example of empirical research in law

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39 Empirical Research in Law

John Baldwin is Professor of Judicial Administration in the Law School, University of Birmingham, and has been Director of the Institute of Judicial Administration since 1982. In the past thirty years, he has conducted a great number of empirical research projects, concerned in particular with the administration of justice, both criminal and civil. His latest book is Small Claims in County Courts in England and Wales: The Bargain Basement of Civil Justice? (Clarendon Press, 1997).

Gwynn Davis is Emeritus Professor and Senior Research Fellow attached to the Department of Law, University of Bristol. Over the past twenty-five years he has conducted over forty empirical research projects in the fields of family law and practice, criminal justice, and developments in the legal profession. He is the author of Partisans and Mediators (Clarendon Press, 1988) and, most recently, Child Support in Action (with Nick Wikeley and Richard Young, Hart, 1998).

Published: 18 September 2012
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This article considers the contribution to legal scholarship which has been and is being made by research strategies which fall under the broad heading of ‘empirical’. Empirical research in law involves the study, through direct methods rather than secondary sources, of the institutions, rules, procedures, and personnel of the law, with a view to understanding how they operate and what effects they have. It is not a synonym for ‘statistical’ or ‘factual’, and its intellectual depth and significance are not determined by the empirical label but can only be judged by reference to the same standards and the same yardsticks as would be applied to any other academic endeavour.

1 Introduction

I n this chapter we consider the contribution to legal scholarship which has been and is being made by research strategies which fall under the broad heading of ‘empirical’. Empirical research may be defined by reference to what it is not, as well as to what it is. It is not purely theoretical or doctrinal; it does not rest on an analysis of statute and decided cases; and it does not rely on secondary sources. What empiricists do, in one way or another, is to study the operations and the effects of the law. This leaves a great many decisions still to be taken. The focus of attention may be upon professional actors or it may be upon consumers; it may be upon the practice of law or upon measures of outcome; it may be upon legal processes which are in any event highly visible, even iconic, or it may be upon aspects of the law which normally remain subterranean; and finally it may involve collecting data on large numbers of cases, each subject to a predetermined scheme of categorization and reporting, or it may involve the painstaking examination of a relatively few interactions. All we can say, therefore, is that empirical research in law involves the study, through direct methods rather than secondary sources, of the institutions, rules, procedures, and personnel of the law, with a view to understanding how they operate and what effects they have. It is not a synonym for ‘statistical’ or ‘factual’, and its intellectual depth and significance are not determined by the empirical label but can only be judged by reference to the same standards and the same yardsticks as would be applied to any other academic endeavour.

There is one distinctive feature of the empirical research enterprise to which we should draw attention at the outset. This is that empirical research in law is not the preserve of the academic lawyer alone, but has attracted scholars from across the social sciences, especially sociologists, economists, and psychologists. This is desirable in principle since the workings of law and legal institutions have such profound social, economic, and political consequences that they ought not to be treated as the monopoly interest of lawyers. In keeping with this, we find that even long-established principles governing the analysis of legal reasoning have been subject to challenge by ‘realist’ scholars (Twining, 1973 ). However, it is principally through empirical study of the practice of law (especially of the preliminary and apparently more mundane aspects), and in studying the way legal processes and decisions impact upon the citizen, that the disciplines of sociology and, to a lesser degree, philosophy, psychology, and economics have entered into and enriched the study of law. This multidisciplinary research has, in turn, influenced many aspects of legal practice, albeit the insights gained may be conveyed imperfectly and in such a manner as barely to do justice to the complexity of the originating ideas. Even the rules and procedures of the law, which can seem arcane and specialist, reflect this influence.

Whilst ‘black letter’ legal scholars engage in painstaking analysis of decisions taken in the courts, especially at appellate level, other social science disciplines have contributed to a widespread recognition that the study of what law does can be as stimulating and intellectually challenging as the study of what the law says , and furthermore that traditional legal scholarship should not be regarded as a separate world but is itself enriched through a fuller understanding of law in its social context. So it is that many distinguished legal scholars, whilst they may not conduct empirical research themselves, engage with the evidence contributed by empiricists as to how law works and how it affects people's lives. Their definition of the scope of their subject now includes these elements.

2 Burgeoning Activity

In the period since World War II growing numbers of academic lawyers and social scientists have become interested in applying empirical research methods to the study of legal processes. It is possible to trace the origins of the empirical approach to a much earlier time, for example, to the work of Quetelet and Guerry in Continental Europe in the early nineteenth century (see Radzinowicz, 1966 : 29–42), but it was not until the advent of the so-called ‘realist’ school of jurisprudence in the middle of the last century that empirical enquiry became an accepted basis for legal analysis. Realist scholars maintained that judicial decisions were influenced by a host of personal and social factors, and that they ought therefore to be analysed not only with reference to statute, precedent, and established legal principle, but also with reference to judges' social backgrounds and political beliefs. Research on the politics of the judiciary conducted in the UK by Griffith ( 1997 ) and Robertson ( 1998 ), and in the United States by writers such as Levin ( 1977 ) and Estreicher and Sexton ( 1986 ), indicates that this tradition is still very much alive. The questions posed by these researchers, and their starting assumptions, have in turn influenced the work of other legal scholars who are not themselves inclined to pursue empirical strategies, thereby contributing to a reorientation in legal thinking and helping to change the nature of legal scholarship.

These developments have not, however, been uniform, either geographically or substantively. Despite the altered profile of legal research in the common law world following the widespread adoption of an empirical approach, there are some law schools—and some disciplines—within which empirical study continues to be regarded as a peripheral and perhaps even a downmarket interest, and certainly the dominant ethos remains doctrinal. However, virtually all law schools contain at least a smattering of empiricists, and some legal disciplines have been transformed through their influence. These include, most obviously, criminal law and criminal justice, family law, and parts of regulatory or ‘public’ law. There is also a burgeoning interest in empirical study of developments within the legal profession.

One factor not intrinsic to the subject itself which has stimulated the empirical approach within law schools has been the income which universities have been able to secure by this means. In the UK all universities have been driven to seek funds beyond their core government grant. Empirical research in, for example, criminal justice has proved attractive to potential sponsors—including government departments—and universities have found it in their interests to sustain empirical researchers who are capable of generating this income. Also, as external audit and monitoring of academic activities have become increasingly prominent in the drive to make these institutions more publicly accountable, the capacity to undertake large-scale empirical research has come to be regarded as one of the hallmarks of a diverse and academically vibrant law school. Similar processes occur in the United States, where the expenditures of public universities may be reviewed by state legislatures, and national grant agencies may require universities to account for their spending. This external auditing, coupled with pressures to extend universities' funding base, has contributed to a situation where empirical research is now central to the life of many law departments—something that would have been unthinkable a few decades ago.

These developments have gone hand in hand with the recruitment of non-lawyers into law schools, and also with collaboration across disciplinary and institutional boundaries. Indeed, it is striking how many prominent empirical researchers in law have a background in other disciplines. Some took their first degree in the humanities, but most were trained in one or other of the social sciences. It is probably fair to say that there has not been a coherent intellectual vision underlying this recruitment and collaboration. It has tended, rather, to be haphazard and serendipitous, reflecting perhaps the fact that scholars happened to be working in the same institution, or a chance convergence of interests. Many non-lawyers working in law schools were recruited in the first instance as contract researchers, employed on projects devised by academic lawyers. Sometimes the process of recruitment has worked the other way, with lawyers being approached by colleagues in the social sciences who were intent on examining some aspects of the legal process and who recognized the need for legal expertise on their research team.

Some of the non-lawyers recruited in this way have a firm grounding in the core discipline of sociology; others have some training in the methods of empirical investigation; but it is a very mixed picture, and many empirical research projects in law, even those with nominal ‘social scientists’ on board, cannot lay claim to intellectual roots located in another discipline. They may be referred to as ‘interdisciplinary’ but reflect only the most basic sociological precepts—for example, that rules do not necessarily determine behaviour, or that the conduct of actors and institutions cannot be understood simply by reference to their officially declared purposes.

So intellectual depth and coherence may sometimes be lacking, but over a period many of these non-lawyers have established a foothold in law schools, following which they have expanded the scope of their activities. For example, some now contribute as teachers of ‘fringe’ legal subjects—for example, criminology, sociology of law, socio-legal studies—which grew in popularity in the 1970s and 1980s as a means of extending a narrowly based law curriculum. Meanwhile, perhaps influenced by this cross-fertilization, some academic lawyers have themselves become interested in the study of legal processes, including the behaviour of professional actors, the strategies of bargaining and negotiation, and the degree to which legal endowments determine formal outcome. The sociological underpinning may be rudimentary, but some of these untutored investigators have proved themselves to be talented, intuitive commentators upon a world which they have chosen to view not as professional insiders but with an outsider's critical eye and an enthusiasm for the great themes exemplified in the practice of law.

This burgeoning interest in the study of legal processes has contributed to the creation of a number of research institutes dedicated to the empirical study of law and legal institutions. Whilst many of these centres are generalist in character, in which case the generic label ‘socio-legal’ may be applied to them, others are more specialist, devoted, for example, to the study of criminal justice, penology, judicial administration, civil justice, or family law. Some of these research centres are long-established, being supported by grants from charitable trusts or foundations, or by government departments; others lead a more precarious existence, being dependent upon their own host institution for financial support. Whilst only a minority of the academic researchers who engage in empirical research in law are employed within these centres, they are none the less important, both practically and symbolically, as demonstrating an institutional commitment to this kind of research and in raising its profile.

Similar observations might be made in relation to the publication of the results of empirical research. In some fields of law it is empirical researchers, rather than doctrinal scholars, who are cited most frequently in the legal literature. Some of the most venerable and prestigious legal journals have proved ready recipients of material with an empirical content. In addition, journals with a distinctly ‘socio-legal’ orientation (e.g. Law and Society Review and Journal of Law and Society ) have flourished in this period and provide an invaluable outlet for socio-legal scholarship, including the empirical. Finally, some mainstream legal publishers have committed themselves to producing substantial monographs which present the fruits of empirical enquiry on a range of legal topics, and we have even seen the advent of distinctively ‘socio-legal’ publishers (for example, Sage Publications and Hart Publishing), much of whose output has an empirical component.

3 Why is Empiricism Found in Some Fields of Law but Not Others?

Whilst some areas of law have been transformed by the empirical approach, others (such as contract) have remained largely untouched by it. It is important to ask why this is, and to consider the implications for legal scholarship generally. One possible starting-point is the observation that empirical strategies reflect the influence of non-lawyers, and it is probably the case that some legal subjects are more accessible to the non-lawyer, and hold greater intuitive appeal, than do others. The technical intricacies of the law relating to trusts or contract, for example, inevitably act as a deterrent to anyone outside the narrow band of legal scholars who have mastered the complexities of the subject. Criminal law is also complex, but it is not ‘law’ as such that has attracted the interest of criminologists and other criminal justice scholars. The focus of attention has tended to be upon legal institutions—for example, the police and prosecuting authorities—rather than upon legal doctrine, and some sociologists have contributed ground-breaking studies of these institutions which in turn have deepened our understanding of legal phenomena (see e.g. Reiner, 1992 ; Rock, 1993 ).

Accessibility to non-lawyers is important because the empirical approach is only likely to appeal, and to seem relevant, when law is conceived as an instrument of social policy, and this is not the way in which doctrinal lawyers are taught to approach their subject. The legal scholar who operates in the common law tradition is primarily interested not in the social policy of the law, or the translation of that policy into effective practice, but rather in the coherence and logic of legal argument applied to a given set of facts. Admittedly, most legal scholars in common law countries would say that their work is informed to some degree by the social sciences, and by theoretical or policy-orientated writing. However, that does not mean that they themselves engage in empirical research, or even that they consume the fruits of others' investigations. Criminal justice and, to a lesser extent, the tort system are perhaps the two areas in which there has been the most effective cross-over—see, for example, the work of Dewees et al . (1996) ; the series of surveys conducted in the 1970s for the Pearson Commission (Royal Commission, 1978 ), and by the Oxford Centre for Socio-Legal Studies; Genn's work on tort settlements (1987); and research sponsored by the Rand Corporation in the US (Hensler et al ., 2000 ).

Despite this important work, there lies buried deep within the traditions of the common law an alternative view of the legal enterprise, one in which ‘policy’ emerges through the application of legal precedent. This is not to say that the academic lawyer is uninterested in broader themes, or in empirical investigation as a means of exploring those themes, but such exploration will tend to be regarded as a parallel activity, one that is undertaken by scholars from other disciplines. In fact, there are many fields of law in which academic lawyers tend not to conduct empirical research, but where their approach is none the less socio-legal in the sense that they draw upon a parallel literature which addresses many of the same issues, but from a sociological or economic perspective. Company law provides one example. This is a field in which there are strong interdisciplinary links and in which the fruits of empirical research are routinely employed by academic lawyers to enrich their view of the subject. There is, for example, a substantial empirical literature on the economic effects of takeovers. However, this empirical investigation tends not to be something that company lawyers carry forward themselves; nor is it defined as falling within the ‘law’ component of the subject.

There are other fields however—and criminal justice is the most obvious example—in which there is no separation of the legal and social policy dimensions and therefore no gulf to bridge. This is partly because the legal dimension is reasonably accessible to the non-lawyer, and partly because lawyers working in these areas tend themselves to be enthused by issues of policy and practice. So it is that in criminal justice (and to a lesser extent in tort, public and family law) academic lawyers have tended to address many of the same issues as social scientists and they have a literature which is at the very least overlapping. They also tend to regard empirical investigation as an essential tool, and one that confers academic prestige and other benefits upon its most skilled and inventive practitioners.

4 Main Questions and Preoccupations

To say that empirical researchers are interested in issues of social policy which are reflected in the law, and in the implementation of that policy through legal practice, leaves much still to be explained about the nature of empirical work. One possible starting-point is with the observation that empiricists aim to describe the legal world as it is, not as it is meant to be, with many studies emphasizing the disparity between textbook depictions of legal and judicial processes and their everyday reality. It was Roscoe Pound in the early years of the last century who first drew the distinction between ‘law in the books’ and ‘law in action’, the essential point being that the study of statute and decided cases is not sufficient as a means of discovering how legal institutions and legal practitioners conduct themselves (Pound, 1910 ). Many aspects of legal process are characterized by the exercise of discretion, and by the development of working practices which do not figure in any account of legal rules. The settlement culture which pervades the civil courts, and plea bargaining within criminal justice, are two examples of this.

This ‘gap’ between legal texts and the day-to-day reality of legal practice has become a preoccupation of the empirical researcher. Studies have been conducted which demonstrate, for example, that the way in which police officers arrive at arrest and detention decisions is often at odds with legal rules; that decision-making in the courtroom reflects the attitudes and prejudices of the judge who hears the case as well as the clinical application of the law; and that the way in which lawyers dispose of their cases frequently departs from legally prescribed procedures. The 1960s, in particular, witnessed a considerable reorientation of criminal justice research, with studies such as those conducted by Piliavin and Briar ( 1964 ), Skolnick ( 1966 ), and Blumberg ( 1967 ) focusing on decision-making at different stages of the justice process. These studies revealed that police officers' arrest decisions reflected their assessments of the character and demeanour of the individuals concerned; that informal interactions between defence and prosecution lawyers were commonly at odds with legally prescribed procedures; and that courtroom decisions reflected, at least in part, the personal attributes of the judge. A typical observation was that of Piliavin and Briar ( 1964 : 214), who concluded that ‘[t]he official delinquent, as distinguished from the juvenile who simply commits a delinquent act, is the product of a social judgment … he is a delinquent because someone in authority has defined him as one, often on the basis of the public face he has presented to officials rather than of the kind of offence he has committed’. These studies represent landmarks in the empirical investigation of legal procedures, and some indication of the scale of the endeavour is provided by the criminal justice bibliography compiled by Radzinowicz and Hood in 1976 which gives 10,000 references to criminal justice research and runs to 400 pages. As those of us embarking on research careers in this period appreciate, this literature had a profound impact, serving to inspire empirical researchers throughout the common law world.

One abiding characteristic of this research was its critical edge, with the authors generally taking a negative view of legal actors' casual approach to rule observance. The disparity between law in the books and law in action was most evident in relation to pre-trial criminal procedures, with legal practitioners being seen to concentrate their energies on avoiding trial rather than preparing for it. The US literature on plea bargaining, focusing upon the inducements offered to criminal defendants to forgo their right to jury trial, would itself fill several shelves in any law library, with most of this work being critical in tone.

As far as civil justice is concerned, empirical research has developed more slowly, but it has still proved influential in certain areas, with US scholars again leading the way. These civil justice studies have been concerned with matters such as case settlement procedures (Rosenberg, 1964 ); access to justice (Cappelletti and Garth, 1978 ); small claims adjudication (Yngvesson and Hennessey, 1975 ); and developments in the legal profession (Abel, 1989 ). As with criminal justice research, many of these authors have highlighted the disparity between formal rules and textbook accounts of process and procedures on the one hand and the reality of legal practice on the other.

The now commonplace observation that the standard means of disposal of both criminal and civil cases is through informal out-of-court negotiation and settlement is derived principally from this research. Although still not reflected in some textbooks, the literature on plea bargaining, and on the settlement of civil claims, has contributed greatly to what is now a general acceptance of the view that in order to understand the justice process it is necessary to observe it at every stage and not to focus exclusively on set-piece courtroom dramas. It is also vital to tap the experience of lay actors and not to view the operation of legal processes solely from the perspective of the powerful and the privileged.

This brings us to a second characteristic of empirical research, which is that it tends to give considerable prominence to the voice of the consumer of legal services. In the best work of this kind, the consumer (whether litigant, witness, victim, or defendant) is not regarded as the sole arbiter of the value and effectiveness of legal services, but the consumer perspective is seen as having its own validity, which means that it can be a useful corrective both to some rule-based accounts and to the voice of the professional practitioner. Some of the most influential research of this kind has achieved its impact precisely because it has demonstrated that the experience of those on the receiving end of legal processes was not understood or adequately represented by the legal practitioners whose job it was to safeguard their interests and who saw themselves as doing this in good faith (Baldwin and McConville, 1977 ; Felstiner et al ., 1981 ; Davis et al ., 1994 ). So the view that legal processes can only be described and evaluated by practitioners and professional commentators—or even that they are best evaluated by these professional insiders—is one that has been effectively challenged through empirical research.

A third feature of the empirical approach is that it tends to be focused upon lower level and preliminary legal processes. It is true that some empirical legal researchers have been concerned with decision-making at more rarified levels, but the main focus tends to be upon those parts of the legal process which are high volume, routine, largely hidden from public view, and which are dealt with cursorily if at all within legal texts. This focus upon routine decision-making is characteristic of empirical studies in both criminal and civil justice. So also is the preoccupation with the early stages of legal proceedings—the so-called ‘low visibility’ part—this being the point at which the future trajectory of both civil and criminal cases is often determined. Empirical researchers have examined, for example, how the police make decisions on the streets; the processes by which the police and prosecuting authorities decide whether an alleged offender should be prosecuted, and on what charge; the settlement strategies of civil and criminal litigators; the significance of procedure, including the part played by preliminary hearings in civil cases, and why these cases typically settle at a late stage. Other studies have examined the interactions between lawyers and their clients, and the impact of lawyers' work management strategies. None of these subjects is intrinsically more (or less) interesting than the preoccupations of the doctrinal legal scholar, but they reflect a view that the practice of law is an important subject for study in its own right, and that this practice needs to be observed in all its ‘minute particulars’ if it is to be accurately described and understood.

Low visibility is also a feature of the mechanisms intended to ensure that organizations comply with the regulations governing their practice, and these regulatory regimes have provided another target for empirical study. This work, which has been conducted in many parts of the common law world, has focused upon the role of inspectorates of various kinds and on the extent to which organizations comply with their own regulatory standards. The studies cover, for example, the work of factory inspectors (Baldwin, 1995 ), environmental health officers (Hutter, 1988 ), occupational health and safety officers (Gunningham and Johnstone, 1999 ), and business and financial services regulators (Grabosky and Braithwaite, 1986 ; Black, 1997 ). A unifying theme of this literature is the significance of negotiation in delivering compliance.

Another ‘driver’ of empirical research, prominent under the recent New Labour administrations in the UK, is the (laudable) impulse to monitor legal innovation by government and to evaluate its consequences. Monitoring and evaluating innovation have provided a great deal of work for the socio-legal community, which perhaps explains why it is seldom questioned, but we have certain reservations about it as the dominant empirical research model. The first arises from the fact that this research may be used essentially for presentational purposes. It is hard to imagine that the government department which commissions the research is seeking totally independent assessment and conclusions: to some degree at least the policy is already formed. The tensions around this issue are reflected in the tussles which occasionally occur between government departments and researchers over the right to publish (see below). A second reservation concerns the ‘reformist’ nature of evaluative research. Neither the practitioners who participate in the initiative nor the researchers who study it can claim that they are addressing the fundamental causes of social problems. As Pawson and Tilley put it: ‘Evaluations are … patently petty political’. In other words, government-sponsored initiatives treat certain social and political configurations as given, so that ‘the programmes which get evaluated are directed at reducing problems with systems, or ensuring the better operation of those systems’ (Pawson and Tilley, 1997 : 12). A third and final reservation concerns the short-term agenda that inevitably drives empirical research on this model. The agenda is that of the commissioning government department—which means the minister and his or her civil servants—and their thinking will tend to be dominated by immediate political considerations.

A distinctive form of evaluative research is that which sails under the ‘what works?’ banner. This question, which can of course be asked of almost anything, has come to function as a label applied to interventions which are geared to changing attitudes or behaviour. It tends to be asked especially of government-sponsored initiatives which, it is hoped, will have therapeutic effects—say, in diverting offenders from whatever patterns of behaviour (such as drug-taking) or whatever modes of thought (such as denying responsibility for their own actions) are deemed to underlie their offending behaviour. Research on this model is designed to test the cost-effectiveness of the intervention, for example, by examining changes in reconviction rates, or by interviewing the relevant population in order to gauge changes in behaviour and attitude.

Whilst each of the above is an immediately recognizable research stimulus, and between them they underpin much of the empirical research undertaken within law schools, it would be a mistake to imagine that empirical research necessarily falls into any one of these categories. Indeed, it is possible to conclude that the very best work transcends all of them, aiming as it does at a fundamental re-evaluation of the operation of a given area of law. In that sense it may not be designed to measure anything in particular, but rather to assist our understanding of the way law works, including the contribution it makes to addressing social problems and its impact upon the citizen. Accordingly, it will tend to explore many of the same themes as underpin the finest works of literature, or the great political debates. It may to some extent engage with the social policy agenda of the government of the day, but it will not be defined by it.

5 Research Methods

There are a host of different approaches to the task of gathering empirical research data, each reflecting different assumptions concerning what questions are worth asking, and what might constitute valid answers to those questions. Whilst the choice of research methods will reflect the researcher's views as to what knowledge is worth acquiring, those views will not necessarily be made explicit. Often they will be implicit in the choice of method and in the way in which evidence is presented. The natural scientist's preoccupation with rigorous scientific method is seldom paralleled in socio-legal research, most of which is conducted by academics who were not educated within that tradition and who, in any event, may doubt whether those techniques would serve their purpose. Thus, for example, the randomized controlled trial, which has long been the gold standard in medical research, hardly figures in research into legal processes. This is partly on ethical grounds, but also because empirical researchers in law are seldom concerned to explore the long-term impact of one specific intervention.

Much empirical legal research is descriptive in character, the aim being to examine the operation of legal processes and to demonstrate how these are perceived by all the parties concerned, perhaps especially by ‘consumers’ who do not normally have much opportunity to explain how the legal world feels to them. But empirical legal research is at least as much about providing explanations for social phenomena as it is descriptive. These explanations vary considerably in the level of sociological sophistication that is brought to bear, and readers may on occasion be tempted to ask whether the implicit value judgements which lie buried within them are ones which they would share. None the less, an explanation of sorts is being offered. Other empirical research is self-consciously evaluative, in which case there may be explicit design and delivery requirements which can be examined with a fair degree of objectivity.

