Carry Rules Bar Jail, Court Retirees A recent case documented by O’Connor shows how the federal courts have barred retired officers from carrying guns despite being permitted to do so by the law.
• “Miranda v. Arizona” Supreme Court Ruling The Supreme Court landmark case “Miranda v. Arizona” was decided in 1966. The ruling pertains to the fifth and sixth constitutional amendments.
• Introduction of Information Technologies in Court Introducing AI and IT technologies in court will significantly increase the technological aspect of justice and its accessibility.
• Stare Decisis and the Principle’s Role in Court Cases Stare decisis is a doctrine that is based on courts following previously covered cases with the aim of increasing efficiency throughout the process.
• Whren v. the United States, 517 US 806 – Supreme Court 1996 Whren’s motion says that the search of the car in which he was a passenger by officers in an unmarked car was a violation of the 4th Amendment.
• The Brown vs. Board of Education Case: Supreme Court Decision This paper will analyze the Supreme Court’s decision in the Brown vs. Board of Education case and discuss its implications for civil rights.
• Canadian Court and Sex Offences This paper focuses on investigating three main factors that may prevent or complicate judicial proceedings regarding sex offense cases.
• Historical Controversies of Supreme Court Justice Selection The selection of the new members of the Court is a complicated process that includes confirmation procedures from the President and the Senate.
• “Kelo v. New London”: Court Case Analysis This paper discusses the case “Kelo v. New London”, where this case was originally filed, what Constitutional issue is being raised, and when was oral argument heard.
• Court Decisions on Drug Offenders The research entails an investigation into the variables that influence the sentencing decisions of judges in drug offenders’ cases.
• The Impact of Race on Court Decisions The main question of the paper is whether white people and people of color, women and men, rich and poor, young and old, receive the same punishment for the same crime.
• Supreme Court’s Marbury v. Madison (1803) Case Marbury v. Madison remains a landmark and relevant ruling today for the Supreme Court because the ruling established the doctrine of judicial review.
• Abortions. Perspectives, Federalism, Court Cases Abortion has been one of the most provocative topics across the globe. People have different views on whether a woman should be permitted to abort her child or not.
• Supreme Court Ruling on Affordable Care Act This paper describes the majority opinion of the United States Supreme Court about the ACA. The paper also discusses personal views on the constitutionality of the ACA.
• Loving vs. Virginia: The Supreme Court Case Loving vs. Virginia 388 U.S. 1 (1967) is a historic decision of the U.S. Supreme Court. It was a controversial one that pushed along reform in the United States.
• Workplace Discrimination Laws: Court Case Employers who terminate their workers on the basis of their sexual orientation break the law by violating Title VII of the Civil Rights Act of 1964.
• “Supreme Court Justices Question…” Article by R. Rubin The reviewed article is titled “Supreme Court Justices Question IRS Shield in Tax-Shelter Case,” and it was posted in The Wall Street Journal on December 1, 2020, by Richard Rubin.
• The Discriminations Based on Significant Court Cases This paper analyzes the differences in the approaches taken to remedy each of the discriminations based on significant court cases.
• Supreme Court Judgeship Appointment Process This paper examines the appointment process and confirming a Supreme Court justice comprising of two main steps – nomination by the President and confirmation by the Senate.
• Lifetime Appointment for the Supreme Court Justices This proposal is motivated by assuming that current Supreme Court Justices are much older and have held office much longer than before.
• Overturning Supreme Court’s Decision on Evenwel v. Abbott and Trump v. Hawaii Evenwel v. Abbott and Trump v. Hawaii are Supreme Court cases in which America’s highest ability to use cumulative population when redistricting and Trump’s immigration policy.
• State and Federal Court Systems in the United States This paper describes the typical state court system and compares it with the federal court, their authority, and current systematic problems of their work.
• Tinker v. Des Moines: Court Proceeding Tinker v. Des Moines Independent Community School District is a historic Supreme Court Ruling that set the standard for school authorities about implementing policies.
• The United States Criminal Court System The systems approach involves the coordination of all the concerned parties, i.e. the law enforcers, correction agencies, and courts.
• Processes that Entail a Juvenile Court The author of this letter writes to someone’s person in order to make that person understand the processes that entail a juvenile court.
• Derivative Lawsuit: The Laws and Court Cases This paper aims to discuss the laws and court cases regulating shareholder derivative lawsuits in the United Kingdom and examine if they have promoted business cultural responsibility.
• Eyewitness Testimony and Its Reliability in the Law Court A citizen becomes a witness due to an accidental combination of circumstances, but his or her role in criminal proceedings is significant since the witness is indispensable.
• US Legal System and Court Experience The jury trial is a highly structured and orderly process that plays a crucial role in the American legal system.
• The Notion of the Supreme Court The following paper is the establishment of a correlation between the Supreme Court and the public as it is supposed to appear in terms of legal considerations.
• The People Jury Court on McNeese Prior to trial before a jury, McNeese was accused of committing a robbery using a deadly weapon. He had served an imprisonment term in the penitentiary at San Quentin.
• Juvenile Court Philosophy: the Parens Patriae Doctrine The Parens Patriae doctrine has become widely adopted in the juvenile system as advocates of such a system assert that juveniles are influenced by their developmental context.
• The Sekmadienis Ltd. v. Lithuania Court Case Sekmadienis Ltd. v. Lithuania concerned a lawsuit filed by the Lithuanian Government against the advertising company.
• Types of ADR and the Hearing Court The primary types of alternative dispute resolution (ADR) methods include mediation and arbitration. Mediation involves the use of a mediator to reach a settlement conflicts.
• Observations of the Virtual Crown Court and the Impact of COVID-19 on Trials The purpose of this paper is to compare theoretical ideas with the observations of the Virtual Crown Court and discuss how COVID-19 impacted the court trials.
• Psychologists’ Involvement in Civil Court Area Psychologists have varied interests within the civil court. Their involvement usually targets the examination of emotional elements associated with individual injury litigation.
• American Psychological Association’s View on Court Judgments According to American Psychological Association, it is crucial for the court to determine the risk of prospect hostility of an individual claimed before passing of judgment.
• Thurgood Marshall and His Supreme Court Justice Thurgood Marshall was born in 1908 in Baltimore, Maryland to William Marshall who worked as a railroad porter and mother an elementary school teacher.
• American Congress, President, and the Supreme Court In performing duties, Congress must keep in mind the interests of the people to ensure that the policies they make serve the best interest of the public.
• The American Supreme Court: Series of Evolution The American Supreme Court has gone through a series of evolutions over the years. Most of these evolutions have been on their composition, size and responsibilities.
• The United States Supreme Court Overview Congress and Justices of the Court are empowered by the constitution to develop the authorities and operations of the entire Judicial Branch of government.
• Supreme Court Decisions on First Amendment “Bill of Rights “is the first ten amendments made to the American constitution. However, the First Amendment is considered to be the most pivotal.
• Supreme Court in Brown v. Board of Education Case The decision of the Supreme Court in “Brown v. Board of Education“ has given the way for the desegregation of white and black in schools.
• American Democracy and Landmark Supreme Court Cases This essay ponders the key values of American democracy and describes landmark Supreme Court cases that have contributed to the formation of the political system.
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What the data says about abortion in the U.S.