As far as technical research skills are concerned, it is likely that few empirical legal researchers will have done more than dip into the various methodological texts that have been published in recent years. Research skills are picked up by observing more experienced colleagues, and there is nothing particularly complex or technical about the methods employed. This is not to say that these researchers lack skill, only that their skill does not lie in a mastery of research techniques. Essentially, it lies in their understanding of the fundamental purposes of the legal enterprise and their ability to deploy Wright Mills's ‘sociological imagination’ in order to re-describe and re-evaluate it. The creativity lies in marrying some aspects of the insider's legal knowledge with the sociologist's ability to discern the wider themes underlying the individual dramas of the law—or, in Wright Mills's terms, to discern the public issue within the private trouble (Wright Mills, 1959 ). The methods by which this is achieved—observing, interviewing, perusing documents—can have a somewhat homespun or improvised feel to them, but this is not necessarily a matter for regret, provided that the methods employed are appropriate as a means of exploring the issues which the researchers say they are trying to explore.

It is important, therefore, to recognize that empirical research in law is a creative process—as, probably, we would find is also true of research in the physical sciences, if only we understood it better. The difference is that one tends not to find within legal research any equivalent of the pure science model of hypothesis formulation, testing, and re-testing, leading to a finding which can then be further explored by means of other studies. Legal researchers seldom identify specific hypotheses which they mean to test, nor is there much enthusiasm for replicating earlier work. This is for three reasons: first, as we have said, most socio-legal research is essentially descriptive and explanatory, rather than evaluative; secondly, it is understood that the researcher's own value judgements lie buried within the research—so there is little enthusiasm for re-testing a ‘finding’ which everyone understands to be subjective to some degree; and thirdly, because much socio-legal scholarship tends to be focused upon process rather than outcome, it is only to a limited extent that its practitioners see themselves as building on earlier studies, thereby playing their part in a gradual accumulation of knowledge. The metaphor of ‘pushing back the frontiers’ tends to be employed only ironically within the socio-legal world. Rather, research is seen as a means of developing more satisfying descriptions and explanations of complex institutions and interactions—in which case it is not essential to replicate earlier methodologies, and indeed it can seem rather unexciting to do so.

5.1 Qualitative versus Quantitative Methods

The main distinction within empirical legal research is that between ‘qualitative’ and ‘quantitative’ approaches. The former involves an attempted in-depth exploration of legal processes, typically focusing on a modest number of interactions but viewing these from a variety of perspectives and perhaps over time. The strength of this approach lies in its capacity to reflect the complexity of legal processes, and the complexity of the relationship between process and outcome. It is also well suited to exploring the meaning which people place on legal events (Miles and Huberman, 1994 ). The research instruments (interview schedules, checklists, and so on) may be rudimentary, perhaps comprising no more than a few prompts for the observer, or reminders to the interviewer. This is a research style favoured by many experienced socio-legal researchers, including the authors, although it has to be accepted that the approach may raise questions as to the researchers' underlying attitudes and assumptions, and hence the validity and generalizability of their findings.

Qualitative research calls for fine judgement in deciding what significance to attach to elements of practice and to fleeting interactions within the individual dramas of the law, and this is a potential weakness as well as a strength. Qualitative researchers would claim that the accounts which they offer reflect the entirety of their research evidence, but this can never be proved given that it is not feasible to present more than snippets of that evidence, essentially for illustrative purposes in any published work. This is a research style in which the researchers first inform themselves and then seek to convey the fruits of that understanding to others. It follows that qualitative research, if it is to be of any value, cannot be conducted at second hand, for example, by a survey company; the aim is to improve understanding, not to gather evidence, and that improved understanding can only be achieved through immersion.

Quantitative approaches appear, at least on the face of it, to conform more closely to the pure scientific model. The objectives will usually be clearly identifiable within the research instruments. The study will be carried out on a large scale, possibly employing the services of a survey company such as the National Centre for Social Research, and the information recorded may allow complex statistical analysis. This approach has been adopted, for example, in the large-scale victimization studies that are now routinely conducted in very many countries in an effort to provide more reliable measures of crime than are to be found in official police records (e.g. van Dijk and Mayhew, 1997 ). So, provided the research questions can be answered by means of this kind of standardized interrogation, the social survey may, if well designed (i.e. with efficient sampling techniques and intelligible, discriminating questions), reveal much about the weight of experience and/or opinion within a given population. Another example of what can be achieved by this approach is the community survey designed to explore the extent of unmet legal need. A recent study of this type in the UK was the investigation conducted by Hazel Genn and the National Centre for Social Research into people's experience of ‘justiciable problems’ (Genn, 1999 ).

At the same time one should acknowledge the limitations of quantitative research methods, at least in respect of certain topics. First, such methods are not necessarily any more ‘objective’ than qualitative approaches in that the researcher's prior assumptions will, inevitably, be embedded in the design of questionnaires and other research instruments. Furthermore, there is no possibility of these biases being corrected as the research proceeds, as is at least possible when researchers are trying to educate themselves in the nuances of a subject. Secondly, surveys are only useful when they focus upon issues concerning which the informants have experience and can respond authoritatively. There is no point asking people about matters of which they have no direct experience and therefore nothing to offer beyond prejudice and received opinion. Yet that is sometimes done, and the resulting ‘findings’ reported as if they were of some value.

Increasingly, empirical legal researchers are employing a combination of qualitative and quantitative techniques, seeking to harness the strengths of both. For example, in-depth interviews may be conducted with comparatively few informants where there is need to explain the results of an earlier survey. Essentially, the data collection methods should reflect the focus of the research. A combination of qualitative and quantitative approaches may or may not offer greater ‘explanatory completeness’ (Pawson and Tilley, 1997 ). One instance where this was achieved was in a recent study of bail decisions and plea bargaining (Kellough and Wortley, 2002 ) which employed a variety of methods, including tracking some 1,800 criminal cases through the courts and, in methodological contrast, face-to-face interviews with a limited number of offenders. The various data sources allowed for statistical identification of the factors that influence bail decisions, but the study also gave insight into the various ways that remands in custody may affect plea decisions and the prosecution's decision to withdraw charges.

5.2 Research Access

One difficulty which characteristically faces the socio-legal researcher is that of securing access to data. Even if funding is secured, access may be denied, or permission to study an organization's work will be granted only on restrictive conditions. There are considerable differences between institutions in this respect, with some having proved more open to research than others. For example, the police have been quite receptive to external research over the years, even though some of that research has been highly critical of their practice, but the judiciary (especially at the most senior levels) have been less welcoming. It is important to recognize, therefore, that empirical research may be conducted in circumstances that are far from ideal. Legal researchers may find that it is not possible for them to examine certain subjects, or not in the way that they would wish. Studies of sensitive subjects will often proceed on the basis of second-best approaches. Other topics are ruled out altogether, the jury's deliberations being one obvious example.

There are two ways of viewing this admittedly frustrating state of affairs. The first is that powerful institutional forces are conspiring to thwart independent academic enquiry. The second is that some empirical researchers are liable to abuse the privilege of research access in order to attack groups or institutions which they have long viewed in a critical light. We think both explanations contain an element of truth. Some legal institutions are beleaguered and respond in a defensive manner; but also, empirical researchers may pursue an agenda which is not rooted in their data, or not in the data upon which they claim to rely, and this in turn may make it more difficult for other members of the academic community to secure the access they need. Researchers who are granted privileged access to confidential material bear a heavy responsibility—first, to respect that confidentiality, but secondly, just as important, to represent their evidence faithfully, in all its complexity. Equally, major legal institutions ought to accept, as most, including the police, seem to do, that they are publicly accountable. One aspect of that accountability lies in their opening themselves up to reputable academic study.

6 The Relationship to Theory

Few empirical researchers in law make any claim that their research is located within some overarching theoretical framework derived from one of the core social science disciplines. In most legal research monographs there is seldom more than a polite nod in the direction of those explanatory frameworks, and when that happens there all too often appears to be no meaningful connection between the theory and the empirical investigation. Of course, all research is influenced by ideas about how groups and institutions function, or about ways in which individuals react to certain life circumstances. However, it is not clear whether we should refer to this knowledge as ‘theory’ or whether we should regard it as part of the store of wisdom which (to some degree) we all acquire as we move through life. Perhaps we should allow that researchers draw on a mix of folk wisdom and academic insights.

Unfortunately, those academic insights may be as contentious as the folk wisdom, so it is helpful if academic researchers are clear about their own core beliefs, and if they are prepared to articulate these. As we have already observed, this seldom happens, and the reader is left attempting to fathom the researcher's ideological stance and starting assumptions. We each have our favoured explanations of motive and behaviour, and there is virtually no research in law which is uninfluenced by this tendency to favour some explanations over others, and to apply a normative colouring to whatever actions are being described.

These sympathies and antipathies, favoured explanations and discarded explanations, underpin all socio-legal writing. Commonly, they will be reflected in the researchers' choice of subject, and even more so in their choice of informants, although it is not unknown for researchers to rely upon interviews with one set of respondents in order to construct accounts which are critical of almost every aspect of that group's professional practice. We should concede that ‘bias’ is inevitable in the sense that the researcher will feel more sympathy towards some informants than others, and will favour some explanations over others. It is impossible to determine what would constitute a neutral stance when interviewing or observing a given group of actors—for example, court officials, legal practitioners, police officers, judges, divorcing parents, criminal defendants, prisoners, or civil litigants. The question—‘whose side are you on?’—is one that is inevitably raised (Becker, 1967 ). The lesson that we draw from this is that it is important for the empiricist to be enthusiastic about evidence, and to want to be surprised by that evidence in the interests of developing an account which offers fresh insight. So far as possible ‘theory’ should grow out of the research data; it should not be the other way around. If the story of the research does not emerge from the data, one may question the decision to undertake empirical research in the first place. The research becomes little more than a cosmetic exercise, designed to add weight to an already well-honed series of descriptions and explanations.

Having said that, describing the world ‘as it is’ is inevitably a problematic undertaking, and empirical researchers do not begin with a clean slate. They will have, from the outset, at least a rudimentary conceptual framework. This will include the main actors and activities to be examined, key relationships to be explored, and questions that they hope to answer. This early conceptualizing of the subject-matter is necessary in order to give clarity and focus, and also to avoid indiscriminate data collection (Miles and Huberman, 1994 ). This, however, is only the beginning of the interpretative task. The essence of the empirical approach lies not only in collecting relevant materials through the development of appropriate research techniques, but in making sense of that material as it is being collected. All interviews, all observations, and all documentary materials have to be given meaning by the researcher. It is only if empirical research is understood to be interpretative that it has some prospect of being anything other than ephemeral. If, on the other hand, empirical research is conceived as an attempt to keep pace with the latest changes in law and procedure in order to describe the impact of these changes, then it may be of some immediate interest to policy-makers and practitioners but that interest will not be sustained. This is because legal processes tend not to change very rapidly, and in many of their more fundamental aspects they hardly change at all. Accordingly, empirical research is devalued if it is regarded primarily as a means of monitoring and evaluating new initiatives. It ought to be seen, instead, as a means of exploring those aspects of law and legal practice which are enduring and which lie at the heart of the enterprise.

The following passage, taken from the final paragraph of a research monograph exploring one ‘hidden’ area of family law—the settlement of financial disputes by the parties' lawyers—is the kind of research output we have in mind:

For the foreseeable future some elements at least of these disputes will continue to be handled by lawyers. But the system was not designed to cope with the present volume of cases and, unsurprisingly, it has many weaknesses. We have identified, in particular: the very great premium which is placed on the energy and inventiveness of individual practitioners; the ponderousness of the process and the opportunity, if either party is so minded, to create delay at every stage; the failure, in many instances, to rectify the bargaining endowments conferred by an ability to conceal resources or to tolerate a postponed resolution; the way in which a settlement culture can override traditional legal values so that the negotiation process becomes no more than a search for compromise; the failure to distinguish between two distinct objectives—advancing the process and promoting settlement—so that these are conflated; and, finally, courts' reluctance or inability to enforce their own procedural orders. (Davis et al ., 1994 : 273)

This analysis is not located within some overarching sociological theory; but nor is it ephemeral. The researchers have drawn on the evidence of a large number of interactions in order to capture, as they see it, the fundamentals of legal practice in this area. It is questionable whether empiricism can deliver more than this. These authors might have attempted a different level of explanation, perhaps one that was rooted in an overarching theory of professionalism, but it would have been difficult for them in that case to assert that the explanation emerged from their data.

7 Influence upon Law and Social Policy

Given that empirical researchers are interested in the social policy of the law, an obvious question arises as to whether empirical research findings do indeed exert some influence, partly in relation to reform of the substantive law, but also in relation to practice and procedure. It has been claimed that the direction of family law, in particular, has been over-influenced by empirical research findings of dubious validity (Deech, 1984 ), and no doubt the same point could be made in relation to other areas of legal practice. However, the more generally accepted view appears to be that the relationship between research and policy, or research and law reform, is extremely tenuous (Thomas, 1985 ). This is despite the fact that much empirical research is, either directly or indirectly, state-funded, in which case it might be anticipated that researchers would wish to address issues that are of immediate concern to government, and furthermore that government would be interested in this research evidence and would use it to inform their policies in relation to legal and social issues. We suspect that both sides to this ‘contract’ would like to believe that this is what happens. On the government side, successive administrations of every hue have been heard to claim that their policies are ‘evidence-led’, whilst empirical researchers, for their part, like to feel that they are of some use, and that they exert influence. Many engage in the business of empirical research because they want to bring about social change.

We suspect that the limitations of the empirical approach lie at least as much with the paucity of imagination displayed by some researchers as they do with a slavish adherence to the government's agenda. The relationship between the government ‘customer’ and the empirical research ‘contractor’ has, over the past forty years, proved something of a disappointment to both. Social policy research has struggled to exert its influence alongside all the other forces bearing upon government, and researchers whose principal motivation has been to influence policy often feel, if not ignored, then manipulated and abused. This is despite the fact that it sometimes suits both sides to pretend that the research in question has made a difference.

Difficulties in the relationship between researchers and policy-makers are perhaps most apparent in disputes which centre on the publication of results. The problems can be acute when the research findings are embarrassing to the government or are believed to offer a distorted and misleading view of the activities of a particular organization. Hostility to publication plans is by no means uncommon, and both authors have experienced it on occasions. A challenge to research findings, whether in private or in public, is invariably an unpleasant experience for the researcher in question and can lead to severe personal and professional difficulties (Baldwin and McConville, 1977 ). When government, or government agencies, attempt to suppress publication of research findings, the academic community has an obligation to resist. This is why contractual arrangements entered into when research is commissioned need to be considered very carefully lest the right to publish be compromised. Government will often find it uncomfortable to allow publication, but that discomfort cannot be avoided. Of course, those who are on the receiving end of research, whether as subjects or as funders, have a right to see drafts prior to publication, and they may wish to comment in uncompromising terms or ultimately to distance themselves from the research findings as these are presented. But, if the integrity and independence of the researcher are to be maintained, that right must fall short of the power of veto.

Partly this problem reflects the way in which research is commissioned in the first place. Government is not always skilled in determining what it needs to know and what kind of research might fit the bill. In fact, research can be commissioned for a number of disreputable, as well as reputable reasons. The work of Thomas and Weiss suggests that research in law and the social sciences is employed for a number of purposes that cannot be openly acknowledged (Thomas, 1985 ; Weiss, 1978 ). These are some of the reasons suggested by Carol Weiss:

as political ammunition;

to delay action;

to avoid taking responsibility for a decision;

to win kudos for a successful innovation;

to discredit a disliked policy;

to maintain the prestige of a government department by supporting well-regarded researchers.

Experienced socio-legal researchers will recognize most of the above list, and they could probably add other equally disreputable items from their own experience. However, it seems to us inevitable that the relationship between research and policy will seldom be direct or straightforward. Government will always use research to serve political ends, and it will likewise do its best to ignore those findings which are politically inconvenient. We would prefer to emphasize independence rather than influence. The key for empirical researchers is to maintain their independence of government (and of any other research customers) in order fully to do justice to the research evidence.

This independence needs to be jealously guarded by the academic community, and for the most part we believe it is. Empirical research in law is often critical of existing policy and practice, and that is appropriate since the research ‘story’ ought to provide an alternative to the accounts which emerge from government or, indeed, from practitioners. Empirical research, in other words, has a debunking tendency. This, in turn, may bring it under attack (sometimes in private, less often in public) from members of the legal profession, the judiciary, or government representatives. This is what one comes to expect. There is inevitably some tension within these relationships, so it is important that those agencies which are liable to be criticized (or to be presented in a light not entirely consistent with the way they choose to present themselves) accept that this is a legitimate academic function and one that is, ultimately, in the public interest.

This brings us back to influence. Most empirical researchers of any experience appreciate that research findings which reinforce the prevailing thinking of the commissioning government department are more likely to be referred to, and therefore to appear to be influential, than are those which run counter to it. Where the research evidence points in an uncomfortable direction it is not difficult for policy-makers to ignore it.

To gain some impression of how limited the influence of researchers can be if their results do not suit the prevailing mood, and how easy it is for policy-makers to disregard their work if they are so minded, one need only consider what has happened in the criminal justice arena in the past twenty years. Punitive law and order policies have been adopted by many Western governments, notwithstanding the empirical research evidence that points to the dangers of such policies. The overwhelming weight of the research and writing produced by generations of criminologists, most of it funded by governments, points to the ineffectiveness of ‘crackdowns’ on crime, ‘wars’ on drugs, tough deterrent sentencing, and the like. Yet none of this evidence has succeeded in dissuading governments from following their own tough ‘law and order’ policies. The fact is that policies based on ‘commonsense’ solutions to crime problems (mandatory sentencing, deterrent sentencing for drug offenders, more liberal use of imprisonment, and so on) carry great electoral appeal, however much they may fly in the fact of empirical research findings. Garland observes that ‘Policy measures [in the United States and the UK] are constructed in ways that appear to value political advantage and public opinion over the views of experts and the evidence of research’ (2001: 13), and he offers a long list of contemporary penal policies (including ‘prison works’, ‘three-strikes-and-you're-out’, ‘no frills prisons’, and ‘zero-tolerance’) to illustrate the point. Nor have protests about these policies in criminological journals cut much ice with policy-makers. It is instructive in this context to consider the reaction of a UK government minister in the 1980s to a research finding that prosecutors at a certain court were sometimes able to secure the listing of a particular trial before a particular judge. He responded: ‘It does not happen, and if it does, it should not’. Another frequent response by policy-makers who wish to dismiss unpalatable research findings is to concede that the research may have been accurate at the time it was carried out, but to assert that practice has since changed in some fundamental way, so that the researchers' conclusions are no longer valid.

We regard this evidence of tension between government and members of the academic community as an inevitable consequence of the proximity of some research to the political process: researchers cannot control the reception of their work by policy-makers, and should not become too agitated if that reception is not as they would wish. Policies that carry electoral appeal, or which are congenial to ministers for ideological reasons, are routinely pursued in the face of contrary advice from academic experts who contend that the available evidence points in a different direction.

The conclusion that we draw from this is not necessarily dispiriting. It is inevitable that the development of social policy in contentious areas will reflect prejudice, gut instinct, and vested interests, as well as the accumulated wisdom of practitioners and researchers over many years. The fact that this research wisdom is ‘accumulated’, and not just the product of the latest monitoring exercise, is in our view a key feature. This is consistent with Weiss's ‘enlightenment’ or ‘knowledge creep’ theory, under which the most common mode of research use ‘is the diffuse and undirected infiltration of research ideas into [decision-makers'] understanding of the world’ (Weiss, 1978 ).

It is essential, therefore, to take a long-term view of the profound social issues which confront policy-makers in, for example, criminal justice and penology. We would prefer empirical researchers also to take this long-term view, in order to address the fundamentals of law and practice in these areas. Of course there are tensions here with the political imperatives with which ministers and civil servants must wrestle on a daily basis. But it would be deeply regrettable were empirical research in law to be conceived as a series of short-term evaluations. One has to hope that government will continue to sponsor research, whilst at the same time accepting that it is not well placed to determine what kinds of evidence will ultimately prove to be of greatest value. This calls for a continuing dialogue between researchers and policy-makers, and a constructive spirit on the part of both.

Abel, R. L. ( 1989 ). Criminal Lawyers , New York: Oxford University Press.

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We are indebted to Julie Vennard, Julian Rivers, and John Parkinson, each of whom made helpful suggestions from within their own fields of expertise, and to Anne Griffiths for secretarial support.

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Empirical Legal Research Resources

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Selected Treatises: How to Conduct Empirical Legal Research

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This page contains a selected list of major treatises on conducting empirical legal research, as well as a selection of books that demonstrate how some scholars use empirical legal research in their writing. You may find more works on the topic using subject headings, such as "legal research -- methodology," in Searchworks , the Stanford Library catalog.

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Preeminent Treatise

In this section, we briefly describe the empirical research process. This description is a summary based on a few sources that together provide much more detail about the process than this guide:

Lee Epstein & Gary King, The Rules of Inference, 69 U. Chi. L. Rev. 1 (2002)
Lee Epstein & Andrew D. Martin, An Introduction to Empirical Legal Research (2014)
Mark A. Hall & Ronald F. Wright, Systematic Content Analysis of Judicial Opinions, 96 Calif. L. Rev. 63 (2008).
Sarah E. Ryan, Teaching Empirical Legal Research Study Design: Topics & Resources, 23 Perspectives 152 (2015)

Other detailed sources on methodology are presented in the Resources on Empirical Research Methodology portion of this guide.

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leiden lawmethods portal

Writing an empirical legal article.

Last update: April 12, 2022

In comparison to a typical article in a law journal that you are perhaps used to, empirical articles – especially those published in social science journals – have a more standardized structure. Such articles typically start with an introduction, then discuss the methods used, subsequently report the findings in a results section, and ultimately discuss the findings and implications in a discussion section (see Figure 1). Below, we will briefly discuss some key points to take into consideration when writing an empirical legal article.

Writing the introduction

In several respects, the introduction of an empirical article mimics that of a typical law journal article. For example, the introduction is used to stress the importance of the research and to entice the reader to continue reading. However, whereas the introductions of articles in law journals are relatively short, the introduction of empirical articles comprises everything up until the methods section. Hence, the introduction then also includes a literature review, sometimes a theoretical framework, perhaps a conceptual framework, the hypotheses and justification/motivation thereof, etc.

Some pointers for the introduction are:

Start broadly and in easy language when making your opening statements. Take the reader by the hand and guide them towards the key focus of your article. refrain from using jargon and other complex terminology in the first few lines of your introduction.
Make it clear relatively early in the paper why the problem discussed in your article is important. Answer the “Who cares?” question. This will help make the reader enthusiastic to continue reading. For an empirical legal article, it is also important to explain why the empirical perspective is of added value for your legal research.
Discuss what we already know about the topic in a carefully curated literature review. It is not always necessary to give a comprehensive overview of everything that has ever been said on the topic, but make sure to discuss/cite the key articles in the field and the most recent insights.
Make it clear how your research helps the literature (and ultimately society). Are there some pressing questions that are yet to be resolved? Are there contradicting findings that you are hoping to resolve? Are you shedding new light on the assumptions underlying certain legal rules or mechanisms?
When your goal is to test certain hypotheses, make your way towards the hypotheses and make sure it is clear to the reader why you expect a certain relationship or effect. In other words, ground them in the literature. In case your research is more exploratory in nature, therefore making it hard to formulate specific hypotheses, there is no need to arbitrarily define hypotheses. Just be transparent about the exploratory nature of your research.
Finally, end your introduction with a short ‘bridge’ to the methods section. What are you going to do you in your study and how does that logically follow from the introduction?