Pew Research Center has conducted many surveys about abortion over the years, providing a lens into Americans’ views on whether the procedure should be legal, among a host of other questions.

In a  Center survey  conducted nearly a year after the Supreme Court’s June 2022 decision that  ended the constitutional right to abortion , 62% of U.S. adults said the practice should be legal in all or most cases, while 36% said it should be illegal in all or most cases. Another survey conducted a few months before the decision showed that relatively few Americans take an absolutist view on the issue .

Find answers to common questions about abortion in America, based on data from the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, which have tracked these patterns for several decades:

How many abortions are there in the U.S. each year?

How has the number of abortions in the u.s. changed over time, what is the abortion rate among women in the u.s. how has it changed over time, what are the most common types of abortion, how many abortion providers are there in the u.s., and how has that number changed, what percentage of abortions are for women who live in a different state from the abortion provider, what are the demographics of women who have had abortions, when during pregnancy do most abortions occur, how often are there medical complications from abortion.

This compilation of data on abortion in the United States draws mainly from two sources: the Centers for Disease Control and Prevention (CDC) and the Guttmacher Institute, both of which have regularly compiled national abortion data for approximately half a century, and which collect their data in different ways.

The CDC data that is highlighted in this post comes from the agency’s “abortion surveillance” reports, which have been published annually since 1974 (and which have included data from 1969). Its figures from 1973 through 1996 include data from all 50 states, the District of Columbia and New York City – 52 “reporting areas” in all. Since 1997, the CDC’s totals have lacked data from some states (most notably California) for the years that those states did not report data to the agency. The four reporting areas that did not submit data to the CDC in 2021 – California, Maryland, New Hampshire and New Jersey – accounted for approximately 25% of all legal induced abortions in the U.S. in 2020, according to Guttmacher’s data. Most states, though,  do  have data in the reports, and the figures for the vast majority of them came from each state’s central health agency, while for some states, the figures came from hospitals and other medical facilities.

Discussion of CDC abortion data involving women’s state of residence, marital status, race, ethnicity, age, abortion history and the number of previous live births excludes the low share of abortions where that information was not supplied. Read the methodology for the CDC’s latest abortion surveillance report , which includes data from 2021, for more details. Previous reports can be found at  stacks.cdc.gov  by entering “abortion surveillance” into the search box.

For the numbers of deaths caused by induced abortions in 1963 and 1965, this analysis looks at reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. In computing those figures, we excluded abortions listed in the report under the categories “spontaneous or unspecified” or as “other.” (“Spontaneous abortion” is another way of referring to miscarriages.)

Guttmacher data in this post comes from national surveys of abortion providers that Guttmacher has conducted 19 times since 1973. Guttmacher compiles its figures after contacting every known provider of abortions – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, and it provides estimates for abortion providers that don’t respond to its inquiries. (In 2020, the last year for which it has released data on the number of abortions in the U.S., it used estimates for 12% of abortions.) For most of the 2000s, Guttmacher has conducted these national surveys every three years, each time getting abortion data for the prior two years. For each interim year, Guttmacher has calculated estimates based on trends from its own figures and from other data.

The latest full summary of Guttmacher data came in the institute’s report titled “Abortion Incidence and Service Availability in the United States, 2020.” It includes figures for 2020 and 2019 and estimates for 2018. The report includes a methods section.

In addition, this post uses data from StatPearls, an online health care resource, on complications from abortion.

An exact answer is hard to come by. The CDC and the Guttmacher Institute have each tried to measure this for around half a century, but they use different methods and publish different figures.

The last year for which the CDC reported a yearly national total for abortions is 2021. It found there were 625,978 abortions in the District of Columbia and the 46 states with available data that year, up from 597,355 in those states and D.C. in 2020. The corresponding figure for 2019 was 607,720.

The last year for which Guttmacher reported a yearly national total was 2020. It said there were 930,160 abortions that year in all 50 states and the District of Columbia, compared with 916,460 in 2019.

• How the CDC gets its data: It compiles figures that are voluntarily reported by states’ central health agencies, including separate figures for New York City and the District of Columbia. Its latest totals do not include figures from California, Maryland, New Hampshire or New Jersey, which did not report data to the CDC. ( Read the methodology from the latest CDC report .)
• How Guttmacher gets its data: It compiles its figures after contacting every known abortion provider – clinics, hospitals and physicians’ offices – in the country. It uses questionnaires and health department data, then provides estimates for abortion providers that don’t respond. Guttmacher’s figures are higher than the CDC’s in part because they include data (and in some instances, estimates) from all 50 states. ( Read the institute’s latest full report and methodology .)