Writing the methods section

The ultimate goal of this section is to make it clear to the reader what you have done. By being transparent, you allow other researchers to verify your findings through conducting the exact same study.

The method section typically discusses:

The sample that you used, including all relevant demographic information. Take the reader by the hand and explain who (or what) your population is, how you chose your sample and why you made certain sampling decisions (e.g., random sampling, theoretical sampling, saturation).
The design of the study. Explain what method you used. If you used a quantitative method, explain the variables (independent and dependent) and explain how you measured certain relationships. If you used a qualitative method, explain how you analyzed the data by for example explaining your coding methods.
Make sure to clarify the procedure you followed, so that your reader knows how you’ve conducted your research. Say something about the set-up of the interview, the structure of the survey, or the design of the experiments, so the reader can understand what it was like to participate in your study.

If you want to publish your empirical research in a law journal or interdisciplinary journal, it is important to explain your methods in such a way that it is understandable for researchers that may not have any experience in empirical research. Some law journals might be a bit hesitant to include an extensive methods section and might want you to move most methodological details to an appendix, or describe them very succinctly in the text. Look for a recent edition of your target journal to see what is common for that journal, and make sure to write with your target audience in mind.

Writing the results section

In this section, you report your analyses and key findings. Regarding the text you use to describe your findings in words, make sure to first state your findings in words, and only then add the statistics. For example: “We hypothesized that judges would dole out harsher punishment if the unintended harm caused was higher. In line with this hypothesis, we found that judges awarded higher damages in the condition where they read the version of the case with the more significant collateral damage versus the cases with only minor side effects. Specifically, in the former condition the average damages awarded was USD 340.345 (SD = 23.763) versus USD 234.567 (SD = 17.345).”

You can already mention whether certain findings were in line with your hypotheses or not, but refrain from elaborately interpreting your findings in this section, as that will be done it the final part of your paper (the discussion section).

If possible, try to visualize your results in graphs and tables. Ideally, one is able to get a full picture of your findings by either looking at the visuals, or by reading the text and ignoring the visuals. It is therefore important to make clear graphs and tables with informative titles.

When publishing your empirical findings in a law journal, it is again important to write with your target audience in mind. Some journals may be hesitant to publish articles that contain a lot of statistical information. It could be that you want to focus on your key findings, and keep most of the statistical tests and number-heavy sections in your footnotes or an appendix. Similar to the methods section, we would say that the more data and information you manage to keep in the main text, the better.

Writing the discussion section

The main goal of this section is to interpret your data and explain what your results may imply. Start off by briefly reiterating the main goals of your research as well as your key findings. Then go on by critically evaluating the methods used and the robustness of your findings. What are some limitations imposed by your chosen method? How valid are your findings? To what extent might they generalize to the real world and other contexts?

Explain how your results fit in the context of your research topic as you explained in your introduction. What knowledge did you add to the existing literature? Explain what your findings mean for debates that are going in into the literature. If you find different results than other scholars, what might explain those differences? Do your findings say anything about how the law works in practice or do they confirm underlying assumptions of legal rules? Also, depending on whether your research has direct implications for (legal) practitioners, you may want to devote a subsection to the practical implications of your findings. In case any normative claims can (and should?) be made based on your findings, you can consider doing so in your article. For more information on this so-called ‘fact-value gap’ (i.e., going from data to normative implications), please see the entry on this topic in the methods portal.

Bem, D. J. (2000). Writing an empirical article. Guide to publishing in psychology journals , 3-16.

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Steven A. Bank. Dividends and Politics (with Brian Cheffins and Marc Goergen), 25 European Journal of Political Economy 208-224 (2009).

Samuel L. Bray. The Myth of the Mild Declaratory Judgment, 63 Duke Law Journal 1091 (2014).

Daniel J. Bussel. Opinions First--Argument Afterwards, 61 UCLA Law Review 1194 (2014).

Scott Cummings. Managing Pro Bono: Doing Well by Doing Better (with Deborah L. Rhode), 78 Fordham Law Review 2359 (2010).

Sharon Dolovich. Two Models of the Prison: Accidental Humanity and Hypermasculinity in the L.A. County Jail, 102 Journal of Criminal Law & Criminology 965 (2012).

Sharon Dolovich. Strategic Segregation in the Modern Prison, 48 American Criminal Law Review 1 (2011).

Ingrid Eagly. Criminal Justice for Noncitizens: An Analysis of Variation in Local Enforcement, 88 NYU Law Review 1126 (2013).

Ingrid Eagly. Local Immigration Prosecution: A Study of Arizona Before SB 1070, 58 UCLA Law Review 1749 (2011).

Ingrid Eagly. Prosecuting Immigration, 104 Northwestern University Law Review 1281 (2010).

Carole Goldberg. A Study of the Administration of Justice in Indian Country (with Duane Champagne), Report to the National Institute of Justice, Department of Justice. Washington, DC. August 31, 2011.

Robert Goldstein. Picturing the Life Course of Procreative Choices, 58 UCLA Law Review Discourse 5 (2010).

Allison Hoffman. Retiree Out-of-Pocket Healthcare Spending: A Study of Consumer Expectations and Policy Implications (with Howell E. Jackson), 39 American Journal of Law and Medicine 1-72 (2013).

Jill R. Horwitz. Expansion of Invasive Cardiac Services in the United States (with Austin Nichols, Brahmajee K. Nallamothu, Comilla Sasson, and Theodore J. Iwashyna), 128(8) Circulation 803-810 (20 August 2013).

Jill R. Horwitz, , Wellness Incentives in the Workplace: Cost Savings through Cost Shifting to Unhealthy Workers (with Brenna D. Kelly, and John DiNardo), 32(3) Health Affairs 468-476 (2013).

Jerry Kang. Are Ideal Litigators White? Measuring the Myth of Colorblindness (with Nilanjana Dasgupta, Kumar Yogeeswaran, & Gary Blasi) 7 J. Empirical Leg. Studies 886-915 (2010).

Kenneth N. Klee. One Size Fits Some: Single Asset Real Estate Bankruptcy Cases, 87 Cornell Law Review 1285-1332 (2002).

Russell Korobkin. Who Wins in Settlement Negotiations? (with Joseph W. Doherty), 11 American Law and Economics Review 162-208 (2009).

Maximo Langer. Managerial Judging Goes International but its Promise Remains Unfulfilled: An Empirical Assessment of the ITCY Reforms (with Joseph W. Doherty), 36 Yale Journal of International Law 241 (2011).

Douglas Lichtman. Rethinking Prosecution History Estoppel, 71 University of Chicago Law Review 151 (2004).

Gerald P. López. The Health of Undocumented Mexicans in in New York City, 32 Chicano-Latino L. Rev . 1 (2013).

Gerald P. López. Access to and Use of Health Services Among Undocumented Mexican Immigrants in a U.S. Urban Area (with Arijit Nandi, Sandro Galea, Vijay Nandi, Stacey Strongarone & Danielle C. Ompad), 98 Am. J. Public Health 2011 (2008).

Gerald P. López. Hunger and Health Among Undocumented Mexican Migrants in a U.S. Urban Area ( with Craig Hadley, Sandro Galea, Vijay Nandi, Arijit Nandi, Gerald López, Stacey Strongarone & Danielle Ompad), 11 Public Health Nutr . 151 (2008).

Lynn M. LoPucki. Controlling Professional Fees in Corporate Bankruptcies: Data, Analysis, and Evaluation (with Joseph W. Doherty). Oxford University Press (2011).

Lynn M. LoPucki. Bankruptcy Fire Sales (with Joseph W. Doherty), 106(1) Michigan Law Review (2007).

Timothy Malloy. The Social Construction of Regulation: Lessons from the War Against Command and Control, 58 Buffalo Law Review 267-354 (2010).

Neil Netanel. Making Sense of Fair Use, 15 Lewis & Clark Law Review 715-771 (2011).

James Park. Bond Investors and the Evolution of the Securities Class Action, 99 Minn. L. Rev. 585 (2014)

James Park. Securities Class Actions and Bankrupt Companies, 111 Michigan Law Review 547 (2013).

Richard H. Sander. Affirmative Action Bans and the "Chilling Effect" (with Kate L. Antonovics), 15 American Law & Economics Review 252 (2013).

Richard H. Sander. The Secret of My Success: How Status, Eliteness, and School Performance Shape Legal Careers (with Jane Bambauer), 2012 Journal of Empirical Legal Studies 893-930 (2012).

Joanna C. Schwartz. Police Indemnification, 89 New York University Law Review 885 (2014).

Joanna C. Schwartz. A Dose of Reality for Medical Malpractice Reform, 88 New York University Law Review 1224 (2013).

Joanna C. Schwartz. What Police Learn from Lawsuits, 33 Cardozo Law Review 841 (2012).

Kirk J. Stark. Tiebout & Tax Revolts: Did Serrano Really Cause Proposition 13? (with Jonathan M. Zasloff), 50 UCLA Law Review 801-58 (2003).

Katherine Stone. The Decline in the Standard Employment Contract: Evidence from Ten Advanced Industrial Countries, in Rethinking Workplace Regulation: Beyond the Standard Contract of Employment (edited by Katherine V.W. Stone and Harry Arthurs, Russell Sage Foundation, 2013).

Rebecca Stone. Pricing Misperceptions: Explaining Pricing Structure in the Cell Phone Service Market (with Oren Bar-Gill), 9 Journal of Empirical Legal Studies 430 (2012).

Alexander Stremitzer. Framing Contracts: Why Loss Framing Increases Effort (with Richard R. W. Brooks and Stephan Tontrup), 168 Journal of Institutional and Theoretical Economics 62-82 (2012).

Sherod Thaxton. Leveraging Death, 103 Journal of Criminal Law & Criminology 475 (2013).

Adam Winkler. Fatal in Theory and Strict in Fact: An Empirical Analysis of Strict Scrutiny in the Federal Courts, 59 Vanderbilt Law Review 793-871 (2006).

Stephen C. Yeazell. Getting What We Asked For, Getting What We Paid For, and Not Liking What We Got: The Vanishing Civil Trial, 1 Journal of Empirical Legal Studies 943-971 (2004).

Jonathan Zasloff. Tiebout & Tax Revolts: Did Serrano Really Cause Proposition 13? (with Kirk J. Stark), 50 UCLA Law Review 801-58 (2003).

Noah Zatz. Revisiting the Class Parity Analysis of Welfare Work Requirements, 83 Social Service Review 213 (2009).

Eric M. Zolt. Inequality and Taxation: Evidence From the Americas on How Inequality May Influence Tax Institutions (with Kenneth L. Sokoloff), 59 Tax Law Review 167-241 (2006).

Commencement 2024: Mariano-Florentino Cuéllar to deliver keynote address at UCLA Law

Angela riley appears on the 'justice matters' podcast to talk about indigenous sovereignty and human rights, rick hasen is quoted by reuters on the impact that trump's 'hush money' trial could have on the presidential race.

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Resources for Empirical Research

This section offers broad advice on what to keep in mind when constructing a research design. Many of the points discussed below are drawn from and presented more fully within Epstein & King (2002) as well as King, Keohane, & Verba (1994) which are highly recommended sources for in-depth guidance on proper research design and execution. The first step of any empirical research study is to formulate a research question (PDF) . What does the study seek to explain? A good research question should generally conform to the following rules:

The question should be relevant to the real world . It is important that the study seeks to provide practical and important implications for society.
The question should contribute to an existing body of scholarly literature . By speaking to an established set of related studies, the researcher can help avoid significant problems such as duplicating or overlooking previous work. Issac Newton’s famous quote, “if I have seen a little further it is by standing on the shoulders of Giants” colorfully illustrates this rule.

Once the research question is clearly stated, the next step is to offer a clear answer to the question which is theoretically informed and from which falsifiable hypotheses can be derived. The hypothesis should:

Be stated clearly enough to allow for a test which can determine if the proposed answer is wrong.
Specify a relationship between an outcome (dependent variable) and one or more explanatory variables (independent variables).

If there is insufficient evidence to reject a clearly stated, falsifiable hypothesis, then the theory becomes increasingly plausible. A theory which offers many observable implications and therefore more opportunities to be tested has the potential to become a very strong theory if the hypotheses derived from it cannot be rejected.

Remember that the fundamental objective of empirical research is to make inferences —that is, using known facts to understand unknown facts. Typically we use observable data (known facts) to test certain hypotheses which are guided by theory to uncover these unknown facts.

Let’s take a look at a simple example .

Does the scale accurately report my weight?
When stepping on the scale multiple times, does it return a consistent weight estimate ?

Data are typically classified into two categories—qualitative and quantitative. The levels of measurement are as follows:

Nominal data are one form of qualitative data where objects have no natural order (e.g. gender, race, religion, brand name). It does not make sense to think of Buddhism being “more than” Confucianism.
Ordinal data are another form of qualitative data—specifically, groups which can be ranked. An example of an ordinal variable is a survey respondent’s sense of agreement (e.g. strongly agree, agree, disagree, and strongly disagree). These responses do have a natural order and can be ranked, although the distance between each response is difficult to determine.
Interval data are one from of quantitative data which have a definite natural order and, unlike ordinal data, the difference between data can be determined and is meaningful. Interval-level data do not have a natural zero point, however. For example, 0 degrees on the Fahrenheit scale is arbitrary and therefore 100 degrees Fahrenheit is not twice as warm as 50 degrees.
Ratio data are the second form of quantitative data. In contrast, to interval data, ratio-level data have a non-arbitrary 0 point. For example, 0 yards means no length. 100 yards is twice as far as 50 yards.

Even though qualitative data are mostly based on unordered groups, they can nevertheless be analyzed quantitatively. This is achieved by coding the qualitative data of interest into numerical values. For example, if we are running a survey, we can transform gender (nominal data) into a dichotomous (dummy) variable with each respondent assigned a 1 if female and 0 if male. Likewise, the attitudinal responses on the survey can be assigned numerical values as well, for example, Strongly Agree = 4; Agree = 3; Disagree = 2; Strongly Disagree = 1. Once qualitative data have been coded into numerical variables, they can be analyzed using both basic and advanced statistical models.

In empirical legal research, content coding of natural language text is commonly employed (see Hall and Wright, 2008 ; Evans et al., 2007 ). Content analysis is a popular methodology which, for example, can be employed to summarize characteristics of interest related to court decisions. When possible, it is always best to have individuals other than the researcher code variables as to reduce bias.

Empirical research on legal issues can rely on primary (original) as well as secondary (obtained from elsewhere) data. Bradley Wright and Robert Christensen, for example, in studying the effects of public service motivation on job sector choice, employ an original survey of law students in one study ( Christensen and Wright, 2011 ) as well as survey data from the American Bar Association in another study ( Wright and Christensen, 2010 ).

Another data collection technique is webscraping, using software to visit web sites and extract specific bits of information. Here is a tutorial on web scraping written in the R language that was prepared by Jonathan Whittinghill, the Applied Research Statistician at the HLS Empirical Research Services.

This section provides links to helpful resources on analytical methodologies—both descriptive and statistical. For further assistance, you may also schedule a meeting with the empirical research services unit at the Law Library. Where to learn about analyzing data:

Empirical courses offered at Harvard For students interested in taking courses on empirical research, there are numerous classes offered at Harvard Law School, Kennedy School and FAS. There are five tracks for student interested in empirical research methods, ranging from courses appropriate for those with no background in empirical research to courses for those students who have an extensive methodological background. A description of these tracks can be found here and a list of courses offered on campus can be found in this file prepared by Jonathan Whittinghill.

MIT OpenCourseWare classes on empirical research

Political Science

Political Science Scope and Methods (Undergraduate, Berinsky, Fall 2010)

Introduction to Statistical Method in Economics (Undergraduate, Bennett, Spring 2006)

Introduction to Statistical Methods in Economics (Undergraduate, Menzel, Spring 2009)

Econometrics (Undergraduate, Angrist, Spring 2007)

Statistical Method in Economics (Graduate, Chernozhukov, Fall 2006)

Econometrics I (Graduate, Hausman & Chernozhukov, Spring 2005)

Nonlinear Econometric Analysis (Graduate, Chernoshukov & Newey, Fall 2007)

New Econometric Methods (Graduate, Newey, Spring 2007)

Time Series Analysis (Graduate, Mikusheva, 2013)

Mathematics

Introduction to Probability and Statistics (Undergraduate, Panchenko, Spring 2005)

Probability and Random Variables (Undergraduate, Sheffield, 2014)

Statistics for Applications (Undergraduate, Kempthorne, 2015)

Sloan School of Management

Statistical Thinking and Data Analysis (Undergraduate, Rudin, 2011)

Data, Models, and Decisions (Graduate, Gamarnik, Freund & Schulz, Fall 2007)

Communicating with Data (Graduate, Carroll, Summer 2003)

Doctoral Seminar in Research Methods I (Graduate, Sorensen & Bailyn, Fall 2004)

Doctoral Seminar in Research Methods II (Graduate, Sorensen, Spring 2004)

Overview of quantitative methods (PDF) prepared by Parina Patel Statistical software packages: Stata is a general-purpose statistical software package which is popular among researchers in economics, sociology, political science, epidemiology and biomedicine among others. The statisticians at Harvard Law School primarily use Stata for data analysis.

Machines with Stata are located in the computer classroom in Langdell 353. You may also purchase Stata directly from Statacorp .

The UCLA Institute for Digital Research & Education Stata site has many excellent step-by-step tutorials on a wide range of statistical estimation procedures using Stata.
Germán Rodriguez, Princeton University, Stata resources also has a comprehensive overview of Stata, including data management, graphics and programming examples.
One of the advantages of Stata is its active community of users. The Statalist is an email listserver where more than 3,500 Stata users discuss all aspects of the program. If you have a question, you are likely to find a relevant discussion in the archives of the listserver.
Stata Press publishes excellent manuals on best-practices for a whole range of statistical estimations. Most titles can be found using Hollis. The Stata Journal is also an invaluable resource for furthering usage effectiveness.
Downloadable material for upcoming Stata workshop

R is an open-source programming language and statistical software environment. R offers a wide variety of statistical and graphical techniques . A good description of the software can be found on the official site of R. Compared to Stata and certainly SPSS, R requires a significant amount of programming proficiency. The program is free and can be downloaded here .

The UCLA Academic Technology Services R resources page includes a great set of introductory tutorials, frequently asked questions, analysis examples, and links to downloadable books on R.
Germán Rodriguez, Princeton University, R page has an excellent series on getting started with R, data management and fitting linear and generalized linear models. A list of helpful books on R is also provided.
Adler, Joseph (2010) R in a Nutshell and Muenchen, Robert A. and Joseph M. Hilbe (2010) R for Stata Users

IBM SPSS is another popular general-purpose statistical software package which can handle almost all econometric estimations. A notable difference between the SPSS and Stata/R environments is that SPSS relies much more on Graphical User Interface (point-and-click) procedures making it more user friendly. While the “vanilla” version of SPSS may be somewhat limited relative to Stata or R, there are many SPSS add-ons and modules which provide additional capabilities. SPSS can be bought directly from IBM SPSS .

The UCLA Academic Technology Services SPSS page offers introductory material, examples, links to web books on SPSS, and other helpful resources.
The UCLA Institute for Digital Research & Education SPSS site covers a wide variety of statistical estimation procedures using SPSS

SAS is yet another popular software package used for statistical analysis. It is generally understood as a powerful program especially when working with very large datasets. One significant limitation of SAS is its poor graphical capabilities.

The UCLA Academic Technology Services SAS page provides introductory materials for beginner SAS users, analysis examples, web books and other resources.
When discussing the results of a regression analysis, do not only focus on the parameters which are “statistically significant.” The researcher should also convey how “substantive” an effect each significant variable has on the outcome (dependent) variable. Holding other variables in the model fixed, for instance, what is the predicted value of the dependent variable when the significant independent variable in question is at its minimum, mean, and maximum values?
If you are attempting to use your results to infer about a population, then you should do so while explicitly discussing the level of uncertainty of your estimates. This typically implies discussing the confidence levels of your estimates. For a fun discussion of uncertainty see Ian Ayres’ SuperCrunchers (pgs. 112-116).
Try to avoid presentation of data and results using tables—graphs are almost always superior.

Epstein et al. (2006) “On the Effective Communication of Results of Empirical Studies, Part I” Vanderbilt Law Review 59(6): 1811-1871

Epstein et al. (2007) “On the Effective Communication of Results of Empirical Studies, Part II” Vanderbilt Law Review vol. 60(3): 801-846.

Once you are finished, you might be interested in trying to get your study published in a law school law review or in a peer-reviewed journal from a society and larger publisher. Washington and Lee’s Law Journal Submission and Ranking website , Ulrich’s Global Serials Directory , and ISI Journal Citation Reports are good resources for identifying both types of journals both within and outside of the United States. (There is a peer-reviewed journal that is actually devoted completely to empirical legal studies work, Journal of Empirical Legal Studies .While simultaneous submission of manuscripts to multiple journals is the norm for most law school law reviews (with August-October and February-April being the big submission “seasons”), most peer-reviewed journals require exclusive submissions. (Some student edited journals like the Harvard Law Review and Stanford Law Review are also starting to experiment with peer or faculty review and may prefer exclusive submissions.) You should always check the journal’s website for specific guidelines about preparing manuscripts for publication. (For example, the NYU Law Review has special guidelines just for empirical work).You might also want to consider depositing your data to make it available for replication and further use by future researchers. Some journals might actually require you to submit your data for manuscript review or for publication. While there are various options for storing and archiving your data, one of the most popular ones with social scientists is IQSS Dataverse . It has several features, including the ability to prepare data visualizations for users.

Journal of Empirical Legal Studies (Harvard Key required)

ELG The Empirical Legal Group

Empirical Legal Studies

ELS Librarians

Other Empirical Legal Studies Library Sites

UC Berkeley

Modal Gallery

Gallery block modal gallery.

[email protected]
080 - 2675 4480
Jubilee Building – 2nd Floor, No.45 Museum Road, Bengaluru - 560 025

10 Empirical Research Topics in Law for Academic Study

August 20, 2023
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Empirical Research Topics in Law

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Empirical Research Topics in Law: 10 Popular Questions Answers

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Empirical research in law is a crucial aspect of legal practice and scholarship. This contract sets out the terms and conditions for conducting empirical research on specific topics in law.

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Open access
Published: 15 April 2024

An empirical investigation of emotion and the criminal law: towards a “criminalization bias”?

Jozef N. Coppelmans ORCID: orcid.org/0000-0001-8406-9125 1 , 2 ,
Fieke M. A. Wagemans 3 &
Lotte F. van Dillen ORCID: orcid.org/0000-0002-3003-5488 2 , 4

Humanities and Social Sciences Communications volume 11 , Article number: 513 ( 2024 ) Cite this article

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Criminology
Science, technology and society

This interdisciplinary study, coupling philosophy of law with empirical cognitive science, presents preliminary insight into the role of emotion in criminalization decisions, for both laypeople and legal professionals. While the traditional approach in criminalization theory emphasizes the role of deliberative and reasoned argumentation, this study hypothesizes that affective and emotional processes (i.e., disgust, as indexed by a dispositional proneness to experience disgust) are also associated with the decision to criminalize behavior, in particular virtual child pornography. To test this empirically, an online study (N = 1402) was conducted in which laypeople and legal professionals provided criminalization ratings on four vignettes adapted from criminal law, in which harmfulness and disgustingness were varied orthogonally. They also completed the 25-item Disgust Scale-Revised (DS-R-NL). In line with the hypothesis, (a) the virtual child pornography vignette (characterized as low in harm, high in disgust) was criminalized more readily than the financial harm vignette (high in harm, low in disgust), and (b) disgust sensitivity was associated with the decision to criminalize behavior, especially virtual child pornography, among both lay participants and legal professionals. These findings suggest that emotion can be relevant in shaping criminalization decisions. Exploring this theoretically, the results could serve as a stepping stone towards a new perspective on criminalization, including a “criminalization bias”. Study limitations and implications for legal theory and policymaking are discussed.