While the Guttmacher Institute supports abortion rights, its empirical data on abortions in the U.S. has been widely cited by  groups  and  publications  across the political spectrum, including by a  number of those  that  disagree with its positions .

These estimates from Guttmacher and the CDC are results of multiyear efforts to collect data on abortion across the U.S. Last year, Guttmacher also began publishing less precise estimates every few months , based on a much smaller sample of providers.

The figures reported by these organizations include only legal induced abortions conducted by clinics, hospitals or physicians’ offices, or those that make use of abortion pills dispensed from certified facilities such as clinics or physicians’ offices. They do not account for the use of abortion pills that were obtained  outside of clinical settings .

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The annual number of U.S. abortions rose for years after Roe v. Wade legalized the procedure in 1973, reaching its highest levels around the late 1980s and early 1990s, according to both the CDC and Guttmacher. Since then, abortions have generally decreased at what a CDC analysis called  “a slow yet steady pace.”

Guttmacher says the number of abortions occurring in the U.S. in 2020 was 40% lower than it was in 1991. According to the CDC, the number was 36% lower in 2021 than in 1991, looking just at the District of Columbia and the 46 states that reported both of those years.

(The corresponding line graph shows the long-term trend in the number of legal abortions reported by both organizations. To allow for consistent comparisons over time, the CDC figures in the chart have been adjusted to ensure that the same states are counted from one year to the next. Using that approach, the CDC figure for 2021 is 622,108 legal abortions.)

There have been occasional breaks in this long-term pattern of decline – during the middle of the first decade of the 2000s, and then again in the late 2010s. The CDC reported modest 1% and 2% increases in abortions in 2018 and 2019, and then, after a 2% decrease in 2020, a 5% increase in 2021. Guttmacher reported an 8% increase over the three-year period from 2017 to 2020.

As noted above, these figures do not include abortions that use pills obtained outside of clinical settings.

Guttmacher says that in 2020 there were 14.4 abortions in the U.S. per 1,000 women ages 15 to 44. Its data shows that the rate of abortions among women has generally been declining in the U.S. since 1981, when it reported there were 29.3 abortions per 1,000 women in that age range.

The CDC says that in 2021, there were 11.6 abortions in the U.S. per 1,000 women ages 15 to 44. (That figure excludes data from California, the District of Columbia, Maryland, New Hampshire and New Jersey.) Like Guttmacher’s data, the CDC’s figures also suggest a general decline in the abortion rate over time. In 1980, when the CDC reported on all 50 states and D.C., it said there were 25 abortions per 1,000 women ages 15 to 44.

That said, both Guttmacher and the CDC say there were slight increases in the rate of abortions during the late 2010s and early 2020s. Guttmacher says the abortion rate per 1,000 women ages 15 to 44 rose from 13.5 in 2017 to 14.4 in 2020. The CDC says it rose from 11.2 per 1,000 in 2017 to 11.4 in 2019, before falling back to 11.1 in 2020 and then rising again to 11.6 in 2021. (The CDC’s figures for those years exclude data from California, D.C., Maryland, New Hampshire and New Jersey.)

The CDC broadly divides abortions into two categories: surgical abortions and medication abortions, which involve pills. Since the Food and Drug Administration first approved abortion pills in 2000, their use has increased over time as a share of abortions nationally, according to both the CDC and Guttmacher.

The majority of abortions in the U.S. now involve pills, according to both the CDC and Guttmacher. The CDC says 56% of U.S. abortions in 2021 involved pills, up from 53% in 2020 and 44% in 2019. Its figures for 2021 include the District of Columbia and 44 states that provided this data; its figures for 2020 include D.C. and 44 states (though not all of the same states as in 2021), and its figures for 2019 include D.C. and 45 states.

Guttmacher, which measures this every three years, says 53% of U.S. abortions involved pills in 2020, up from 39% in 2017.

Two pills commonly used together for medication abortions are mifepristone, which, taken first, blocks hormones that support a pregnancy, and misoprostol, which then causes the uterus to empty. According to the FDA, medication abortions are safe  until 10 weeks into pregnancy.