The impact of legal expertise on moral decision-making biases

Sandra Baez, Michel Patiño-Sáenz, … Agustín Ibáñez

Social threat indirectly increases moral condemnation via thwarting fundamental social needs

Robert K. Henderson & Simone Schnall

Bystanders’ collective responses set the norm against hate speech

Jimena Zapata, Justin Sulik, … Ophelia Deroy

Introduction

One of the most fundamental questions in legal theory is: what type of behavior should we bring under the scope of the criminal law (Stanton-Ife, 2022 )? Traditionally, in law and philosophy, the answer to this question is considered to be found through reflective, “System 2” thinking (to use Kahneman’s [ 2011 ] term), guided by rational deliberation and legal reasoning (Alexander and Sherwin, 2021 ; Dickson, 2016 ; Duff, 2014 ; Dworkin, 1977 ; Edwards, 2018 ; Feinberg, 1984 , 1988 ; Hart, 1963 ; Husak, 2008 ; Levi, 1949/ 2013 ; Simester and Von Hirsch, 2011 ; Tadros, 2016 ). In their reasoning, legal scholars draw upon various normative concepts such as the harm principle (Mill, 1859/ 2005 ), moral wrongfulness (Devlin, 1965 ), and legal goods (Birnbaum, 1834 ). The harm principle, which posits that conduct can only be criminalized to prevent harm to others, is widely considered the most important basis for criminalization (Feinberg, 1984 ; Hart, 1963 ; Stanton-Ife, 2022 ). Footnote 1

However, research in the field of moral psychology and cognitive neuroscience has revealed that moral decision-making encompasses not only “reflective and deliberative” processes but also “affective and emotional” processes (Cushman, 2013 ; Greene, 2013 ; Haidt, 2012 ). Footnote 2 The latter often occur without individuals being aware of them and can result in rationalization (Cushman, 2020 ; Greene, 2013 ; Haidt, 2001 ). In moral philosophy, these insights have been examined extensively, both theoretically (Greene, 2014 , 2023 ; Königs, 2022 ; Railton, 2014 ) and empirically (Greene et al., 2001 ; 2004 ; Greene and Young, 2020 ; Miller et al., 2014 ). More recently, the debate over the new insights on affective and emotional processes has also started to emerge within criminalization theory (Alces & Sapolsky, 2023 ; Coppelmans, 2013 ; Patrick and Lieberman, 2018 ; Persak, 2019 ; Winter, 2024 ). Footnote 3 However, the discussion remains primarily theoretical in nature. There are currently few or no empirical studies that specifically investigate the relationship between emotion and the decision to criminalize behavior: it is an “under-represented” area (Persak, 2019 ; Sznycer and Patrick 2020 ; Winter, 2024 ). Footnote 4 Therefore, instead of solely relying on a theoretical approach, and following the rise of “experimental jurisprudence” (Knobe and Shapiro, 2021 ), we aim to investigate empirically whether emotion is related to the criminalization of behavior. Footnote 5

We intend to do this by investigating to what extent the emotion of disgust (as indexed by a dispositional proneness to experience disgust) is associated with criminalization decisions. Additionally, and strictly from a theoretical perspective, we aim to propose the potential development of a new perspective on criminalization, introducing a potential “criminalization bias”. In order to demonstrate both, we use virtual child pornography as a case study (i.e., images depicting virtual children engaging in virtual sexual conduct). Footnote 6

Virtual child pornography

Technological developments have presented legislators with novel types of behavior that are not easy to characterize in ethical terms, with virtual child pornography as an example (Gillespie, 2018 ). This topic is a highly contentious and sensitive issue, with arguments both in favor and against criminalizing it, ranging from the harm principle to legal moralism (Gillespie, 2018 ; Levy, 2002 ; Luck, 2009 ; Ost, 2010 ; Strikwerda, 2011 , 2014 ). Over recent decades, several countries have included (realistic) virtual child pornography under the purview of criminal law, including the United States, Canada, Australia, South Africa, and many EU member states (Bird, 2011 ; Gillespie, 2018 ; Witting, 2020 ). Footnote 7 In good legal tradition (Edwards, 2018 ), arguments for this are based on “deliberative, legal reasoning”. That is, policymakers, legislators, and legal scholars have been using their cognitive reasoning abilities to develop a line of argumentation.

The central argument refers to the harm principle (Mill, 1859/ 2005 ), with legislators claiming that virtual child pornography is—directly or indirectly—harmful to children (Bird, 2011 ; Gillespie, 2018 ; McLelland and Yoo, 2007 ; Williams, 2004 ; Witting, 2020 ). Footnote 8 This claim has met with criticism (Ashcroft v. Free Speech Coalition, 2002 ; Bell, 2012 ; Bird, 2011 ; Burke, 1997 ; Gillespie, 2018 ; Gray, 2021 ; Ost, 2010 ; Williams, 2004 ; Witting, 2020 ). It is uncertain whether virtual child pornography is harmful, i.e., that online child offenses “fuel” offline child abuse; clear evidence for this is, at best, mixed (Babchishin et al., 2015 , 2018 ; Endrass et al., 2009 ; Gottfried et al., 2020 ; Gray, 2021 ; Houtepen et al., 2014 ; Ost, 2010 ; Gillespie, 2018 ; Nair, 2019 ; Seto, 2013 , 2018 ; Seto et al., 2011 ; but see Christensen et al., 2021 ). Moreover, it is suggested that the availability of virtual child pornography makes people less prone to child sexual abuse by providing an outlet or a means of treatment (Cisneros, 2002 ; Diamond, 2009 ; Levy, 2002 ; Seto, 2013 ). Here, we sidestep this debate. Obviously, if future evidence demonstrates that virtual child sexual material poses a threat to children, it should be a criminal offense. For present purposes, however, we will follow the most prominent claims in the literature and assume (again: for now) that the harmfulness of virtual child sexual material is—at most—low.

Amongst legal scholars, another line of argumentation is based on legal moralism (Bartel, 2012 ; Strikwerda, 2011 , 2017 ; Patridge, 2013 ). However, arguments of this kind are deemed abstract and “rickety” (Ost, 2010 ) and are generally not considered a legitimate reason in themselves to criminalize conduct (Feinberg, 1988 ; Luck, 2009 ; Nair, 2019 ; Ost, 2010 ; Simester and Von Hirsch, 2011 ; Patridge, 2013 ). As Luck ( 2009 ) expresses it in his gamer’s dilemma: “while virtual murder scarcely raises an eyebrow, (…) most people think that virtual pedophilia is not morally permissible”.

This raises the question: what exactly motivates legislators and legal scholars to criminalize virtual child pornography? Arguments are often rooted in unfounded claims about its harmfulness or in abstract legal moralism (Ashcroft v. Free Speech Coalition, 2002 ; Ost, 2010 , see also Sood and Darley, 2012 ), which makes the foundation of this criminalization unclear. What is clear, however, is that both legal scholars and legislators try to find arguments based on “deliberative, legal reasoning”. Given the extremely sensitive nature of virtual child pornography, we hypothesize that the decision to criminalize this conduct is not mainly based on deliberative legal reasoning, but rather is influenced by affective and emotional processes, such as disgust. Hence, we think that virtual child pornography lends itself well for an inquiry into the role of emotion in criminalization decisions.

Moral decision-making and emotional processes

There is considerable evidence to support the claim that affective and emotional processes are relevant for moral judgment (Greene, 2011 , 2023 ; Greene and Young, 2020 ; Haidt, 2001 , 2012 ; Koenigs et al., 2007 ; May and Kumar, 2018 ; McCormick et al., 2016 ; Prinz, 2007 ; Slovic et al., 2002 ; Valdesolo, 2018 ; van Honk et al., 2022 ). As to whether these affective and emotional processes are best described in terms of “distinct emotions” such as disgust, anger and fear (Adolphs and Anderson, 2018 ), “constructed emotions” based on active inference (Feldman Barrett, 2017 ; Seth and Friston, 2016 ; Parr, Pezzulo and Friston, 2022 ) or other computational mechanisms such as “heuristics” (Hjeij and Vilks, 2023 ; Kahneman, 2011 ; Slovic et al., 2002 ) or “model-free reinforcement algorithms” (Cushman, 2013 ; Crockett, 2013 ), the debate continues (Fox, 2018 ; Cushman and Gershman, 2019 ). For now, in operationalizing affective and emotional processes (Haidt, 2012 ; Landy and Piazza, 2019 ), we focus on a line of research that has received much attention, namely disgust (Giner-Sorolla, 2021 ; Haidt, 2001 ; Inbar and Pizarro, 2022 ; Kelly, 2011 ; Patrick and Lieberman, 2018 ; Piazza et al., 2018 ; Tybur et al., 2013 ).

Moral decision-making and disgust

Disgust is characterized as a powerful, negative emotion and is considered one of the primary outputs of the so-called behavioral immune system, a set of behavioral adaptations to mitigate pathogen threats (Ackerman et al., 2018 ; Schaller and Park, 2011 ). Disgust is also argued to underlie moral condemnation (Lieberman and Patrick, 2018 ; Nussbaum, 2004 ; Rozin et al., 2008 ; Tybur et al., 2013 ), particularly when pathogen threats are involved (Inbar and Pizarro, 2022 ) and for acts involving physical or spiritual “purity” (Atari et al., 2023; Graham et al., 2013 ). Footnote 9 First, people commonly experience disgust along with anger and other negative emotions following moral transgressions (Cannon et al., 2011 ; Chapman et al., 2009 ; Danovitch and Bloom, 2009 ; Haidt et al., 1997 ) and against political outgroups (Landy et al., 2023 ). Second, some experiments indicate that disgust manipulations lead to harsher moral judgment (e.g., Eskine et al., 2011 ; Horberg et al., 2009 ; Schnall et al., 2008 ; Seidel and Prinz, 2013 ; Tracy et al., 2019 ; Van Dillen et al., 2012 ; Wheatley and Haidt, 2005 ). Though some experimental effects could not be reproduced or verified by recent attempts (Ghelfi et al., 2020 ; Johnson et al., 2016 ; Jylkkä et al., 2021 ; Sanyal et al., 2023 ). In addition, a meta-analysis reported an average effect size near zero after correcting for publication bias, although specifically gustatory/olfactory disgust inductions did produce a robust and relatively large effect, d = 0.37 (Landy and Goodwin, 2015 ). Third, one of the more replicable effects within the domain of disgust is the association between disgust sensitivity and attitudes to morally deviant behavior (Inbar and Pizarro, 2022 ).

Disgust sensitivity

Disgust sensitivity is an individual’s propensity to experience disgust (Haidt et al., 1994 ). Footnote 10 While state disgust refers to someone’s current emotional experience, trait disgust sensitivity refers to people’s stable tendency to experience disgust over time. Many studies report an association between disgust sensitivity and moral decision-making (Donner et al., 2023 ). People who are in general more easily disgusted will more readily convict a suspect in a murder, burglary, or sexual assault case (Jones and Fitness, 2008 ), punish purity violations more harshly and reward purity virtues more strongly (Horberg et al., 2009 ), and show a stronger preference for order, hierarchy, and deontological judgment (Robinson et al., 2019 ). They also demonstrate more negative attitudes to organ donation (Mazur and Gormsen, 2020 ), vaccination (Clifford and Wendell, 2016 ; Kempthorne and Terrizzi, 2021 ; Reuben et al., 2020 ), immigrants and foreign ethnic groups (Aarøe et al. 2017 ; Brenner and Inbar, 2015 ; Hodson and Costello, 2007 ; with cross-national insights from Clifford et al., 2023 and Zakrzewska et al., 2019 , 2023 ), gays and lesbians (Inbar et al., 2009 ; Kiss et al., 2020 ; Olatunji, 2008 ; Terrizzi et al., 2010 ; Van Leeuwen et al., 2022 , Wang et al., 2019 ), and groups that threaten traditional sexual morality more generally (Crawford et al., 2014 ; Van Leeuwen et al., 2022 ). Furthermore, people who are more easily disgusted tend to judge violations of moral and social conventions more harshly, with these effects being most pronounced within the domain of purity (Wagemans et al., 2018 ; Liuzza et al., 2019 ), but also observable outside of this domain (Chapman and Anderson, 2014 ; Karinen and Chapman, 2019 ); for a meta-analytic review, see Donner et al., ( 2023 ).

However, and importantly, it should be noted that sensitivity to several affective states (not only disgust, but also anxiety and anger) predicts extremity in normative and evaluative judgments (Cheng et al., 2013 ; Landy and Piazza, 2019 ). This suggests that the relationship between disgust and moral condemnation could be a specification of a broader relationship between affective processes and (moral) evaluation (Inbar and Pizarro, 2022 ; Piazza et al., 2018 ). In sum, the measurement of individual differences in disgust sensitivity might provide a good means for examining the role of “emotion” in criminalization decisions.

Current research: criminalization and emotional processes

Given the evidence that emotional processes are associated with the decision to morally condemn behavior, it is reasonable to hypothesize that emotional processes are also associated with the decision to criminalize behavior. At the theoretical level, this has been proposed earlier (Devlin, 1965 ; Coppelmans, 2013 ; Kahan, 1999 ; Moore, 1997 ; Nussbaum, 2004 ; Patrick, 2021 ; Patrick and Lieberman, 2018 ; Persak, 2019 ; Sunstein, 2008 ). Our goal is to provide an early empirical investigation of this claim, by correlating disgust sensitivity with criminalization decisions. This will be depicted through a series of vignettes drawn from criminal law scenarios and traditional criminalization theory. We will use four different vignettes in which we orthogonally varied the levels of harmfulness Footnote 11 (low, high) and disgustingness (low, high). The scenarios are virtual child pornography (characterized as low in harm, high in disgust), actual child pornography (high harm, high disgust), wearing a sweater with clashing bright colors (low harm, low disgust; Feinberg, 1985 ), and the use of contingent convertible bonds, which is a high-risk financial instrument (high harm, low disgust; see Berg and Kaserer, 2015 ; Chan and van Wijnbergen, 2015 ; Goncharenko et al., 2021 ; Fatou et al., 2022 ).

A criminalization bias?

We expect disgust sensitivity to correlate with criminalization decisions in particular for violations that are concrete and personal (Greene, 2009b , 2013 ) and violations that pertain to the domain of “purity” (Atari et al., 2023 ; Haidt, 2012 ); with virtual child pornography being a prime example of both. We expect the correlation with disgust sensitivity to a lesser extent for violations that are abstract and impersonal (Greene, 2009a , 2013 ), with high-risk financial behavior as an example (i.e., contingent convertible bonds). The “concrete and personal”-distinction is based on Greene (Greene, 2009b , 2013 ), and points to behavior that is especially expected to trigger an emotional response because it resembles threatening behavior that existed in our ancestors’ personal environment during evolutionary history, such as hitting, killing, rape and other forms of prototypically violent behavior. Footnote 12 The category of “purity,” as part of Moral Foundations Theory and outlined by Haidt (Atari et al. 2023 ; Graham et al., 2013 , 2018 ; Haidt, 2012 ), encompasses behaviors that are expected to provoke disgust because they are associated with “bodily and spiritual contamination and degradation” and conflict with “intuitions about sexually deviant practices, chastity and wholesomeness”. Understandably, virtual child pornography falls within both categories. This sexually deviant practice is characterized as “concrete and personal”, as it closely resembles real child sex abuse, behavior that was certainly threatening in Homo sapiens’ personal environment during evolutionary history. It is thus assumed to provoke a strong emotional response. The technically and abstract high-risk financial behavior does not easily align with threats from our ancestors’ evolutionary history, nor does it readily associate with “purity-based” offenses. Consequently, virtual child pornography is believed to elicit a significantly stronger emotional response than high-risk financial behavior.

The noteworthy aspect of these two scenarios is an expected “ criminalization bias ” (Coppelmans, 2024 ), Footnote 13 predicted on the assumption that the primary reason here for criminalization is based on the harm principle (as is formally the case in most countries; see Footnote 7). That is, virtual child pornography may not cause significant harm (see above), Footnote 14 but it is expected to elicit a strong aversive emotional reaction leading to a decision to criminalize this conduct. Footnote 15 On the other hand, high-risk financial behavior has the potential to harm individuals and society at large (Berg and Kaserer, 2015 ; Chan and van Wijnbergen, 2015 ; Goncharenko et al., 2021 ; Fatou et al., 2022 ), yet it generally does not elicit a strong emotional reaction, resulting in a decision not to criminalize this conduct. Footnote 16

Legal expertise and hypotheses

We will test our hypotheses with both lay participants and legal professionals, as we are interested in determining whether legal education and expertise (i.e., professional judgment) can mitigate the impact of affective and emotional processes on moral decision-making. Some research indicates that it does (Baez et al., 2020 ) or finds it to be context-dependent (Teichman et al. 2024 ), while other research suggests that education and expertise do not necessarily mitigate the influence of affective and emotional processing across different domains, including legal and philosophical (Horvath and Wiegmann, 2022 ; Kahan et al., 2016 , 2017 ; Rachlinkski and Wistrich, 2017 ; Schwitzgebel and Cushman, 2012 ; Wistrich et al., 2015 ). Given the scarcity of research specifically in the legal domain, it is valuable to explore this area.

In conclusion, we hypothesize that criminalization ratings are influenced by both the characteristics of the vignettes (harmfulness and disgustingness) and the characteristics of the participants (disgust sensitivity). Specifically, we anticipate that vignettes that are highly disgusting (the virtual and actual child pornography) are more likely to be criminalized than the vignette high in harm, low in disgust (the high-risk financial instrument). Further, we anticipate that these effects will be enhanced for participants that score high on trait disgust sensitivity, and that, according to our theoretical framework, this relationship may be especially strong in the case of virtual child pornography. Finally, we explore to what extend legal expertise, as reported by our sample’s participants, moderates the hypothesized effects of disgust on criminalization decisions.

Participants and design

The survey was completed by 1725 individuals. Participants who were younger than 16 years old (N = 38) and those who did not rate any of the vignettes (N = 285) were excluded. This resulted in a final sample of 1402 participants (517 males, 876 females, 9 not reported, M age = 33.57 years, SD = 13.02 years, with 4 missings). Of this sample, a subset of 103 participants who have expertise in the legal domain, such as legislative lawyers, legal policymakers, lawyers, and judges were considered as “experts” in formal, law-based (criminalization) decisions. The remaining sample (N = 1297) was considered to have a lay perspective. The two subsets were matched in terms of age and gender distribution.

We indicated a minimum sample of 600 participants but given our interest in the between person moderators of disgust sensitivity and legal expertise, and to account for possible attrition, we aimed to recruit as large a sample as possible. To determine the sensitivity power analysis (Lakens, 2022 ) to detect our focal effects, we conducted simulation-based sensitivity analyses following data collection, that allow for simulation of mixed-effects (logistic) regression models that capture multiple sources of random variations (Kumle et al., 2021 ; see Analytic Strategy and Results for further details).

The study was designed to investigate whether participants’ criminalization decisions depend on vignette characteristics (harm and disgustingness) and/or respondent characteristics (disgust sensitivity). It involved a 2(harm; high versus low) × 2(disgustingness; high versus low) within-participant design with disgust sensitivity as a between-participant continuous variable. The focal dependent variables involved participants’ continuous estimations and binary decisions of criminalization.

Four vignettes were created, each depicting a distinct type of behavior. The researchers evaluated the vignettes on two dimensions (high/low) of harmfulness and disgustingness. It is important to note that the level of harm represents the objective harmfulness of the behavior (as established in previous literature; see earlier Introduction) and not the perceived harmfulness by the participants. The four vignettes include: (1) wearing a sweater with clashing bright colors (low harm, low disgust); (2) contingent convertible bonds, explained as a high-risk financial instrument (high harm, low disgust); (3) virtual child pornography (low harm, high disgust); and (4) actual child pornography (high harm, high disgust) (refer to Appendix A for further details). Each vignette presented a brief description of a specific behavior followed by the statement “This type of behavior should be considered a criminal offense.” Participants were asked to indicate their agreement or disagreement with this statement through a binary response (agree/disagree) and a 7-point Likert scale, where 1 represents “Strongly Disagree” and 7 represents “Strongly Agree”.

Also, the participants were administered the Dutch version of the revised 25-item Disgust Scale (DS-R-NL; Haidt et al., 1994 ; modified by Olatunji et al., 2007 , Dutch version: M. van Overveld) to measure individual differences in disgust sensitivity. Cronbach’s alpha of the scale in the current study sample was α = 0.78 (N = 1175). The psychometric properties of the DS-R-NL are comparable to those of the English version (Olatunji et al., 2009 ). Participants were asked to rate their agreement or disagreement with 13 statements and their level of disgust in 12 scenarios. Each item was rated on a 5-point scale, with 0 representing “strongly disagree” or “not at all disgusting” and 4 indicating “strongly agree” or “very disgusting”. An example of a statement: “I might be willing to try eating monkey meat, under some circumstances”. An example af a scenario: “You see maggots on a piece of meat in an outdoor garbage pail”. Higher scores reflect higher levels of disgust sensitivity. Two “catch questions” were excluded from the analysis.

Data collection was conducted over the period of February 2017 to February 2018. The questionnaire was administered electronically using the Qualtrics platform. Participants were recruited through various channels to achieve a broad and diverse sample. These channels included university communication networks, Quest Braintainment (a popular science magazine), and multiple social media channels. Specifically, we placed the questionnaire in Facebook community groups from different cities throughout the Netherlands, ranging in size from small villages to medium-sized and large cities, to reach as diverse a population as possible. Expert participants were solicited through multiple law firms and the Dutch Academy for Legislation, an educational institution for legislative lawyers.

After informed consent was obtained, participants completed the survey in three phases. First, demographic information (age, gender, education), legal expertise, occupation, and any connection to the domain of criminal law were obtained. Participants then proceeded to rating each of the four vignettes and subsequently to the 25-item Disgust Sensitivity Scale (DS-R-NL), or vice versa. The four vignettes were each followed by two questions pertaining to criminalization, which was defined as whether the behavior should be subject to criminal law. To counteract any carryover effects, the vignettes were presented in a randomized order. After both questionnaires, participants were debriefed.

All procedures were approved by the Psychology Research Ethics Committee (CEP), protocol number CEP17-0803_260, at Leiden University. Whereas we did not formally pre-register our hypotheses and study set up at the time, the time-stamped approved ethics protocol contains all relevant information, as well as our hypotheses regarding the effects of disgust sensitivity and can be found in the OSF project folder. This folder also contains the raw data, analysis code, and a PDF document of the Qualtrics survey. See: https://osf.io/a49qe .

Data analytic strategy

Linear mixed-effects models and mixed-effects logistic regression models were applied to estimate the effects of vignette type and disgust sensitivity on the continuous and dichotomous criminalization variables, respectively. For this, the “lmer” function in the “lme4” and “lmerTest” packages of R was used (Bates et al., 2015 ; Kuznetsova et al., 2013 ). Confidence intervals were obtained using the “confint” function in the “stats” package using Monte Carlo simulations with 1000 bootstrap samples (R Core Team, 2019 ). Simple effects were obtained using the “lstrends” function in the “lsmeans” package (Lenth, 2016 ). All models include random intercepts so that individuals are given their own starting points on the dependent variable and, when this increased the fit further and the model did not fail to converge, random slopes for participants, to account for individual differences in the effect of disgust sensitivity on the dependent variable. For the analyses, disgust sensitivity scores were standardized (generating z-scores). The exact coefficients of all outcomes of the regression models can be found in Tables 1 – 3 .

Simulation-based power analyses were conducted for the highest order focal interaction effects (of vignette disgust and harm, and disgust sensitivity), applying the respective mixed-effects models and the compatible simR package (Baayen et al., 2008 ; Green and MacLeod, 2016 ). The logic of simulation-based power analyses is as follows: (1) simulate new data sets, (2) analyze each data set and test for statistical significance, and (3) calculate the proportion of significant to all simulations. Monte Carlo simulations were used with 1000 bootstrap samples for the continuous outcome variable and 500 simulations for the dichotomous outcome variable to optimize fitting. Adopting effect sizes from existing data involves the risk of performing the analyses on inflated effect sizes, which in turn would result in an underpowered design. To protect against such bias or uncertainty in the data used for simulation one approach is choosing a conservative smallest effect size of interest. For instance, one could determine the value of the estimated effect size by reducing the observed beta coefficients of interest by 15% (Kumle et al., 2021 ). Here, instead, we applied the small parameter estimate of b = 0.15 in our simulations (which falls well below the 15% range of the observed coefficients of interest).

The analysis code and the raw data can be retrieved at the study’s open science framework page: https://osf.io/a49qe .

Criminalization ratings (continuous)

To test whether vignette type influences criminalization of behavior, a model including harm (low and high), disgustingness (low and high), and their interaction was fitted to the criminalization ratings. This yielded main effects for both harm ( b = 2.51, SE = 0.05, t [3880.83] = 46.25, p < 0.001; 95% CI [2.42, 2.63]) and disgust ( b = 3.63, SE = 0.05, t [3882.47] = 67.17, p < 0.001; 95% CI [3.52, 3.72]), such that vignettes that are high in harm resulted in higher criminalization ratings than vignettes low in harm, and vignettes that are high in disgust resulted in higher criminalization ratings than vignettes low in disgust. These main effects were further qualified by a significant interaction effect ( b = −0.56, SE = 0.08, t [3884.15] = −7.26, p < 0.001; 95% CI [−0.71, −0.40]). Whether or not the behavior was harmful determined criminalization ratings to a greater extent depending on whether the vignette also induced disgust. That is, harm increased criminalization ratings of low disgust vignettes more (resp. M = 1.22, SE = 0.04 and M = 3.73, SE = 0.04; b = 2.51, SE = 0.05, t [3887] = 46.25, p < 0.001) than of high disgust vignettes (resp. M = 4.84, SE = 0.04 and M = 6.80, SE = 0.04; b = −1.96, SE = 0.05, t [3893] = 36.27, p < 0.001). Likewise, disgust affected the criminalization ratings of low harm vignettes more ( b = 3.63, SE = 0.05, t [3889] = 67.17, p < 0.001) than of high harm vignettes ( b = 3.1, SE = 0.05, t [3895] = 56.445, p < 0.001). Means and standard deviations are depicted in Fig. 1 .

Mean criminalization ratings were higher for the virtual child pornography vignette (low in harm, high in disgust) than for the financial crime vignette (high in harm, low in disgust). Note . Mean criminalization ratings (1 totally disagree to 7 totally agree). Error bars depict standard errors.

Criminalization decisions (dichotomous)

To determine to what extent the level of disgust and harm affected participants’ decisions to criminalize (yes/no) the behavior described in the vignette we fitted a similar model to the dichotomous criminalization variable. Comparable main effects were observed for the vignettes’ level of disgust ( b = 4.92, SE = 0.26, z = 17.71, p < 0.001; 95% CI [4.45, 5.49]) and harm ( b = 3.80, SE = 0.25, z = 15.09, p < 0.001; 95% CI [3.34, 4.39]). Participants were more inclined to criminalize behavior high compared to low in disgust or harm. Contrary to the analysis of continuous criminalization ratings, there was no significant interaction effect between disgust and harm level on criminalization, b = 0.17, SE = 0.37, z = 0.46, p = 0.65; 95% CI [−0.61, 1.00]. Table 2 depicts the decision frequencies for the various vignette.

Disgust sensitivity as a moderator of the effects of vignette characteristics on criminalization

Next, we examined whether individual differences in disgust sensitivity further moderated the effects of disgust and harm levels of the vignette on criminalization. To do so, a full-factorial model including disgust level (low and high), harm level (low and high), and disgust sensitivity (standardized scores), was fitted to the data. For the continuous outcome of criminalization, this analysis revealed in addition to the above-reported effects, an interaction effect of the vignette’s disgust level and disgust sensitivity ( b = 0.47, SE = 0.06, t [3602.40] = 8.53, p < 0.001; 95% CI [0.36, 0.58]), and the vignette’s harm level and disgust sensitivity ( b = 0.18, SE = 0.06, t [3602.80] = 3.28, p = 0.002; 95% CI [0.07, 0.29]). These two-way interactions were moreover qualified by a three-way interaction between disgust level, harm level, and disgust sensitivity ( b = −0.66, SE = 0.08, t [3602.82] = −8.742, p < 0.001; 95% CI [−0.83, −0.51]). Disgust sensitivity did not affect criminalization ratings of vignettes low in both harm and disgust ( b = 0.01, SE = 0.04, 95% CI [−0.07, 0.09]) or high in both harm and disgust ( b = 0.0005, SE = 0.04, 95% CI [−0.08, 0.08]). However, individual differences in disgust sensitivity did affect criminalization ratings for the low disgust, high harm vignette ( b = 0.19, SE = 0.04, 95% CI [0.11, 0.27]), and, even more so, for the low harm, high disgust vignette ( b = 0.48, SE = 0.04, 95% CI [0.41, 0.56]). In both cases, more disgust-sensitive individuals gave higher criminalization ratings than less disgust-sensitive individuals (see Fig. 2 ).

The slope indicating the relationship between disgust sensitivity and the criminalization of virtual child pornography (low harm, high disgust) is steeper than the slope for the financial crime scenario (high harm, low disgust).

A simulation-based power sensitivity analysis of the three-way interaction effect, estimating a small effect size of b = 0.15 and an alpha level of 0.05, yielded a power of 100%, 95% CI [99.63, 100]. Hence, our sample seemed sufficiently large to detect the effect size of interest.

The pattern was not fully replicated when we fitted a similar model to the dichotomous criminalization decisions. Here, we observed a significant main effect of disgust sensitivity ( b = 0.59, SE = 0.26, z = 2.29, p = 0.02; 95% CI [0.04, 1.17]), in addition to the previously reported main effects of the vignette’s levels of disgust and harm. Thus, participants were overall more inclined to decide that behavior should be criminalized, the greater their disgust sensitivity. However, only an interaction effect of the vignette’s harm level and disgust sensitivity reached significance ( b = 0.54, SE = 0.27, z = 2.04, p = 0.04; 95% CI [−1.10, 0 L.04]; see Table 1 ).

Note though, that those criminalization decisions for the vignettes that were either both low or both high in harm and disgust showed very little variation (i.e., 99% decided against, or for, respectively), which problematizes the fitting of more complex models including interaction terms with a continuous between-participant variable. Hence, we followed up this analysis with a more focal model that only included the high harm, low disgust and low harm, high disgust vignettes, which, according to our analysis of the continuous criminalization measure, should be most affected by individual differences in disgust sensitivity (given that these vignettes yielded more variation to begin with). In addition to a main effect of vignette similar to the previously observed pattern of results ( b = 1.18, SE = 0.09, z = 12.71, p < 0.001; 95% CI [1.00, 1.36]), this analysis yielded an interaction effect of disgust sensitivity and vignette ( b = 0.49, SE = 0.09, z = 5.30, p < 0.001; 95% CI [0.30, 0.68]). This indicates that, in line with the pattern of results for the continuous criminalization measure, disgust sensitivity was more strongly associated with criminalization tendencies of the low harm, high disgust vignette, than the low disgust, high harm vignette.

A simulation-based power sensitivity analysis of this interaction effect, estimating a small effect size of b = 0.15 (well below the observed effect size of 0.49), and an alpha level of 0.05, yielded a power of 91%, 95% CI [88.82, 93.88]. Thus, our sample seemed sufficiently powered to detect the effect size of interest.

Moderation by expertise

As a final step, we explored whether the above-reported effects of the vignette’s levels of harm and disgust and individual differences in disgust sensitivity were mitigated by professional expertise. To this end, we fitted a full-factorial model including disgust level (low and high), harm level (low and high), disgust sensitivity (standardized), and expertise level (1 = layman, 2 = expert). For the continuous criminalization ratings, in addition to the previously reported effects, we observed a significant two-way interaction of the vignette’s harm level and expertise level that was further qualified by a three-way interaction between the vignettes’ disgust and harm levels, and expertise ( b = 0.94, SE = 0.29, t [3592.41] = 3.19, p = 0.001; 95% CI [0.35, 1.53]), (see Fig. 3 ).

Both laypeople and legal professionals gave higher criminalization ratings for the virtual child pornography scenario (low in harm, high in disgust) compared to the financial crime scenario (high in harm, low in disgust).

As depicted in Fig. 3 , focused comparisons of the estimated means showed that whereas experts and laypeople displayed overall similar rating patterns, laypeople criminalized the high harm low disgust vignette ( M = 4.87, SE = 0.04) more than experts ( M = 3.23, SE = 0.15, b = 0.55, SE = 0.15, t [4745] = 3.64, p = 0.002). The four-way interaction between disgust level, harm level, disgust sensitivity, and expertise level was not significant, t [3592.31] = 0.83, p = 0.41, suggesting that both groups were equally affected by this individual difference variable. See for an overview of the effects, Table 3 . A similar model fitted to the dichotomous criminalization decisions did not yield any additional significant effects.

The results of this study offer an early empirical insight into the association between emotion (i.e., disgust) and the decision to criminalize behavior, for both laypeople and legal professionals. An analysis was conducted to determine what type of behavior (scored in terms of harmfulness and disgustingness) was more likely to be criminalized, and to what extent disgust sensitivity was associated with this decision. Our results partly confirmed what we expected. Participants—both laypeople and legal experts—were more likely to criminalize behavior that was highly disgusting (child pornography), even when it was low in harm (virtual child pornography), Footnote 17 compared to behavior that is high in harm but less disgusting (high-risk financial behavior), see Fig. 1 . This is interesting from a theoretical perspective, as one would expect that behaviors that are potentially very harmful would be criminalized more readily than those that are relatively harmless, particularly given the preeminent position of the harm principle in normative theory (Feinberg, 1984 ; Mill, 1859/ 2005 ; Stanton-Ife, 2022 ).

Additionally, our results indicate that individual variations in disgust sensitivity further moderated this pattern, such that disgust sensitivity was positively correlated with criminalization ratings for the high-risk financial behavior scenario (low disgust/high harm) and even more so for the virtual child pornography scenario (low harm/high disgust). In both scenarios, individuals who demonstrated a higher level of disgust sensitivity provided higher criminalization ratings compared to individuals with lower levels of disgust sensitivity (as depicted in Fig. 2 ). For the virtual child pornography vignette, this aligns well with previous literature, which has established a relationship between disgust and moral evaluations (Giner-Sorolla et al., 2018 ; Karinen and Chapman, 2019 ) and has shown that an individual’s level of disgust sensitivity is positively correlated with their level of moral condemnation, especially within the “purity-domain” (Chapman and Anderson, 2014 ; Jones and Fitness, 2008 ; Inbar and Pizarro, 2022 ; Karinen and Chapman, 2019 ; Van Leeuwen et al., 2022 ; Wagemans et al., 2018 ; Wang et al., 2019 ). For the high-risk financial scenario however, it is intriguing that disgust sensitivity is at all linked to this scenario, considering its abstract and non-visceral nature. However, there is also evidence suggesting that disgust sensitivity has a modest association with less visceral moral transgressions outside of the purity domain, like issues of fairness and honesty (Van Dillen et al., 2012 ), theft, fraud, and breaches of social conventions (Jones and Fitness, 2008 ; Chapman and Anderson, 2014 ), as well as instances of mild physical and psychological harm (Karinen and Chapman, 2019 ). It is worth noting, though, that this relationship is markedly less strong than the relationship with transgressions in the purity domain (Wagemans et al., 2018 ; for a meta-analytic review, see Donner et al., 2023 ). As noted, the positive association of trait disgust sensitivity with criminalization ratings appeared most pronounced for the virtual child pornography vignette (low harm/high disgust) and this pattern of results was mimicked for the dichotomous criminalization measure.

Existing literature also offers insight into why individuals with high levels of disgust sensitivity rated virtual child pornography as more deserving of criminalization compared to high-risk financial behavior. Consistent with the frameworks presented by Haidt and Greene (Haidt 2012 ; Graham et al., 2013 ; Greene, 2013 , 2014 ), virtual child pornography can be categorized as a personal violation within the purity domain, likely eliciting a strong emotional reaction. In contrast, the high-risk financial instrument, being abstract and impersonal, is less prone to provoke such a strong emotional response (Greene, 2009b , 2013 ). It is thus not surprising, and as hypothesized, that the association found in the virtual child pornography vignette is more pronounced than the association observed in the financial instrument vignette.

Disgust sensitivity did not affect criminalization ratings for the sweater (low harm, low disgust) and the actual child pornography vignettes (high harm, high disgust). This is probably due to little variation in the criminalization judgments for those scenarios (i.e., strong floor and ceiling effects, see Fig. 2 ). Wearing a sweater with clashing bright colors is not behavior that any individual (whether low or high in disgust sensitivity) would want to bring under the scope of the criminal law. And even a low disgust-sensitive individual would see the sense in criminalizing actual child pornography, because it is both extremely harmful and disgusting. Comparing the cases of the sweater and actual child pornography to virtual child pornography and financial crime suggests that individual variations in disgust sensitivity play a particularly prominent role in situations where the relationship between harm and disgust is ambiguous . Future research could address this idea further to assess the robustness of this effect.

Laypeople vs. legal professionals

A remarkable aspect in these results is the impact of legal education and expertise (i.e., professional judgment). Legal professionals displayed - to the same extent as laypeople - a similar rating pattern in criminalizing virtual child pornography (low harm, high disgust). Moreover, for experts and laypeople a similar significant relationship was found between disgust sensitivity and the criminalization of virtual child pornography. These findings seem to indicate that legal education and expertise do not mitigate the effects of emotional processing, which contrasts with earlier research findings reported by Baez et al. ( 2020 ). Also surprising is that experts criminalized the high-risk financial scenario (characterized as high in harm, low in disgust) less frequently than laypeople, as one would expect them to do so more often, again, given the dominant position of the harm principle in (continental) legal theory (Mill, 1859/ 2005 ; Stanton-Ife, 2022 ). Although one might argue in light of these findings that the design of the vignettes may not be entirely accurate, several other potential reasons might explain these observations. First, literature on motivated reasoning indicates that (legal) education and expertise do not necessarily act as a buffer against affective and emotional processing (Kahan et al., 2016 , 2017 ; Rachlinkski and Wistrich, 2017 ; Wistrich et al., 2015 ). On the contrary: gaining knowledge, education and expertise (all of which can reasonably be understood to cultivate proficiency in conscious, analytical forms of reasoning; Kahan et al., 2016 ) can cause people to adhere even more strongly to their affective and cultural (group-identity) beliefs because they are better at finding evidence and arguments to support their position and to rationalize away the rest (Kahan, 2015 , 2017 ; Baekgaard et al., 2019 ; Savadori et al., 2022 ). Second, regarding the high-risk financial scenario, it is also possible that legal experts emphasize the complexity of the potential harmfulness in the vignette more than laypeople, thereby opting for the default option: not criminalizing the behavior.

To fully elucidate the impact of legal expertise on moral decision-making and criminalization, further research is needed, particularly with stricter controlled vignettes, in different legal contexts and across varied forms of expertise. Also, the effects observed should be considered exploratory, as they were tested in a sample that may not have been adequately powered to detect the moderating effect of expertise. Hence, future studies should further examine the robustness of these findings. In addition, qualitative research in particular could offer deeper insights into the decision-making process of (legal) professionals.

Theoretical implications

Our observations could have important implications for criminalization theory. As indicated, scholars in this domain are generally assumed to rely heavily on deliberative, reasoned processes when theorizing about criminalization (Alexander and Sherwin, 2021 ; Dickson, 2016 ; Dworkin, 1977 ; Duff, 2014 ; Edwards, 2018 ; Levi, 1949/ 2013 ; Posner, 2010 ; Simester and Von Hirsch, 2011 ; Sunstein, 2008 ; Tadros, 2016 ). The question is whether this assumption is correct. Our findings, albeit preliminary, support the idea that emotional processes are significantly associated with the decision to criminalize behavior. Combining these findings with existing literature in cognitive science (Atari et al., 2023 ; Greene, 2014 ; Haidt, 2012 ; Sznycer and Patrick, 2020 ) could give rise to the hypothesis that legal scholars besides reasoned processes additionally rely on emotional processes when theorizing about criminalization (Lieberman and Patrick, 2018 ; Kahan, 1999 ; Nussbaum, 2004 ; Persak, 2019 ; Sunstein, 2008 ). Based on the same body of literature, a further hypothesis could be that while doing so, they often rationalize their emotional preferences, rather than reason towards a reflective outcome (Cushman, 2020 ; Greene, 2014 ; Haidt, 2001 ; Kahan, 2013 ; Lieberman and Patrick, 2018 ; Sood and Darley, 2012 ). However, this remains purely theoretical. Further research on this topic would be both interesting and beneficial, ideally combining quantitative and qualitative research methods.

As an aside, it is important to note that it has long been acknowledged by criminalization scholars that emotions can play a role in criminalization decisions (Devlin, 1965 ; Dworkin, 1977 ; Hart, 1963 ; Moore, 1997 ). Some scholars even find justification in them (Devlin, 1965 ; Moore, 1997 ). However, the more recent findings from moral psychology and cognitive neuroscience (Cushman, 2013 ; Greene, 2013 ; Greene & Young 2020 ; Haidt, 2012 ) attribute a much larger role to affective and emotional processes in moral decision-making than generally considered amongst (legal) scholars, including subsequent rationalization mechanisms (refer to endnote 3).

Towards a new perspective on criminalization

Theorizing this further, our findings could provide a stepping stone for the development of a new perspective on criminalization theory (Coppelmans, 2024 ), including a potential “ criminalization bias ” (refer to endnote 13). This perspective would be both descriptive and normative, and would place much more emphasis on affective and emotional processes, along with the accompanying rationalization mechanisms, than usual in traditional criminalization theory (e.g., Duff, 2014 ; Dworkin, 1977 ; Edwards, 2018 ; Feinberg, 1984 , 1988 ; Hart; 1963 ; Husak, 2008 ; Posner, 2010 ; Simester and Von Hirsch, 2011 ). Hence, the perspective could be named a “ dual-process theory of criminalization ” (Coppelmans, 2024 ; Winter, 2024 ), Footnote 18 a corollary of Greene’s dual-process theory of moral decision-making (Greene, 2013 , 2014 , 2017 ). The dual-process theory of criminalization would posit, first, that criminalization decisions and theory are grounded in both reflective and affective (intuitive) processes; Footnote 19 where the latter often result to rationalization (see also Sood and Darley, 2012 ). Second, that affective processes are especially prevalent in evaluating “personal” types of (criminal) behavior. Footnote 20 Third, and importantly, that our thinking about criminalization is not always “reliable”. It can be biased, especially when evaluating so-called “modern (criminal) offenses”: which leads to the introduction of a potential “criminalization bias”.

Traditional and modern criminal offenses

To elucidate, the deep evolutionary origins of our emotions and justice intuitions (Greene, 2013 ; Haidt, 2012 ; Jones, 1999 ; Sznycer and Patrick, 2020 ; Williams and Patrick, 2023 ) make it conceivable that our emotions are very well attuned to dealing with what we can call “traditional criminal offenses” (i.e., murder, burglary, aggressive behavior, sexual abuse: the so-called malum in se)—simply because our emotional reactions have millions of years of (cumulative cultural) evolutionary experience with them. Footnote 21 Our emotional system has learned to give a strong aversive signal when confronted with behavior that was threatening in our personal environment during our evolutionary history (Greene, 2013 ); which is subsequently reinforced in a culturally-specific manner (Atari et al., 2023 Dunstone and Caldwell, 2018 ; Graham et al., 2018 ; Henrich and Muthukrishna, 2021 ). Footnote 22 However, our emotions are much less attuned to deal with what we can call “ modern criminal offenses ”, offenses that are extremely recent on an evolutionary timescale (see also Coppelmans, 2024 ; Winter, 2024 ). These can be diverse, such as environmental harms (e.g., climate change, ecocide), abstract financial behavior (e.g., credit default swaps, dark pools, tax avoidance), offenses that arise from (bio)technological developments (e.g., A.I., robot sex, child sex dolls, deepfakes, genetic engineering, stem cell research, abortion) and offenses that stem from intercultural mixing (e.g., wearing a burqa or forced marriage). During evolutionary history, these offenses were certainly not threatening, as they simply did not exist, at least not in the personal environment of our ancestors (in their present form and intensity). Footnote 23 This new perspective on criminalization proposes that our thinking about specifically these modern criminal offenses can be biased—simply because our affective and emotional processes have very little (evolutionary) experience with them (Coppelmans, 2024 ; Greene, 2013 ; See also Brosnan and Jones, 2023 ). Footnote 24

The criminalization bias

This mismatch can be named the “criminalization bias” (Coppelmans, 2024 ), Footnote 25 in which the deviation can go two ways. Footnote 26 First, our emotional responses could ‘overreact’, that is, react too quickly and too harshly, to personal , emotion-provoking offenses that, in reality, are quite harmless Footnote 27 (e.g., incest while using contraceptives [Haidt, 2001 ], child sex dolls, virtual child pornography). Second, our emotions could “underreact”, that is, react very weakly or do not react at all, to abstract and impersonal , non-emotion-provoking offenses that in reality can be quite harmful (e.g., abstract and invisible hazards like climate change, artificial intelligence, tax avoidance, high-risk financial behavior, etc.) (see also Greene, 2013 ). Both ways leading to biased evaluations of harmfulness in light of criminalization decisions. Footnote 28 Crucially, it should be noted that while these ideas are grounded in theory, more empirical research is necessary to corroborate them.

Limitations and future directions

Our research has several limitations. First, considering our vignettes, we deliberately selected renowned examples from criminalization theory and practice (e.g., Feinberg 1985 ; Simester and Von Hirsch 2011 ). This approach was motivated by a desire to maximize ecological validity. We prioritized scenarios that already existed in criminalization literature, ensuring their relevance and applicability to (a) our legal professional participants and (b) the judicial part of our audience: legal scholars, policymakers and legal practitioners. However, the disparities in the orthogonality, length and complexity of the vignettes, notably in the high harm, low-disgust financial scenario, might have introduced potential confounds. While our primary goal was to ensure each vignette clearly represented its intended scenario, this occasionally resulted in additional detail, which may have influenced participants’ perceptions or responses. Future research should consider standardizing vignette complexity to minimize potential biases. Footnote 29 Also, the use of less complex “abstract/financial crime”-scenarios is advisable (such as tax avoidance by large corporates or large scale privacy infringement). Additionally, and importantly, we recognize the need for more rigorous testing and validation of these scenarios in subsequent studies.

Second, the positive association of trait disgust sensitivity with criminalization ratings appeared most pronounced for the low harm/high disgust vignette (virtual child pornography). However, it is good to note that the observed effect primarily rests on a relative claim: that the effect size of the disgust sensitivity effect is larger for the low harm/high disgust scenario (virtual child pornography) than for the high harm/low disgust scenario (abstract financial crime). Footnote 30 It would therefore be prudent to exercise caution in drawing overly definitive conclusions. Also, it is important to note that we used only one vignette per harm/disgust-manipulation, which limits the generalization of the results to other behaviors. For future research it is advisable to use multiple vignettes and examine various types of behavior.