Surgical abortions conducted  during the first trimester  of pregnancy typically use a suction process, while the relatively few surgical abortions that occur  during the second trimester  of a pregnancy typically use a process called dilation and evacuation, according to the UCLA School of Medicine.

In 2020, there were 1,603 facilities in the U.S. that provided abortions,  according to Guttmacher . This included 807 clinics, 530 hospitals and 266 physicians’ offices.

While clinics make up half of the facilities that provide abortions, they are the sites where the vast majority (96%) of abortions are administered, either through procedures or the distribution of pills, according to Guttmacher’s 2020 data. (This includes 54% of abortions that are administered at specialized abortion clinics and 43% at nonspecialized clinics.) Hospitals made up 33% of the facilities that provided abortions in 2020 but accounted for only 3% of abortions that year, while just 1% of abortions were conducted by physicians’ offices.

Looking just at clinics – that is, the total number of specialized abortion clinics and nonspecialized clinics in the U.S. – Guttmacher found the total virtually unchanged between 2017 (808 clinics) and 2020 (807 clinics). However, there were regional differences. In the Midwest, the number of clinics that provide abortions increased by 11% during those years, and in the West by 6%. The number of clinics  decreased  during those years by 9% in the Northeast and 3% in the South.

The total number of abortion providers has declined dramatically since the 1980s. In 1982, according to Guttmacher, there were 2,908 facilities providing abortions in the U.S., including 789 clinics, 1,405 hospitals and 714 physicians’ offices.

The CDC does not track the number of abortion providers.

In the District of Columbia and the 46 states that provided abortion and residency information to the CDC in 2021, 10.9% of all abortions were performed on women known to live outside the state where the abortion occurred – slightly higher than the percentage in 2020 (9.7%). That year, D.C. and 46 states (though not the same ones as in 2021) reported abortion and residency data. (The total number of abortions used in these calculations included figures for women with both known and unknown residential status.)

The share of reported abortions performed on women outside their state of residence was much higher before the 1973 Roe decision that stopped states from banning abortion. In 1972, 41% of all abortions in D.C. and the 20 states that provided this information to the CDC that year were performed on women outside their state of residence. In 1973, the corresponding figure was 21% in the District of Columbia and the 41 states that provided this information, and in 1974 it was 11% in D.C. and the 43 states that provided data.

In the District of Columbia and the 46 states that reported age data to  the CDC in 2021, the majority of women who had abortions (57%) were in their 20s, while about three-in-ten (31%) were in their 30s. Teens ages 13 to 19 accounted for 8% of those who had abortions, while women ages 40 to 44 accounted for about 4%.

The vast majority of women who had abortions in 2021 were unmarried (87%), while married women accounted for 13%, according to  the CDC , which had data on this from 37 states.

In the District of Columbia, New York City (but not the rest of New York) and the 31 states that reported racial and ethnic data on abortion to  the CDC , 42% of all women who had abortions in 2021 were non-Hispanic Black, while 30% were non-Hispanic White, 22% were Hispanic and 6% were of other races.

Looking at abortion rates among those ages 15 to 44, there were 28.6 abortions per 1,000 non-Hispanic Black women in 2021; 12.3 abortions per 1,000 Hispanic women; 6.4 abortions per 1,000 non-Hispanic White women; and 9.2 abortions per 1,000 women of other races, the  CDC reported  from those same 31 states, D.C. and New York City.

For 57% of U.S. women who had induced abortions in 2021, it was the first time they had ever had one,  according to the CDC.  For nearly a quarter (24%), it was their second abortion. For 11% of women who had an abortion that year, it was their third, and for 8% it was their fourth or more. These CDC figures include data from 41 states and New York City, but not the rest of New York.

Nearly four-in-ten women who had abortions in 2021 (39%) had no previous live births at the time they had an abortion,  according to the CDC . Almost a quarter (24%) of women who had abortions in 2021 had one previous live birth, 20% had two previous live births, 10% had three, and 7% had four or more previous live births. These CDC figures include data from 41 states and New York City, but not the rest of New York.