Third, due to its extensive documentation in the literature, our study focused exclusively on disgust as a proxy for the role of emotion (Giner-Sorolla et al., 2018 ; Rozin et al., 2008 ; Russell and Giner-Sorolla, 2013 ). However, other affective and emotional processes could also play a role in criminalization decisions (Haidt, 2012 ; Greene 2013 ). It has been suggested that sensitivity to various affective states (not just disgust, but also anxiety and anger) predicts extremity in evaluative judgments (Cheng et al., 2013 ; Landy and Piazza, 2019 ). This would indicate that the connection between disgust and moral condemnation is part of a wider relationship between (negative valenced) affect and moral judgment (Piazza et al., 2018 ). Therefore, for future research, it is important to explore the role of various emotions in criminalization, by focusing on the diverse ways in which affective and emotional processes contribute to criminalization decisions (Atari et al., 2023 ; Cushman, 2013 ; Greene and Young, 2020 ; Greene, 2023 ).

Fourth, concerning our legal professional participants, it is important to highlight that making conclusions based solely on the responses from these experts can be intricate due to the smaller effect size (N = 103) when compared to the overall participant sample (N = 1297). Furthermore, our group of legal experts was varied, comprising legislative lawyers, legal policymakers, lawyers and judges. For future research, maintaining greater consistency within the participant group might be advisable. It could also be advisable to provide legal professionals with a monetary incentive, which appears to enhance the accuracy of responses from professionals (Kahan, 2015 ).

Finally, while this study may not present novel insights from a purely psychological perspective, it aims to contribute to the interdisciplinary field of legal-psychological research, particularly within the realm of criminalization. The limitations we have identified highlight the complex interplay between emotional responses and criminalization decisions. Our findings suggest a potential link between these variables, a link that seems to hold true even among experienced legal professionals. To unravel the precise dynamics of this relationship, future research will need to employ more sophisticated research methodologies and deploy vignettes that have undergone more rigorous validation.

Practical implications

Lastly, our findings have potential practical implications. They suggest that emotion, specifically disgust, is associated with the decision to criminalize behavior, in particular virtual child pornography. This might be informative for legislatures and policymakers. As previously noted, many countries have subjected (realistic) virtual child pornography to criminal law, reasoning that such content is harmful (Bird, 2011 ; Gillespie, 2018 ; Williams, 2004 ; Witting, 2020 ). Yet, our study provides an alternative, albeit preliminary, perspective: the decision to criminalize virtual child pornography might stem less from reasoned judgment and more from an emotional response, possibly triggered by associations with real child sexual abuse.

This descriptive finding (if it holds up) could have practical, normative implications. That is, virtual child pornography resembles a so-called “ modern criminal offense ”: it is a consequence of recent technological developments that allow for highly realistic simulations of behavior. The (cumulative cultural) evolutionary origins of our emotions (Brosnan and Jones, 2023 ; Greene, 2013 ; Haidt, 2012 ; Henrich and Muthukrishna, 2021 ; Sznycer and Patrick, 2020 ; Williams and Patrick, 2023 ) implicate that it is quite conceivable that our emotions are not well attuned to evaluate the danger posed by such virtual types of offenses: our emotional moral thinking is “unfamiliar” with them. During thousands of years of (cultural) evolution, sex with children was only very real, and very harmful: Footnote 31 it was never realistically virtual. Footnote 32 Thus, it can be argued that during these thousands of years our emotions have (genetically, culturally, individually) “learned” to react strongly to anything that merely resembles real sex with children. Footnote 33 Unsurprisingly, this leads to bias when evaluating the legitimacy of virtual child pornography: our emotional moral brain simply does not have (cumulative cultural) evolutionary experience with the virtual version and therefore reacts (one could say: ‘overreacts’) with extreme aversion. That is, ‘seeing’ danger where there is none. Footnote 34 This way, virtual child pornography may well be a moral illusion : a type of conduct that is perceived as extremely harmful, while in reality, it is not. Footnote 35 In other words, our cognition could contain a criminalization bias when considering the harmfulness of virtual child pornography in the context of criminalization. Footnote 36

Normative implications?

If we are willing to accept these descriptive hypotheses, the normative question arises: do we consider our emotions—which very likely evolved in order to prevent real child sex abuse—to be a morally relevant factor when evaluating virtual child sex abuse? And if not, are we willing to accept that, in the words of Luck ( 2009 ), we should “bite the virtual bullet”? That is, setting our emotions aside and taking a more nuanced, reasoned approach to virtual child sexual material: solely evaluating it on its consequences (philosophically, this constitutes a debunking argument ; Greene, 2014 ; Königs, 2022 ; Singer, 2005 ). Should it perhaps be legalized for certain populations, under certain restrictions, as a means for research and/or treatment (Malamuth and Huppin, 2007 ; McLelland, 2012 ; McLelland and Yoo 2007 ; Seto, 2013 )? Footnote 37 On the other hand, the answer can also be affirmative: yes, we should consider our emotions a morally relevant factor when criminalizing virtual child pornography. In that case, however, it is important to establish the criminalization on the proper normative foundation: not based on the harm principle (Feinberg, 1984 ; Mill, 1859/ 2005 ), as is still formal practice in many countries, but rather on the offense principle, by deeming it “obscene” (Feinberg, 1985 ). Footnote 38 Determining which course of action is the most ethical, is a matter for another time. Looking forward to a future of ever-advancing technological innovations and ever-changing criminal offenses, these questions will become more and more pressing. The ultimate answer, however, remains with us.

The results from this study offer an empirical, albeit preliminary, insight into the role of emotion in criminalization decisions. While the most central legal principle in criminalization theory is the harm principle (Mill, 1859/ 2005 ), both laypeople and legal professionals were more inclined to criminalize virtual child pornography (characterized as low in harm) compared to high-risk financial behavior (characterized as high in harm). Furthermore, disgust sensitivity was associated with these decisions, particularly concerning the virtual child pornography scenario—again, for both laypeople and legal professionals. This indicates that emotion can be associated with the decision to criminalize behavior. Theoretically, this could have multiple implications for criminalization theory, one of which being the potential presence of a “criminalization bias” in thinking about morality and the law (Coppelmans, 2024 ). More research is encouraged to corroborate these findings and to draw more substantive conclusions.

From a broader perspective, the findings also touch upon another point: the contribution of empirical cognitive science to philosophy of law, more specifically to criminalization theory. Criminalization is a fundamental topic, with substantial stakes at hand. Therefore, it seems crucial that we “know what we are doing” when considering the criminalization of behavior, in other words, that we understand the actual (cognitive, philosophical) foundations upon which a decision is based or a theory is constructed. Moral psychology and cognitive neuroscience embody the tools to gain this understanding (Brosnan and Jones, 2023 ; Greene and Young, 2020 ). Footnote 39 It seems therefore only rational to use these scientific tools to garner deeper insights into the foundations of our moral thinking—ideally leading to better-informed decisions and more refined theories in criminal law. This empirical way appears to be the right direction to pursue (Knobe and Shapiro, 2021 ), towards criminalization theory as a truly interdisciplinary enterprise.

Data availability

The datasets generated and analyzed during the current study are available in the Open Science Framework repository: https://osf.io/a49qe .

This is a topic of debate: most notably between defenders of the harm principle and “legal moralists”. Legal moralism is the belief that [ prima facie ] it can be morally justifiable to prohibit certain conduct based on the belief that it is morally wrongful (i.e., inherently immoral or immoral based on other normative ethical principles), even if it does not cause harm or offense to the individual or to others (Duff, 2014 ; Moore, 1997 ; Stanton-Ife, 2022 ). Another theoretical justification for criminalization is the offense principle, e.g., the “obscenity” of the conduct (Feinberg, 1985 ). For the purpose of this study, we won’t delve deeper into legal moralism or the offense principle. Relevant is that criminalization is always a balancing act of considerations, and this study focuses on one of these considerations, that is “harmfulness”. Harm itself is defined as a “(potential) setback of a welfare interest, i.e., one’s body or property, that is a wrong” (see also Feinberg, 1984 ).

This view is grounded in dual-process theory (Kahneman, 2011 ; Sherman et al., 2014), with affective and reflective processes also referred to as “System 1 and System 2”, “moral intuition and moral reasoning”, “habit and goal-directed behavior” and more recently “model-free and model-based learning algorithms” (Cushman, 2013 ; Greene, 2013 , 2017 ; Haidt, 2012 ).

Two considerations are noteworthy. (1) First, there is extensive literature concerning the relationship between emotion and other area’s within criminal legal theory (Bandes et al., 2021 ; Brosnan and Jones, 2023 ; Jones, 1999 ; Kahan and Nussbaum, 1996 ; see for an overview Abrams and Keren, 2010 ; Patrick, 2015 ; Persak, 2019 ); however, the role of emotional processes in specifically the criminalization of behavior was under-explored (Persak, 2019 , p. 48. Some notable exceptions are Kahan, 1999 ; Moore, 1997 ; Nussbaum, 1999 , 2004 ; Prince, 2010 ; Sunstein, 2008 ). (2) Second, within criminalization theory, it has long been acknowledged that emotions can play a (key) role in criminalization decisions (Devlin, 1965 ; Dworkin, 1977 ; Hart, 1963 ; most thoroughly: Moore, 1997 ). Some scholars even find justification in them (Devlin, 1965 ; Moore, 1997 ). However, the literature on criminalization has yet to extensively incorporate the more recent findings from moral psychology and cognitive (computational) neuroscience, on affective and emotional processing in moral judgment (Haidt, 2012 ; Greene and Young, 2020 ), dual-process morality (Greene, 2013 , 2023 ), evolutionary origins of morality (Joyce, 2007 ; Greene, 2013 ), model-based and model-free learning mechanisms (Cushman, 2013 ; Crockett, 2013 ), biases and heuristics (Kahneman, 2011 ), rationalization (Cushman, 2020 ; Haidt, 2001 ) and debunking arguments (Greene, 2014 ; Königs, 2022 ). An important difference is that the vast majority of legal scholars still seem to regard rational deliberation and legal reasoning as doing ‘most of the work’ in moral judgment (with Moore, 1997 as a notable exception), and consider ‘emotion’ as having a minor role, or, at least, fall within our awareness (Alexander and Sherwin, 2021 ; also according to Sunstein, 2008 ). This differs from recent models on moral judgment, which consider affective and emotional processes (or: model-free learning algorithms) to be doing ‘most of the work’ and often operating unconsciously; whereas reflective and reasoned processes are considered to play a lesser role than previously thought, and are often a rationalization (Greene, 2008 , 2013 , 2014 ; Haidt, 2001 ; Kahneman, 2011 ; see also Cushman, 2020 ; Sood and Darley, 2012 ).

The studies of Sood and Darley ( 2012 ) and Baez et al. ( 2020 ) are indirectly related.

Following the rise of the broader “experimental philosophy” movement (Knobe and Nichols, 2017 ), the discipline of “experimental jurisprudence” (also known as experimental philosophy of law or empirical legal studies) has emerged in recent years (Knobe and Shapiro, 2021 ; Prochownik, 2021 ).

In this paper, we define “virtual material” as material that is created without involving any real or identifiable minors, such as computer-animated videos and images, cartoons, drawings, sculptures, and paintings. This material is also known as “virtual child sexual exploitation material”, in reference to its potential harmful effects. For more information on this definition, refer to Gillespie ( 2018 ).

E.g., Child Pornography Prevention Act 1996 (invalidated by Ashcroft v. Free Speech Coalition, 2002 ); PROTECT Act 2003 ; Convention on the Protection of Children against Sexual Exploitation and Sexual Abuse 2007 (Lanzarote Convention); Coroners and Justice Act 2009 ; Combating sexual abuse and sexual exploitation of children and child pornography (Directive 2011 /93/EU).

Of course, this is a topic of much debate, not just regarding whether the conduct is harmful, but also what constitutes harm. For instance: young children viewing virtual child sexual material through a search engine is harmful as well. Also, some virtual material is indistinguishable from actual material, which could harm the investigation and prosecution of the latter. For present purposes, we want to further narrow the definition of virtual child pornography to only include material provided by healthcare providers and only accessible in secure, online environments. This way, we can concentrate on the risk that virtual child pornography may increase child abuse either by fueling it after consumption or through other means.

Within Moral Foundations Theory, Haidt and colleagues (Atari et al., 2022 ) propose that our moral intuitions are based on various “psychological foundations”, which they categorize as—amongst others—Care, Equality, Proportionality, Loyalty, Authority, and Purity. The category of Purity pertains to intuitions about avoiding physical contamination and spiritual degeneration of the mind and soul, as well as the degradation of society as a whole, encompassing virtues like chastity, wholesomeness, and control of desires. The emotion of disgust is thought to translate into moral beliefs regarding the purity of the body and soul, and the impurity of groups with a different, perceived ‘unclean’ lifestyle such as immigrants, other religious and cultural groups and groups with a sexual lifestyle that deviates from the norm, e.g., LGBTQIA) (Atari et al., 2022 ; Graham et al., 2013 , 2018 . See also: Gray et al., 2023 ; Piazza and Sousa, 2023 ).

This could be a general sensitivity to experience disgust (measurable by the DS-R; Haidt et al., 1994 , modified by Olatunji et al., 2007 ) or a sensitivity to specifically experience body odor disgust (measurable by the BODS; Liuzza et al., 2017 ).

This is not the perceived harmfulness of the conduct by participants; it is the “objective” harmfulness of the conduct based on existing literature (see Introduction).

It is important to note that, evidently, not only “genetic” mechanisms but also (cumulative) cultural and individual learning mechanisms influence our affective and emotional reactions to moral cases (Graham et al., 2013 , 2018 ; Greene, 2013 , 2017 ). The term “evolutionary” in this paper therefore refers to both genetic and cumulative cultural evolution (Henrich and Muthukrishna, 2021 ).

We define bias as an (unconscious) systematic and specific deviation in our cognition from a certain norm, in this case the norm being the actual harmfulness of behavior when evaluating criminalization decisions. The proposed “criminalization bias” is thought to be driven by (the absence of) a strong emotional, model-free response, and could therefore also be referred to as an “emotional bias in criminalization decisions”. It is regarderd as a broadly applicable bias within criminalization theory, explaining its name. The bias is based on a general-purpose heuristic: the affect heuristic (Kahneman, 2016 ; Slovic et al., 2002 ). The question what factors give rise to our affective responses (and could lead to biased criminalization decisions) is still up for research. Personalness of harm could play a role, so can virtuality , the presence of harm as a side-effect (Greene, 2014 ) or the perspective one takes when evaluating harm (Miller et al., 2014 ), etc.

Again, when provided by healthcare providers and only accessible in secure, online environments.

And leading to a perception of the behavior as (very) harmful (see also Sood and Darley, 2012 ).

And leading to a perception of the behavior as low(er) in harm. When the behavior has already been criminalized, it may result in a decision either not to prosecute or to impose a relatively milder sentence. However, this area warrants further investigation.

Again, harmfulness was determined by the researchers based on existing literature (refer to Introduction). Of course, further research with other types of behavior is warranted to corroborate these findings.

At the same time but independent of each other, both Jozef Nicola Coppelmans and Christoph Winter originated the same idea: combining Joshua Greene’s insights with traditional criminalization theory, leading to a ‘Dual-Process Theory of Criminalization’. See Coppelmans ( 2024 ) and Winter ( 2024 ).

In psychology, the computational perspective is on the rise (Cushman and Gershman, 2019 ). Hence, the “dual” processes are increasingly characterized in computational terms, such as model-based and model-free reinforcement learning algorithms (Cushman, 2013 ; Crockett, 2013 ; Greene, 2017 ). Model-based algorithms correspond with more “reasoned” thinking: it attaches values to outcomes, and indirect to actions, based on an explicit model of cause and effect relationships between actions and their outcomes, and the values attached to those outcomes. In contrast, model-free algorithms correspond with intuitive thinking: it relies on accessing values directly attached to actions, based on previous reinforcement. It is important to note that model-based judgment is not fully devoid of emotions, as values must still be attached to outcomes (Greene, 2023 ; Patil et al., 2021 ).

Again: “personal” refers to conduct that resembles threatening behavior found in Homo sapiens’ personal environment during evolutionary history, such as hitting, killing, rape and other forms of prototypically violent behavior (Greene, 2009a , 2013 ).

In our opinion, this also explains why certain crimes are called malum in se , i.e., “evil or wrong in themselves .” They simply feel intrinsically wrong, because our emotional processes have had (cumulative cultural) evolutionary “experience” with them for millions of years (Coppelmans, 2024 ; Winter, 2024 . See also Greene, 2013 ; Sznycer and Patrick, 2020 ).

According to the recent dual inheritance theory (Henrich and Muthukrishna, 2021 ), “biological” evolution encompasses both genetic and cumulative cultural evolution. Haidt employs the terminology “organized in advance of experience” (Graham et al., 2018 ).

Of course, cultural variation did exist across different groups. However, during the course of evolutionary history, intergroup mixing did not occur to the extent that it does currently (Haidt, 2012 ; Greene, 2013 ).

Unrelated to criminalization, the same “evolutionary mismatch” argument is made by Gigerenzer ( 2000 ) and Jones ( 2001 ). Greene ( 2017 ) reframed his argument in terms of “the bad data and bad training problem”. And again, “experience” means experience based on genetic, cumulative cultural and individual learning mechanisms (Henrich and Muthukrishna, 2021 ).

See also Footnote 13. We define bias as an (unconscious) systematic and specific deviation in our cognition from a certain norm, in this case the norm being the actual harmfulness of behavior when evaluating criminalization decisions. In general, the ‘norm’ can refer to (a) an individual’s personal goals or motives, (b) the objective truth, or (c) another normative model such as the rules of statistics, logic or in this case: a legal principle (see also Kahneman, 2011 ).

Again, we assume that the harm principle is the most central limiting principle used by (Western) governments to criminalize behavior (Feinberg, 1984 ). Of course, this is a topic of debate (Stanton-Ife, 2022 ).

“Harm” is a concept that can be interpreted in a multitude of ways (Feinberg, 1984 ; Hart, 1963 ; Simester and Von Hirsch, 2011 ). In this context, We refer to harm as a direct or potential infringement on (the interest of) one’s body or property. Yet, harm can be defined in much broader terms such as “harm to society”, “harm to familial institutions”, or “harm against public morals”. However, we believe it’s more beneficial to refrain from such expansive definitions of harm, especially given the risk that our brains might construct rationalizations for our emotionally-driven moral preferences.

This descriptive finding could have normative implications. It can introduce a so-called debunking argument (Königs, 2022 ), following the normative question: should we trust our affective and emotional processes (and the accompanying rationalizations) when evaluating “modern criminal offenses”? And, if not, are we willing to debunk our legal reasoning and exclude our (unconscious) emotional reactions from our argumentation, possibly leading to a different moral evaluation (Coppelmans, 2024 ; Winter, 2024 ; Greene, 2013 , 2014 ; Singer, 2005 )? This way, a “ dual-process ” perspective on criminalization could have descriptive and normative implications, offering many opportunities for further research in criminalization theory—both empirically and theoretically.

For example, other child-related scenarios could be “hitting a child” for the high harm low-disgust scenario (at least in terms of purity-related disgust), whereas a child dressed in an unconventional way could be a low-harm low-disgust control scenario. Gratitude is owed to Reviewer 1 for this valuable suggestion.

We extend our gratitude to Reviewer 2 for this insightful recommendation.

One could argue that, throughout history, there have been instances of marriage and sexual relationships between adults and children as young as 10 years old. However, it is not known if sexual relationships with very young children under the age of ten were ever considered acceptable practice. It is very well conceivable that the human affective and emotional reactions have evolved to strongly react against such behavior.

At least not very realistically and digitally virtual.

Again, “learning” refers to the process of acquiring skill and knowledge through a complex combination of genetic mechanisms, cumulative culturally evolved mechanisms and social/individual learning mechanisms: they all reflect “trial-and-error learning”, it is only the time scales and transmission mechanisms that differ (Greene, 2017 ; Henrich and Muthukrishna, 2021 ).

In somewhat other terms, Miller ( 1997 ) made the same argument claiming that under the influence of disgust, we too readily blame persons for deformities of character over which they have no control.

Again, for the purposes of this discussion, we will assume that the harm caused by virtual child sexual material is low, based on current literature. The primary objective is to demonstrate the influence of emotion on criminalization decisions, and how it could potentially lead us astray. If research clearly shows that virtual child pornography leads to harmful behavior, then obviously it should be a criminal offense.

Of course, there are other justifications for criminalization, such as the moral wrongfulness or obscenity of the material. However, and crucially, the lawmakers mentioned above did not consider these grounds, they specifically referred to the harmfulness of the behavior, making it possible for a criminalization bias to be present.

For instance, to be consumed only within secure online environments and with a prescription from a medical specialist. Of course, only when there is absolutely no involvement of actual children.

As is done by the United States in the PROTECT Act 2003 , section 1466A (see also Bell, 2012 ; Bird, 2011 ; Miller v. California, 1973 ). Philosophically, the question is whether mere offensiveness is a sufficient ground for criminalization (Feinberg, 1985 , 1988 ). However, this goes beyond the scope of this article. For now, our goal is simply to illustrate the potential impact that empirical moral psychology may have on our legal-ethical thinking and on normative questions surrounding criminalization.

They embody not only empirical tools, but also theoretical tools, such as work on affective and emotional processing in moral judgment (Haidt, 2012 ; Greene and Young, 2020 ), dual-process thinking (Greene, 2013 , 2023 ), the evolutionary origins of morality (Joyce, 2007 ; Greene, 2013 ; Sznycer and Patrick, 2020 ), model-based and model-free reinforcement learning (Cushman, 2013 ), biases and heuristics (Kahneman, 2011 ), rationalization (Cushman, 2020 ; Haidt, 2001 ) and debunking argumentation (Greene, 2014 ; Königs, 2022 ). For an overview, see Coppelmans ( 2024 ).

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Acknowledgements

We thank the participants of this study for their willingness to participate in scientific research. We are also grateful to Kristina Salontaji and Kiki Twisk for their help in organizing the data and to Pim Bellinga, Wim Bosch, Tineke Cleiren, Tom Heyman, Arne Popma, and Jeroen ten Voorde for their helpful comments and feedback, with particular gratitude to Annemarij Wams for her invaluable support.

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JC initiated and coordinated the research project, and collected the data. JC and LD conceptualized the idea and created the research design. LD and FW organized and analyzed the data. JC wrote the introduction, methods, and discussion sections, while LD and FW wrote the results paragraph. All authors reviewed and revised the manuscript. LD supervised the research project.

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Coppelmans, J.N., Wagemans, F.M.A. & van Dillen, L.F. An empirical investigation of emotion and the criminal law: towards a “criminalization bias”?. Humanit Soc Sci Commun 11 , 513 (2024). https://doi.org/10.1057/s41599-024-02842-8

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Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and qualitative market research methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
Qualitative research: Qualitative research methods are used to gather non numerical data. It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

Observational method: Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

LEARN ABOUT: Best Data Collection Tools

Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

It is used to authenticate traditional research through various experiments and observations.
This research methodology makes the research being conducted more competent and authentic.
It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
The level of control in such a research is high so the researcher can control multiple variables.
It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

LEARN ABOUT: Social Communication Questionnaire

Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

LEARN ABOUT: 12 Best Tools for Researchers

With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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Period Poverty And Law: An Empirical Research

INTRODUCTION

During the catastrophic and unprecedented situation during the Covid-19 pandemic in the year 2020, the people encountered the longest and most stringent lockdown at the national level. Though the lockdown was imposed with an aim to benefit the people at a large, it resulted in the destruction of the global economy, earnings of daily-wage workers, et al. The underprivileged strata of Indian society, as a result of this lockdown, were forced into poverty in such a manner that they even fail to feed themselves as well as their children. The rise in poverty during these times backed with pre-existing poverty unleashed havoc on the menstrual health of women in India.