The vast majority of abortions occur during the first trimester of a pregnancy. In 2021, 93% of abortions occurred during the first trimester – that is, at or before 13 weeks of gestation,  according to the CDC . An additional 6% occurred between 14 and 20 weeks of pregnancy, and about 1% were performed at 21 weeks or more of gestation. These CDC figures include data from 40 states and New York City, but not the rest of New York.

About 2% of all abortions in the U.S. involve some type of complication for the woman , according to an article in StatPearls, an online health care resource. “Most complications are considered minor such as pain, bleeding, infection and post-anesthesia complications,” according to the article.

The CDC calculates  case-fatality rates for women from induced abortions – that is, how many women die from abortion-related complications, for every 100,000 legal abortions that occur in the U.S .  The rate was lowest during the most recent period examined by the agency (2013 to 2020), when there were 0.45 deaths to women per 100,000 legal induced abortions. The case-fatality rate reported by the CDC was highest during the first period examined by the agency (1973 to 1977), when it was 2.09 deaths to women per 100,000 legal induced abortions. During the five-year periods in between, the figure ranged from 0.52 (from 1993 to 1997) to 0.78 (from 1978 to 1982).

The CDC calculates death rates by five-year and seven-year periods because of year-to-year fluctuation in the numbers and due to the relatively low number of women who die from legal induced abortions.

In 2020, the last year for which the CDC has information , six women in the U.S. died due to complications from induced abortions. Four women died in this way in 2019, two in 2018, and three in 2017. (These deaths all followed legal abortions.) Since 1990, the annual number of deaths among women due to legal induced abortion has ranged from two to 12.

The annual number of reported deaths from induced abortions (legal and illegal) tended to be higher in the 1980s, when it ranged from nine to 16, and from 1972 to 1979, when it ranged from 13 to 63. One driver of the decline was the drop in deaths from illegal abortions. There were 39 deaths from illegal abortions in 1972, the last full year before Roe v. Wade. The total fell to 19 in 1973 and to single digits or zero every year after that. (The number of deaths from legal abortions has also declined since then, though with some slight variation over time.)

The number of deaths from induced abortions was considerably higher in the 1960s than afterward. For instance, there were 119 deaths from induced abortions in  1963  and 99 in  1965 , according to reports by the then-U.S. Department of Health, Education and Welfare, a precursor to the Department of Health and Human Services. The CDC is a division of Health and Human Services.

Note: This is an update of a post originally published May 27, 2022, and first updated June 24, 2022.

Support for legal abortion is widespread in many countries, especially in Europe

Nearly a year after roe’s demise, americans’ views of abortion access increasingly vary by where they live, by more than two-to-one, americans say medication abortion should be legal in their state, most latinos say democrats care about them and work hard for their vote, far fewer say so of gop, positive views of supreme court decline sharply following abortion ruling, most popular.

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Guest Essay

Don’t Overlook the Power of the Civil Cases Against Donald Trump

By David Lat and Zachary B. Shemtob

Mr. Lat writes about the legal profession. Mr. Shemtob is a lawyer.

For months now, the country has been riveted by the four criminal cases against Donald Trump: the New York state case involving hush-money payments to an adult film star, the federal case involving classified documents, the Georgia election-interference case and the federal election-interference case. But some have been postponed or had important deadlines delayed. The only case with a realistic shot of producing a verdict before the election, the New York case, involves relatively minor charges of falsifying business records that are unlikely to result in any significant prison time . None of the other three are likely to be resolved before November.

It’s only the civil courts that have rendered judgments on Mr. Trump. In the first two months of 2024, Mr. Trump was hit with more than half a billion dollars in judgments in civil cases — around $450 million in the civil fraud case brought by the New York attorney general, Letitia James, and $83.3 million in the defamation case brought by the writer E. Jean Carroll.