In order to cope up with the menstrual concerns, women started using unhygienic measures such as clothes, tree leaves, etc. Though the Government of India had launched a scheme, the pandemic even hindered its functioning. Women in poverty started spending money by giving importance to other concerns than their menstrual hygiene. As a result of which, they suffered major physical and psychological implications. The study aims to delve into the issue of menstrual hygiene management and concerns over period poverty to showcase the prevalence of period poverty in India with the help of the data collected from the respondents. To obtain untainted data, the data collected was kept anonymous in nature and the data poll comprised 115+ girls/women hailing from different institutions of India.

RESEARCH OBJECTIVE

To study and analyse psychological aspects associated with Period Poverty.
To understand the prevalence of Period Poverty in Indian society.
To study and determine the awareness among the masses regarding Menstrual Hygiene.
To analyse how social taboos negatively affect the psychological well-being of girls/women.
To study the interrelation between economic well-being and menstrual hygiene.
To study the negative consequences that girls/women face after facing period poverty.

RESEARCH HYPOTHESIS

Period Poverty exists in Indian Society. (Figure 03)
Economic wellness plays a major role in determining the victims of Period Poverty. (Chart 01, Figure 02)
Lack of awareness is a major concern, facilitating Period Poverty. (Figure 7 & Chart 2)
Period Poverty adversely affects the psychology of a girl/woman. Figure 08
Social taboos, Myths and Misconceptions further augment Period Poverty. Figure 06
Authorities fail in providing access to basic menstrual hygiene facilities. Chart 03

RESEARCH METHODOLOGY

SAMPLE SIZE

In this study, the researcher has successfully received responses from 117 girls and/or women. The researched has sent his questionnaire to female students of different universities across India that consisted of the National University of Study and Research in Law, Ranchi (Jharkhand), National Academy of Legal Studies and Research (Telangana), Chanakya National Law University, Patna (Bihar), National Law University, Cuttack (Orissa), Himachal Pradesh National Law University, Shimla (Himachal Pradesh), Maharashtra National Law University, Nagpur (Maharashtra), Christ University, Bangalore (Karnataka), Bharati Vidyapeeth Deemed University, Pune (Maharashtra), et al. The age group in the study ranged between 14 to 20 years. Moreover, 15 girls and/or women in part of the survey were of 20+ years. The collection of responses was turned off on Thursday (October 14, 2021).

To facilitate the present research work a ‘ Questionnaire Method ’ involving a total of 08 were used. The Questionnaire was circulated by a way of Google form and proper steps were taken to ensure the anonymity of the person responding. The type of questions in the first part of the Questionnaire i.e., Questions No. 1 and 2 give the general information about the person taking the test, that is, age and family income of the person. Further, these questions were accompanied by whether they have ever faced Period Poverty and if yes, then what impact they have suffered as a result of it.

Ordinarily, in the Questionnaire-based research method, the questions can be of three types viz., open-ended, close-ended or the combination of the first two. The ‘open ended’ questions are such that it is left on the part of the respondent to answer the set of questions as per what she feels about the topic on which the research is being conducted. While in the second type of question i.e., ‘close ended’ questions, the respondent has no liberty but to answer the questions from the provided answers in the form of options. The present research employs the third method, which is an amalgamation of both of these two types of questions. The Questionnaire circulated is attached as Annexure A at the end of the paper.

RESEARCH DESIGN

The research design employed in the study is Survey Research Design . Survey Research Design involves the collection of data by asking questions and recording the response provided by the people. The research is conducted with an aim to examine the prevalence of Period Poverty in India and further delve into the psychological impacts that girls/women face as a result of it.

For the successful competition of this study and in order to collect the data from the defined set of the population across India, the researcher created a ‘Google Form’ which was circulated among the target groups through ‘ Snowball Sampling ’. Snowball Sampling is a method where the researcher recruits (asks) other participants to circulate it in their peer groups, family groups, friend groups, etc. so as to collect data. In the present case, the researcher asked his friends, both boys and girls, to circulate the form through WhatsApp and/or any other social media app among their groups. The researcher also asked the girls to not only share but fill it as well.

The major reason behind employing the method of Google Form was that it is an easier way of collecting data and ensures that the information collected is bereft of any anonymity. The link of the attached Google Form (Annexure A) was circulated with the declaration that the survey is being done for educational purposes and that the confidentiality of their responses will be maintained, throughout this study and even after its completion. The estimated time for the completion of the survey was calculated to be 02 minutes. No time limit was set, and the individuals were given the liberty to answer accurately. In addition to these sets of instructions, it was categorically stated that only women and/or girls are eligible to fill this form.

Moreover, the description clearly mentioned the definition of Period Poverty. It was defined as “ When a girl not being able to afford menstrual products (sanitation facilities in schools/colleges/hospitals/workplace due to lack of infrastructure or facilities available). Subjected to discrimination inside her home during the time periods by her family members. Family members base their argument on religion/religious scriptures, however, the religion neither implicitly nor explicitly mentions any such thing. Lack of awareness in you (the respondent) yourself. ”

The Google Form was first rolled out on October 4, 2021, after being examined by the concerned faculty and the responses were recorded till October 14, 2021. Continuous monitoring of the responses was made in order to ensure that the recorded data does not include any unnecessary responses and responses from those people who did not belong to the prescribed age limit. After having the required responses, data was sent to the excel sheet for the making of the graphs and at the end, the graphs were ready on the basis of which analysis was done and produced below in the form of ‘Discussion’.

Author: Kaustubh Kumar, in case of any queries please contact/write back to us via email to [email protected] or at Khurana & Khurana, Advocates and IP Attorney.

ORIGINAL RESEARCH article

Decomposing the inequalities in the catastrophic health expenditures on the hospitalization in india: empirical evidence from national sample survey data.

1 Department of Social and Public Health, College of Health Sciences and Professions, Ohio University, Athens, OH, United States
2 Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
3 Chettinad Hospital and Research Institute, Chennai, Tamil Nadu, India
4 Taibah University, Medina, Saudi Arabia

Introduction: Sustainable Development Goal (SDG) Target 3.8.2 entails financial protection against catastrophic health expenditure (CHE) by reducing out-of-pocket expenditure (OOPE) on healthcare. India is characterized by one of the highest OOPE on healthcare, in conjunction with the pervasive socio-economic disparities entrenched in the population. As a corollary, India has embarked on the trajectory of ensuring financial risk protection, particularly for the poor, with the launch of various flagship initiatives. Overall, the evidence on wealth-related inequities in the incidence of CHE in low- and middle-Income countries has been heterogenous. Thus, this study was conducted to estimate the income-related inequalities in the incidence of CHE on hospitalization and glean the individual contributions of wider socio-economic determinants in influencing these inequalities in India.

Methods: The study employed cross-sectional data from the nationally represented survey on morbidity and healthcare (75th round of National Sample Survey Organization) conducted during 2017–2018, which circumscribed a sample size of 1,13,823 households and 5,57,887 individuals. The inequalities and need-adjusted inequities in the incidence of CHE on hospitalization care were assessed via the Erreygers corrected concentration index. Need-standardized concentration indices were further used to unravel the inter- and intra-regional income-related inequities in the outcome of interest. The factors associated with the incidence of CHE were explored using multivariate logistic regression within the framework of Andersen’s model of behavioral health. Additionally, regression-based decomposition was performed to delineate the individual contributions of legitimate and illegitimate factors in the measured inequalities of CHE.

Results: Our findings revealed pervasive wealth-related inequalities in the CHE for hospitalization care in India, with a profound gap between the poorest and richest income quintiles. The negative value of the concentration index (EI: −0.19) indicated that the inequalities were significantly concentrated among the poor. Furthermore, the need-adjusted inequalities also demonstrated the pro-poor concentration (EI: −0.26), denoting the unfair systemic inequalities in the CHE, which are disadvantageous to the poor. Multivariate logistic results indicated that households with older adult, smaller size, vulnerable caste affiliation, poorest income quintile, no insurance cover, hospitalization in a private facility, longer stay duration in the hospital, and residence in the region at a lower level of epidemiological transition level were associated with increased likelihood of incurring CHE on hospitalization. The decomposition analysis unraveled that the contribution of non-need/illegitimate factors (127.1%) in driving the inequality was positive and relatively high vis-à-vis negative low contribution of need/legitimate factors (35.3%). However, most of the unfair inequalities were accounted for by socio-structural factors such as the size of the household and enabling factors such as income group and utilization pattern.

Conclusion: The study underscored the skewed distribution of CHE as the poor were found to incur more CHE on hospitalization care despite the targeted programs by the government. Concomitantly, most of the inequality was driven by illegitimate factors amenable to policy change. Thus, policy interventions such as increasing the awareness, enrollment, and utilization of Publicly Financed Health Insurance schemes, strengthening the public hospitals to provide improved quality of specialized care and referral mechanisms, and increasing the overall budgetary share of healthcare to improve the institutional capacities are suggested.

1 Introduction

The Universal Health Coverage (UHC) has been proclaimed as the third major transition in health, after the demographic and epidemiological transitions ( 1 ) and has become the focal point of health policy discourse as the world made transition from millennium development goals (MDSs) to sustainable development goals (SDGs). Goal 3.8 of the SDG Agenda enunciates to achieve the UHC and encompasses two components: (i) Indicator 3.8.1–Coverage of essential health services (defined as average coverage of essential services based on tracer interventions that include reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, and service capacity and access, among the general and most disadvantaged population). (ii) Indicator 3.8.2–Incidence of catastrophic health spending (defined as the proportion of the population with large household expenditures on health as a share of total household expenditure or income). Despite the institutional commitment, there is an inordinate reliance on out-of-pocket-expenditure (OOPE) to finance healthcare due to the severely underfunded health system. For India, specifically, the public health expenditure as a share of GDP (1.25%) is the lowest in the world. Furthermore, the estimates from the National Health Accounts of India divulged that abysmally low coverage of government-sponsored pre-payment schemes coupled with the dearth of private health insurance has impelled households to have excessive reliance on out-of-pocket payments (58.7% of total health expenditure) for healthcare ( 2 ).

Healthcare expenditures or costs are incurred whenever a person accesses the healthcare system and utilizes the healthcare services. Health expenditures could be broadly defined as any expense that is spent on healthcare and related activities, including paying premiums for private or public health insurance coverage ( 3 ). A multitude of cost components encompasses healthcare payments on hospitalization, such as direct medical costs related to user fees, made at the time of health service use, incorporating charges ranging from registration, consultation, drugs, diagnostics, bed charges, etc. A legion of studies examining the impact of user fees on healthcare-seeking behavior in LMICs have conceded that the higher user fee/increase in prices can lead to decreased healthcare utilization and vice-versa ( 4 – 6 ). Literature in the Indian context underscores the impact of user charges and direct medical costs, specifically on drugs and diagnostics ( 7 , 8 ). In addition to the direct cost, indirect costs, such as expenses on food, lodging, and transportation, also account for a large proportion of OOPE, as evinced in the literature from LMICs and India ( 9 – 13 ). Furthermore, other invisible costs that were not incurred because of medical management of disease but rather of other incurred losses, such as lost wages, lost productivity, and costs resulting from the need for home care and child care otherwise not incurred, also pose a formidable barrier to access.

The unprecedented level of financial burden posed by healthcare expenditures has two-pronged implications. First, at the macroeconomic level, the burden posed by forgone care due to affordability barriers has a deleterious impact on the economic growth of the region due to loss in productivity. Second, out-of-pocket health payments precipitate an adverse shock on the financial stability of households incurring such expenditure, subsequently rendering the households vulnerable to catastrophic health expenditure and impoverishment due to income shocks perpetuated via health shocks, which can further potentially culminate into a trans-generational cycle of poverty, bearing long-term consequences. Health shock is the most common idiosyncratic income shock and one of the most pertinent reasons for the descent of households into poverty in LMICs ( 14 ).

The out-of-pocket payments for healthcare are usually the most inequitable type of finance due to its tendency to hit the poor the hardest by being a barrier to healthcare/by denying individuals’ financial protection from catastrophic illness ( 15 ). Studies from India have established the Inverse Care Law, i.e., individuals with the greatest need for healthcare have the greatest difficulty in accessing healthcare services ( 16 – 18 ). There is strong evidence that financial access to healthcare is very low among those residing in rural areas, uneducated, lowest wealth quintile, and otherwise marginalized sections of society ( 19 ). In a resource-poor setting, there are substantial heterogeneities in healthcare measures and capacity to pay thereof; as a corollary, pervasive income-based inequalities in the economic burden of care on the households are pronounced in these settings as well. A systematic review of LMICs has evinced that across all the LMICs, the risk of incurring CHE is six times more concentrated among the poor ( 20 ). Furthermore, evidence on hospitalization from countries such as Argentina, China, India, and Tanzania also revealed the disproportionate impact of CHE on the poor ( 21 ). Although there is some literature on the impact of socio-economic inequalities on the incidence of catastrophic payments in the Indian context ( 22 – 24 ), the evidence is rather exiguous and does not commensurate with the policy implications.

In India, the National Health Policy 2017 ( 25 ) directed that budgetary allocations would ensure horizontal equity by targeting specific population subgroups, geographical areas, healthcare services, and gender-related issues. Horizontal equity entails equal treatment for equal needs, irrespective of other socio-economic characteristics such as income, education, place of residence, and social group. Meanwhile, vertical equity connotes unequal treatment for unequal needs. However, the measurement of horizontal inequities is quite complex vis-a-vis vertical inequality, as need is a rather elusive concept both in terms of the choice of measurable indicators and also normative ethical considerations ( 26 ). However, the degree to which health inequality is considered inequitable is estimated via the need-adjustment of inequality. Literature commonly suggests that people with similar health statuses have the same needs and persons with dissimilar health statuses have different needs ( 27 ). The need-based variables are not amenable to the policy intervention and, thus, considered as fair or legitimate variables, whereas non-need variables are due to systemic inequalities and are amenable to policy intervention, thus, considered as unfair or illegitimate. Therefore, standardizing the inequality in health outcomes by need results in systematic disparities and captures the degree to which the inequality is inequitable.

The systemic inequalities along the socio-economic gradient with respect to the burden of healthcare payments continue to pose an unprecedented challenge in India despite the launch of various initiatives to provide financial risk protection to the poor and vulnerable. Previous studies have revealed that the incidence of CHE on hospitalization care has increased in the last few decades in India ( 24 ). However, the evidence of the impact of these initiatives in reducing the catastrophic burden among poor households remains elusive. Thus, it becomes imperative to explore the dimension of equity w.r.t. incidence of the catastrophic burden of out-of-pocket payments to correct existing interventions and promulgate inclusive policies.

However, there is a dearth of literature to study the need-adjusted inequities in the incidence of CHE for hospitalization care, and, further, to the best of our knowledge, no study has been conducted to decompose the effect of the legitimate and illegitimate factors causing the inequalities in the CHE. At the same time, it is pertinent to decompose and identify the need and non-need factors that affect the health and financial protection in the household to enable the targeted policy response. Thus, this study was conducted to estimate the degree of inequalities and need-adjusted inequities in the incidence of CHE for hospitalization care using a modified Erreygers concentration index. Furthermore, wider socio-economic-contextual determinates influencing the CHE on hospitalization care were unraveled succinctly within a conceptual framework. Additionally, the study also attempted to measure the relative contributions of need and non-need factors driving the inequality in the CHE by conducting a robust regression-based decomposition of the inequalities to identify the key variables for the policy response.

The study employed national representative unit-level cross-sectional data from the 75th round of the National Sample Survey Organization (Household Social Consumption in India: Health) . The survey was conducted under the stewardship of the Ministry of Statistics and Program Implementation , Government of India, during the time period of July 2017–June 2018. The survey schedule collects information pertaining to the demographic - socio-economic characteristics , morbidity status , utilization of healthcare services, and healthcare expenditure across ambulatory, inpatient, delivery, and immunization care for households and individuals. A two-stage stratified random sampling design was adopted in the survey with census villages and urban blocks as the First Stage Units for rural and urban areas, respectively, and households as the Second Stage Units. The overall sample size consisted of 1,13,823 households and 5,57,887 individuals (including the death cases). The analysis, however, circumscribed 66,237 individuals who were hospitalized in the last 365 days of the survey (without childbirth episodes). For this study, the information encompassing both medical expenses such as doctor’s/surgeon’s fee, medicines, diagnostic tests, bed charges, and consumables, viz. blood, oxygen, etc., and non-medical expenses such as expenses incurred on transportation, food, and lodging on account of treatment was employed in the study. Detailed information on the survey design can be found in the official report released by the National Sample Survey Organization ( 28 ).

2.2 Measures

The following measures were assessed in the study: (a) Extent of CHE on hospitalization cases in India; (b) Wealth-related inequities in the incidence of CHE on hospitalization; (c) Socio-economic-demographic factors impacting the CHE on hospitalization cases; and (d) Relative contribution of the factors in driving the wealth-based inequality in the CHE for hospitalization cases.

2.2.1 Outcome measure

The survey encompasses information on the expenses incurred in hospital treatment (medical and non-medical). The medical component subsumed data on the expenses toward the doctor’s/surgeon’s fee, medicines, diagnostics, bed charges, physiotherapy, personal medical appliances, and other consumables such as oxygen and blood. However, the non-medical component incorporated the expenses incurred on other ancillary payments, such as transportation, lodging, and food for the patient and caretaker, on account of the treatment. Given the information, the out-of-pocket expenditure (OOPE) is then defined as the direct payments made by the patients at the time of treatment, net of any reimbursements by the insurance provider. The CHE can be defined via two approaches, i.e., (a) capacity-to-pay approach and (b) budget-share approach. Under the capacity-to-pay approach, the OOPE on healthcare is considered catastrophic if a household’s financial contributions to the healthcare treatment exceed the 40% of income remaining after the subsistence needs have been met ( 29 , 30 ). Meanwhile, under the Budget-share approach, the OOPE is catastrophic if a household’s financial contribution to the treatment equals or exceeds 10% of the household’s total expenditure ( 31 , 32 ). In this study, the CHE was computed using the budget-share approach, where a 10% threshold of total household expenditure was considered. The outcome variable of interest in the study was binary in nature, indicating whether a household faced CHE on inpatient treatment.

2.2.2 Covariates

A gamut of household and individual level variables, drawn from Andersen’s behavioral health model ( 33 ), were incorporated into the study. The covariates were cogitated into legitimate/need and illegitimate/non-need variables to unravel the horizontal inequities underlying the CHE. The need for healthcare is considered an elusive concept, and the choice of variables is embedded in the normative categorization, which requires a potentially contestable value judgment ( 27 ). In general, the need sources of variation in health are ethically acceptable, whereas the non-need sources are ethically unjust or unfair ( 34 ). The variables underscoring the differential need for healthcare expenditure, viz. demographic characteristics, health status, and severity of ailments, such as age composition of household members, number of chronic members, hospitalization cases in households, and duration of stay in the hospital, were considered as the need-based variables in the study.

A myriad of factors impacted the choice of non-need variables, such as previous literature ( 35 – 37 ), relevance to explaining the inequality within the available dataset, and availability of periodic and routine monitoring of the indicators. A broad spectrum of household-level variables across the demographic characteristics such as age and gender of the household head, household size, and marital status of the household members; socio-economic characteristics, such as education, social group, religion, principal occupation of the household, monthly household consumption expenditure, and housing conditions (comprehensive indicator coalescing information on the drinking water source, cooking source, drainage type, and garbage disposal) ; enabling characteristics, such as insurance coverage and type of facility where care is sought; and contextual var iables such as the level of epidemiological transition level of the residential region and the geographical location (urban/rural) were chosen as the non-need variables. The monthly household consumption expenditure was adjusted to account for the economies of scale in household consumption stemming from the household size and demographic composition due to underlying differences in need among the household members using the Oxford equivalence scale ( 38 ). Furthermore, the monthly consumption household expenditure was converted to the annual expenditure to make it uniform with the expenses incurred on hospitalization with a recall period of 365 days.

2.3 Statistical analysis

2.3.1 incidence of catastrophic health expenditure.

The incidence of catastrophic health expenditure was computed via a budget-share approach and elucidated as the share of out-of-pocket health expenditure and out of the total household expenditure:

Where, O O P E i is the out-of-pocket expenditure of household i , T H E i is the household’s total consumption expenditure of household i , and S i is the share of the total healthcare expenditure out of the total consumption expenditure of household i . Consider Z i is the threshold beyond which the household i incurs catastrophic expenditure if S i > 10 % , which can be represented as:

2.3.2 Concentration curve and index

The concentration curve was used to glean the inequities in the CHE on hospitalization care. Cumulative proportions of the catastrophic health payment (vertical axis) were plotted against the cumulative proportion of the households with hospitalization cases (horizontal axis), ranked by the equivalized household consumption expenditure. The concentration index, denoted by C, is estimated as twice the area between the concentration curve and diagonal, which is represented as:

where, C H E i is the variable of interest for the household; μ is the mean of C H E i ; and R i is the i t h ranked household in the socio-economic distribution from most disadvantaged (i.e., poorest) to the least disadvantaged (i.e., richest). The value of C I ranges between −1 and + 1, where a positive value indicates the distribution concentrated among the rich and a negative value represents a distribution concentrated among the poor.

2.3.3 Choice of index

The outcome variable chosen in our study is binary, which is not consonant with the standard concentration index that measures relative inequality and does not allow for the differences between the individuals to be compared. When the standard concentration index is applied to the binary variable, characterized by ordinal and bounded nature, erroneous estimates are produced due to the following reasons: (a) An increase in the binary measure is mirrored by the decrease in the measure; (b) An equi-proportionate increase in the binary measure does not translate to the equi-proportionate decrease in the measure; and (c) Bounds act as constraints to (proportionally) equal transformations of the binary measure. The standard concentration index violates the mirror condition and cardinal invariance property. Additionally, a scale-invariant and rank-dependent index, such as the standard concentration index, fails to account for mirror conditions while accounting for the relative differences simultaneously ( 39 , 40 ). These conditions, however, can be satisfied by the generalized version of the modified concentration index proposed by Wagstaff ( 41 ) or Erreygers corrected concentration index ( 39 ). The generalized concentration index departs from the Erreygers index based on value judgments related to the desirability of level independence ( 42 ). This study employed the Erreygers corrected concentration index to compute the wealth-related inequalities in incurring the CHE by the households. Erreygers corrected concentration index is an absolute rather than a relative measure and is only a rank-dependent measure, which is suitable for our binary outcome measure as it satisfies all the desirable properties for rank-dependent indices, i.e., mirror, transfer, cardinal invariance, and level independence. Furthermore, Erreygers has developed the notions of ‘quasi-absoluteness’ and ‘quasi-relativity’ best suited for the bounded variables as they mitigate the infeasibility of equi-proportional change or equal additions in binary constructs. The index is represented as:

Where C I denotes the standard concentration index as represented in Equation 2 , μ is the mean of CHE in the population, and a n , b n are the upper and lower bounds of the outcome variables.