For Trump opponents who want to see him behind bars, even a half-billion-dollar hit to his wallet might not carry the same satisfaction. But if, as Jonathan Mahler suggested in 2020, “visions of Donald Trump in an orange jumpsuit” turn out to be “more fantasy than reality,” civil justice has already shown itself to be a valuable tool for keeping him in check — and it may ultimately prove more successful in the long run at reining him in.

The legal system is not a monolith but a collection of different, interrelated systems. Although not as heralded as the criminal cases against Mr. Trump, civil suits have proved effective in imposing some measure of accountability on him, in situations where criminal prosecution might be too delayed, divisive or damaging to the law.

To understand why the civil system has been so successful against Mr. Trump, it’s important to understand some differences between civil and criminal justice. Civil actions have a lower standard of proof than criminal ones. In the civil fraud case, Justice Arthur Engoron applied a “ preponderance of the evidence ” standard, which required the attorney general to prove that it was more likely than not that Mr. Trump committed fraud. (Criminal cases require a jury or judge to decide beyond a reasonable doubt that the defendant committed a crime, a far higher standard.) As a result, it is much easier for those suing Mr. Trump in civil court to obtain favorable judgments.

These judgments can help — and already are helping — curb Mr. Trump’s behavior. Since Justice Engoron’s judgment in the civil fraud case, the monitor assigned to watch over the Trump Organization, the former federal judge Barbara Jones, has already identified deficiencies in the company’s financial reporting. After the second jury verdict in Ms. Carroll’s favor, Mr. Trump did not immediately return to attacking her, as he did in the past. (He remained relatively silent about her for several weeks before lashing out again in March.)

Returning to the White House will not insulate Mr. Trump from the consequences of civil litigation. As president, he could direct his attorney general to dismiss federal criminal charges against him or even attempt to pardon himself if convicted. He cannot do either with civil cases, which can proceed even against presidents. (In Clinton v. Jones , the Supreme Court held that a sitting president has no immunity from civil litigation for acts done before taking office and unrelated to the office. And as recently as December, the U.S. Court of Appeals for the District of Columbia Circuit made clear that even if the challenged acts took place during his presidency, when the president “acts in an unofficial, private capacity, he is subject to civil suits like any private citizen.”)

It may also be difficult for Mr. Trump to avoid the most serious penalties in a civil case. To appeal both recent civil judgments, Mr. Trump must come up with hundreds of millions of dollars in cash or secure a bond from an outside company. Although he managed to post a $91.6 million bond in the Carroll case, he initially encountered what his lawyers described as “ insurmountable difficulties ” in securing the half-billion-dollar bond he was originally ordered to post in the civil fraud case. An appeals court order last week cut that bond to $175 million — but if Mr. Trump cannot post this bond, Ms. James can start enforcing her judgment by seizing his beloved real estate or freezing his bank accounts. And even though it appears that he will be able to post the reduced bond, the damage done to his cash position and liquidity poses a significant threat to and limitation on his business operations.

Furthermore, through civil litigation, we could one day learn more about the inner workings of the Trump empire. Civil cases allow for broader discovery than criminal cases do. Ms. James, for instance, was able to investigate Mr. Trump’s businesses for almost three years before filing suit. And in the Carroll cases, Mr. Trump had to sit for depositions — an experience he seemed not to enjoy, according to Ms. Carroll’s attorney. There is no equivalent pretrial process in the criminal context, where defendants enjoy greater protections — most notably, the Fifth Amendment privilege against self-incrimination.

Finally, civil cases generally have fewer externalities or unintended consequences. There are typically not as many constitutional issues to navigate and less risk of the prosecution appearing political. As a result, civil cases may be less divisive for the nation. Considering the extreme political polarization in the United States right now, which the presidential election will probably only exacerbate, this advantage should not be underestimated.

David Lat ( @DavidLat ), a former federal law clerk and prosecutor, writes Original Jurisdiction , a newsletter about law and the legal profession. Zachary B. Shemtob is a former federal law clerk and practicing lawyer.

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here’s our email: [email protected] .

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An earlier version of this article misstated Arthur Engoron’s title. He is a justice on the New York State Supreme Court, not a judge.

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