2.3.4 Need standardization

The differential role of need-based factors such as health conditions and demographics in driving health inequality is not considered in the unstandardized distribution of the outcome measures. However, the differential role of such factors can be observed by segregating the inequality into legitimate and illegitimate health inequality. As a result, the need-standardization was conducted to adjust for the legitimate factors impacting health inequality and to facilitate the comparison across groups. The need-standardization can be done via direct-standardization and indirect-standardization methods. The indirect standardization, reflecting the actual distribution of healthcare outcomes and the distribution that would be expected given the distribution of need, was adopted in this study. The indirect standardization exhibits greater accuracy when dealing with unit-level data. However, the evidence on standardization of equity procedures suggests that inequity measures do not digress significantly with the use of linear methods vis-a-vis non-linear methods ( 43 , 44 ). Thus, a linear regression model for standardization was employed first, which is depicted as follows:

Where, y i is the CHE for the household i ; x j i and Z k i are the vectors of need and non-need factors driving the inequality; α , β j , and θ k are the parameters, while the ε i is the error term. Additionally, the predicted values of the outcome measure ( y ^ i x ) was obtained using the OLS parameter estimates ( a ^ , β ^ j , and θ ^ k ), individual values of the need-variables ( x j i ), and sampled means of the controlled non-need variables ( z ¯ j i ). In the next step, the estimates for indirect standardization of outcome measure ( y ^ i IS ) was obtained by subtracting the predicted values from actual values and adding the overall sample mean ( y ¯ ). The subsequent procedure is depicted as follows:

2.3.5 Decomposition of concentration index

The Erreygers concentration index was decomposed to estimate the relative contribution of covariates to explain the inequality in the outcome measure and other unexplained residual variations. A linear approximation of the model, which is based on the partial effects of each covariate evaluated at the sample means, was employed to perform the decomposition. The linear decomposition of inequalities in outcome measure is illustrated as:

Where, x ¯ j and z ¯ j denotes the means of need and non-need factors, respectively, whereas, C I j and C I k are representative of the respective concentration indices. G C I ε is the generalized concentration index for ε i (residual term), which corresponds to the inequality in the outcome measure that cannot be explained by the systematic variation in other variables. The representation is depicted below:

The modified form of decomposition of Erreyger’s index is thus, given as ( 44 ):

The horizontal inequity (HI) in the CHE was thus estimated by subtracting the absolute contributions made by the need-based factors from the unadjusted value of the Erreygers index. A positive value of HI indicates the inequities concentrated among the better-off, whereas a negative value indicates the inequities concentrated among the worse-off.

2.3.6 Determinants of catastrophic health expenditure

The determinants of CHE were gleaned using a gamut of variables that were embedded within Andersen’s behavioral health model ( 45 ). As per the Andersen framework, the choice variables were prorated into (a) Predisposing components reflecting the demographic and socio-structural characteristics of the household; (b) Enabling components subsuming standard of living and insurance coverage for the households; (c) Need components underscoring the severity of disease, frequency, and duration of hospitalization episodes; and (d) Contextual components comprising the regional aspects such as spatial location and burden of the NCD’s in the region.

A multivariate logistic regression model was employed to unravel the determinants of CHE, represented as:

where, the S i , which is the share of out-of-pocket health expenditure ( O O P E i ) out of the total health expenditure ( T H E i ), is dichotomous, i.e., S i assumes the value of 1 if the out-of-pocket health expenditure ( O O P E i ) exceeds the 10% threshold of the total health expenditure T H E i and 0 otherwise. The notation X 1 , X 2 ….. X n represents the socio-economic-demographic-contextual variables driving the CHE. The analysis was conducted using the STATA 15.0 statistical package. The estimates were weighted to account for the complex multistage sample design and confidence intervals for the horizontal inequity index were computed using Bootstrap with 1,000 replications.

The unstandardized and need-standardized distribution of CHE on Hospitalization care in India is illustrated in Figure 1 . Overall, 27% of the ailing treated as inpatients (except for childbirth) incurred CHE during 2017–2018 in India. The incidence of CHE, however, exhibited an inverse relationship with the relative ranking of the expenditure quintile groups. An extensive gradient in the levels of CHE was found between the lowest and highest quintile groups. The incidence of CHE for the population hospitalized in the poorest quintile (41%) was more than twice as compared to the richest quintile (19%). Furthermore, the estimates of the need-standardized CHE were found to be higher than the unstandardized CHE estimates for poor- and middle-income groups (need-standardized CHE greater than unstandardized by 4, 2, and 1% points for poorest, poor, and middle quintile groups); whereas, standardized CHE levels were less than the unstandardized estimates for the wealthier groups (need-standardized CHE lesser than unstandardized estimates by 1 and 7% for rich and richest quintile groups, respectively).

Figure 1 . Distribution of actual and need-standardized levels of CHE on inpatient care in India.

3.1 Inequality and inequities in the catastrophic health expenditure on hospitalization care

The concentration curve eliciting the inequalities and inequities in the CHE on hospitalization care is plotted in Figure 2 . The concentration curve (unstandardized) was found to be above (dominates) the line of equality, indicating that the burden of CHE on inpatient care was concentrated among the poor. Furthermore, the standardized curve (adjusted for differential needs) dominated the unstandardized curve, which denoted that for equal need, the concentration of inequality among the poor was more pronounced vis-a-vis the inequality in CHE, which is not adjusted by the need-based confounding factors. The dominance testing to test the difference between estimated concentration curve ordinates and diagonal via the Multiple Comparison Approach and Intersection Union Principle rejected the null of no wealth-related inequality and established that concentration curves significantly dominated the line of equality. Correspondingly, the estimated value of the Erreyger’s corrected concentration index ( Table 1 ) was negative and significant (EI: -0.191; p < 0.05), underscoring the disproportionate incidence of CHE among the poor in India. Moreover, the estimates of the need-adjusted concentration index (EI: -0.258; p < 0.01) corroborated the wider inequities when accounting for the differential needs.

Figure 2 . Concentration curves depicting the inequalities in CHE on inpatient care in India.

Table 1 . Concentration indices depicting the inequality in CHE for hospitalization care.

3.2 Inter-state differentials in the inequities in CHE on hospitalization

The extent of the need-adjusted wealth inequities in incurring the CHE on inpatient care is exhibited in Figure 3 . The measure of inequity was perceptibly concentrated among the poor in most of the Indian states. However, substantial heterogeneities were found in the degree of the inequities among the states. Wealth-related inequities (concentrated among the poor) were found to be high in the states such as Goa (EI: −0.18) and Jharkhand (EI: −0.13). A few states, such as Uttar Pradesh and Maharashtra, with just approximately one-fourth of the total health spending financed by the government, also exhibited significantly high inequities concentrated among the poor. Conversely, no inequities (EI: 0.00) were estimated for the states of Bihar, Chhattisgarh, and Kerala. Furthermore, the states of Assam and Jammu and Kashmir with the highest level of government spending as a proportion of total health spending (55.2 and 51.3% for Assam and Jammu and Kashmir, respectively) evinced relatively less wealth-related inequities. However, the need-adjusted inequalities were concentrated among the rich in the North-Eastern states of Sikkim (EI: 0.07) and Manipur (0.03) in India.

Figure 3 . Need-adjusted inequality indices for CHE on hospitalization in Indian states.

3.3 Descriptive statistics of the variables

The descriptive statistics of the households with hospitalization episodes in the survey period are presented in Table 2 . Most households were headed by adults aged 25–75 years (95.6%) and were men (88.6%). The demographic structure consisted of small (47.5%) and middle (50.2%)-sized households, and more than half of the households (53.5%) lived with children and older adult dependents. Furthermore, one-fourth of the households had a vulnerable widowed population. Approximately 24% of households were headed by household heads who were not literate, and a majority of the households were not employed in activities with regular sources of income. Most of the targeted surveyed households prescribed the religion of Hinduism (75.8%), followed by Islam (13.6%). Socially, a vast proportion of households belonged to the marginal communities, viz. scheduled caste/scheduled tribes (27.9%) and other backward castes (40.2%). Additionally, the housing conditions for most of the households were good (82.3%). However, the access to healthcare services for the household members was considerably low as more than three-fourths of the households were bereft of insurance coverage. Government-sponsored insurance coverage (14%) constituted the highest financial risk protection cover, followed by employer-sponsored coverage (4.4%). Health-seeking behavior divulged that a colossal 50.8% of households sought care from only private facilities, whereas less than half of the households (43.1%) sought care from only public facilities (43.1%). The need for healthcare was more for certain households, as approximately one-fourth of households had at least two or more members suffering from chronic ailments and had more than one hospitalization episode. The majority of the households (63.2%) accounted for a total duration of ≤7 days stay in the hospital, while 32% of households reported a hospital stay of between 7 and 14 days. Spatially, approximately 50.9% of households were residing in the states/UT’s with a higher-middle and high epidemiological transition level. Furthermore, 55.7% of households were in rural areas, while 44.3% of sampled households were residing in urban areas.

Table 2 . Descriptive statistics of the variables.

3.4 Factors impacting the CHE on hospitalization care among households

The wider socio-economic-contextual predictors of the CHE on hospitalization care among households in India are presented in Table 3 . The estimates revealed that among the predisposing demographic factors, the age mix in the household significantly impacted the CHE. Households that were composed of only older adult members and older adult, but no children, were 9% (significant at 1% level) and 4.7% (significant at 1% level), respectively, more likely to incur the CHE vis-a-vis households with a mixed composition of both children and older adult. The structural factor of household size strongly influenced the outcome, as smaller households with less than 5 members and 5–10 members had 16.3 and 10.7%, respectively, more probability than larger households to get impacted by the CHE on inpatient care. Additionally, those households that are principally unemployed/engaged in unpaid work were less likely to be subjected to the CHE vis-a-vis households that were self-employed or receiving pensions post-retirement. Among the social characteristics, households that are ascribed to the other backward castes were more likely to suffer the catastrophic impacts of health payments compared to the households that are classified as scheduled caste/scheduled tribes. Furthermore, practicing Hinduism or other religions, such as Sikhism and Judaism, was positively associated with the CHE incidence as Hindus and other religious groups were 4 and 7.3% more likely vis-a-vis households practicing Islam to face the CHE. The results also underscored the significance of enabling factors in driving the CHE. The evidence indicated an inverse relationship of the CHE with the wealth of households, as richer households were significantly less likely to incur the CHE than their poorer counterparts. The poor, middle, rich, and richest had 11.2, 18.7, 24.1, and 30.5%, respectively, less probability of facing CHE than the poorest household. Analogously, households with government-sponsored insurance cover (6.6%), employer-sponsored cover (10.9%), and private insurance/other covers (12.9%) were less likely to incur CHE vis-a-vis households that are not covered under any financial risk protection scheme. Conversely, households that sought inpatient treatment from private facilities had significantly more likelihood of spending a catastrophic amount on treatment (24.7% for households who sought treatment in a mix of public and private facilities and 32.7% for households who sought treatment in private facilities alone) than those households which sought treatment in just the public hospitals. With respect to the need-based factors, longer duration of hospital stay was associated with more CHE; the probability of incurring CHE was lesser for shorter admission time of fewer than 2 weeks (18.9%), 4–7 days (35.9%), and 3 or fewer days (50.7%) in comparison with the households with longer inpatient days. Finally, the contextual factor of geographical (spatial) location impacted the CHE, as households residing in the regions at higher levels of epidemiological transition level were less likely (7, 4.8, and 6.8% lesser probability for lower-middle, higher-middle, and high epidemiological transition level) to face the CHE on hospital stay as compared to the households residing in the regions having low epidemiolocal level.

Table 3 . Determinants of the CHE on hospitalization care among households in India.

3.5 Decomposition of the inequalities in the CHE on hospitalization care in India

The results ascertaining the contribution of various determinants in driving the wealth-related inequality in CHE on hospitalization care in India is encapsulated in Table 4 , which presents the estimates of coefficients, Erreyger’s concentration indices, absolute contributions (computing the product of elasticity and regressor’s concentration index), and relative contributions (denoting the percentage of inequality in CHE attributable to the inequality in the contributing factor). A positive (negative) value of the absolute contribution of a correlate demonstrates that if the inequality in the CHE was determined by that correlate alone, then it would be concentrated toward the worse-off (better off). The relative contribution of a correlate is computed by dividing the absolute contribution of correlates by total inequality in the outcome variable and multiplying it by 100. The aggregate relative contributions of covariates in driving the inequality are also illustrated in Figure 4 . Overall, the relative contribution of need-based variables was exhibited to be negative, connoting that if the CHE were determined by need alone, it would be more concentrated among the poor. Aggregately, the need factors accounted for 35.3% of the unstandardized concentration index, and most of this contribution was attributed to the duration of stay (30.6% of the unstandardized concentration index) in the hospital. However, the inequality push toward the poor was offset to a degree by the effect of the non-need/illegitimate factors. The majority of the inequality in the CHE was driven by illegitimate/non-need factors, with most of the contributions from the enabling factors such as inequality in the wealth of households (expenditure quintiles) and health utilization pattern (facility mix for hospitalization) in conjunction with socio-structural variables such as the size of the household. Additionally, the decomposition results enable the estimation of horizontal inequity, which is obtained by subtracting the absolute need contributions (0.068) from the unstandardized index (−0.19), thus yielding an index value of −0.26.

Table 4 . Regression coefficients (B), absolute contribution and relative contribution of determinants to income-related inequality in catastrophic health expenditure on hospitalization in India.

Figure 4 . Decomposition analysis of income-related inequalities in CHE on hospitalization.

4 Discussion and conclusion

Our study revealed significant wealth-related inequalities in the CHE for hospitalization care in India, with a pervasive gap between the poorest and richest income quintiles. The CHE was concentrated more among the poor, with the incidence of CHE being more than twice for the poorest quintile vis-a-vis the richest quintile group. The findings were corroborated by the negative value of the Erreygers concentration index, denoting the inequalities that are disadvantageous to the poor. Furthermore, need-adjusted inequalities also underscored the systemic inequalities (caused by the factors amenable to the policy change) to be concentrated among the poor. Globally, the evidence on the relationship between CHE and socio-economic status has been mixed, and few findings suggest that the better-off experience more CHE in low- and middle-income settings (LMIC) due to the higher propensity of the rich to consume more health services ( 46 ). However, our findings were consonant with the studies conducted in other LMIC settings such as Iran ( 47 ), China ( 48 ), Malawi ( 49 ), Columbia ( 50 ), and Sub-Saharan Africa ( 46 ), where inequality gradients indicated the poor getting afflicted by the CHE disproportionately. The higher incidence of CHE among the poor can be understood by the fact that for households with low income, even a small proportion of healthcare costs can be catastrophic.

The relatively higher incidence of CHE among the poor is pertinent from a policy perspective as it also connotes the intrinsic disparities in healthcare access and finance. India has launched various programs targeted toward the poor to move along the trajectory of Universal Health Coverage (UHC). To achieve the goal of equitable financial risk protection for the marginalized, India launched flagship initiatives such as the National Rural Health Mission (NRHM) in 2005, providing free cost care to the poor and Rashtriya Swasthya Bima Yojana (RSBY) in 2008, covering the poor population with cashless insurance on hospitalization. However, the relatively higher incidence of CHE among the poor alludes to the inefficacy of these programs in providing financial risk protection to the poor. Furthermore, the empirical evidence on the impact of schemes such as RSBY has concurred with its ineffectiveness in reducing the inpatient out-of-pocket spending and catastrophic inpatient spending ( 51 , 52 ). However, India recently revamped and bolstered these schemes further for expanded coverage by launching the Ayushman Bharat (AB) Program (National Health Protection Mission) for integrated healthcare. The scheme has two components: (a) AB-Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), which provides cashless cover up to INR 5 lakh per family for hospitalization in secondary and tertiary care to over 10 crore poor and vulnerable families; and (b) AB-Health and Wellness Centers (AB-HWCs) providing comprehensive primary and community-based services free of cost to the population. Furthermore, India has launched other initiatives such as free drugs and diagnostics services and financial assistance to patients living below the poverty line for life-threatening diseases under schemes such as Rashtriya Arogya Nidhi (RAN), Health Minister’s Cancer Patient Fund (HMCPF), and Health Minister’s Discretionary Grant (HMDG). Furthermore, affordable medicines and reliable implants for treatment (AMRIT) deendayal outlets have been opened to make available drugs and implants for cardiovascular diseases (CVDs), cancer, and diabetes at discounted prices to patients ( 53 ). Although a legion of health initiatives providing free healthcare to different marginalized sections of society have been launched recently, the impact evaluation of these interventions in reducing the burden of OOP on hospitalization among the poor in India needs to be undertaken.

Our findings indicated that members of more than half of the poor households were hospitalized in private facilities with a disproportionately higher incidence of CHE (38.5% in private facilities vis-a-vis 11.5% in public facilities). A myriad of reasons for the preference for private provider(s) in India has been expounded in literature, such as poor readiness and quality of care, higher waiting times, inconvenient facility timings, long distances, absence of healthcare personnel, and lack of acceptability and trust in public providers ( 54 – 57 ). Hence, it is recommended to strengthen the public healthcare system to encompass NCD care (with a disproportionately higher incidence of CHE) ( 58 ) and improve the quality of care in terms of infrastructure, equipment, drugs, and diagnostics. A legion of guidelines and standards to ensure the quality of care has been enforced in India, such as Indian Public Health Standards (IPHS), Mera Aspataal (My hospital), and National Quality Assurance Standards (NQAS). However, the non-compliance of quality protocols and standards has hampered the readiness of public health facilities. Thus, the objective periodic monitoring and evaluation of the quality parameters along the continuum of care is suggested to ensure readiness. Concomitantly, surveillance measures such as record keeping, frequent monitoring of employee absence behavior, detection of absence via biometric attendance, and management-oriented punitive action measures for dereliction of duties can be introduced to minimize absenteeism. Simultaneously, to mitigate the low acceptability and poor confidence in public provider, knowledge dissemination, advocacy, and public engagement activities should be promoted at an individual, household, and community and regional level as a confidence-building measure.

Our findings found a legion of factors influencing CHE on hospitalization care. The role of demographic factors was accentuated in the study, and it was found that households comprising only older adult members incur significantly high CHE on hospitalization, which is in tandem with other studies conducted in India ( 59 ). Analogously, our estimates revealed that larger size households experience more CHE, which is conflated by other research conducted in LMICs ( 60 , 61 ). Additionally, other predisposing socio-structural factors, such as affiliation with the marginalized social group and practicing the religion of Hinduism, are associated with higher CHE, which is consonant with the other studies conducted in India ( 62 – 64 ). Although equity has been a primary goal of the flagship programs launched by the Government of India, the related policy discourse has been focused on the praxis of wealth-related inequalities and has precluded other social disparities, such as religion and caste, as a potential axis of healthcare marginalization ( 65 ). The multivariate regression estimates also underscored the role of enabling factors such as the absence of insurance coverage and treatment-seeking in private facilities to increase the CHE significantly. The role of these enabling factors, such as the type of health facility and insurance coverage, in influencing the CHE has also been accentuated in many other studies from similar settings ( 66 , 67 ).

In the LMIC context, the policy discourse has given impetus to the establishment/extension of national/social health insurance in which service providers are paid from designated government funds, which are partly funded through taxes. India via AB-PMJAY provides such insurance coverage for hospitalization to the poor and vulnerable; however, evidence from rural India suggests that around one-fourth of the eligible participants are still unaware of the AB-PMJAY scheme; moreover, the level of utilization of the scheme has been found to be abysmally low at 1.3% ( 68 ). The low level of utilization can be explained via complex enrollment or reimbursement process, which acts as a significant barrier to take up. The findings on PMJAY in India also suggest that this scheme shifted the use of health facilities from public providers to privately empaneled hospitals where the cost of care is higher ( 69 ). Thus, a gamut of strategies can be employed to increase the penetration and uptake of Public Funded Health Insurance (PHFI) schemes in India, such as an increase in the awareness of benefits and community engagement via appropriate training for competencies of the community health workers, such as Accredited social health activists (ASHA) and Anganwadi workers (AWW); easing the process of enrollment and reimbursement and streamlining other hospital-based processes for effective implementation of the scheme ( 70 ) and establishing a robust referral linkage between the primary healthcare facilities with secondary and tertiary hospitals with the help of digital interventions and infrastructure. However, in regions where the institutional capacity to organize mandatory nationwide risk-pooling is weak, community-based health insurance schemes can be effective in protecting poor households from unpredictably high medical expenses ( 31 ).

The findings also demonstrated the role of contextual factors such as the region in influencing the CHE as the households belonging to the states with higher levels of the epidemiological transition level (defined based on the ratio of disability-adjusted life years and computed as the sum of years of potential life lost due to the premature mortality and the years of productive life lost due to disability from communicable disease to those from non-communicable and injuries combined) incurred lesser CHE as compared to their counterparts residing in the states at a lower level of ETL. These inter-region heterogeneities can be explained by the inverse relationship between the epidemiological transition ratio and socio-economic development of the states ( 71 ). A higher burden of CHE on the states with a lower level of epidemiological transition is a pertinent finding from the policy perspective as these states are associated with the lower per capita expenditure on healthcare, thus lacking financial risk protection vis-a-vis other states. Thus, there is a need to increase public spending on healthcare to reach the targeted level of 4% of GDP by 2025. However, realistically, the state governments can set a target to allocate at least 2.5% of the state’s gross domestic product (SGDP) to healthcare, which is the recommended level by the World Health Organization (WHO). It is further suggested that the government explore new and innovative financing mechanisms to generate the fiscal space, such as the public–private partnership to fund the sector; simultaneously, other fiscal space measures, such as the collection of health-specific tax, goods, and services tax reform, higher excise duty on tobacco products, tax administration reform and direct beneficiary transfer of health services could be employed as the alternative revenue mobilization channels for fiscal space in health ( 72 ).

The decomposition analysis revealed that the contribution of non-need/illegitimate factors in driving the inequality was relatively high vis-à-vis need/legitimate factors, as most of the inequality in CHE was driven by the non-need factors amenable to the policy change. Most of the unfair inequalities arose from socio-structural factors such as the size of the household and enabling factors such as income (expenditure) and type of facility (public or private) utilized. The relative contribution of these determinants in influencing inequalities in CHE is found in other LMICs. A study on the decomposition of inequalities in CHE in Iran ( 47 ) demonstrated that most of the illegitimate inequalities emanated from household economic status (64%), followed by household size (40%). Other studies in China have also accounted for household size as the largest contributor to CHE inequality ( 73 , 74 ). Furthermore, evidence from Sierra Leonne suggested that the distributional effect of the type of facility significantly impacted the inequalities in the CHE ( 75 ). Thus, from the policy perspective, it is imperative to invest more in public health facilities, providing significant financial risk protection to the poor. From the Indian perspective, the burden of CHE was found to be disproportionately higher for the poor and middle-population groups as well. Thus, it is suggested that the state and central governments expand the PFHI coverage to the missing middle population as well.

The study has a few caveats due to the nature of the dataset and the methodological approach. First , the same weights are assigned to the catastrophic payments incurred by poor and non-poor households and, thus, ignore the differentials in the opportunity cost in the health spending between rich and poor, thereby rendering the measure non-normative, which does not allow for distributional sensitivity. Second , health expenditures are not adjusted for coping mechanisms such as distressed financing or adjustment in the consumption pattern to pay for the health expenditure, thus understating CHE. Third , the data on expenditure used in the survey is self-reported and is susceptible to recall and information bias. Fourth, in the multivariate regression, the information on outcome measures and covariates was collected concurrently due to the cross-sectional design; thus, associations rather than causal relationships are defined in the study. Fifth, the information on self-reported monthly household consumer expenditure is a one-shot open-ended with no parallel validation, and thus can lead to the underestimation of the household’s income.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

SS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. VV: Data curation, Formal analysis, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. PG: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – review & editing. MA: Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The research was funded by the internal funding support made available to the first and corresponding author Dr. Shyamkumar Sriram from Ohio University College of Health Sciences and Professions, Ohio University, USA.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

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74. Kavosi, Z, and Rashidian, A. Inequality in household catastrophic health care expenditure in a low-income society of Iran. Health Policy Plan . 27:613–23. doi: 10.1093/heapol/czs001

75. Edoka, I, Mcpake, B, Ensor, T, Amara, R, and Edem-Hotah, J. Changes in catastrophic health expenditure in post-conflict Sierra Leone: an Oaxaca-blinder decomposition analysis. Int J Equity Health . 1:661. doi: 10.1186/s12939-017-0661-4

Keywords: out-of-pocket healthcare expenditures, hospitalization care, catastrophic health expenditures, inequality, need-adjusted inequities, decomposition of inequality

Citation: Sriram S, Verma VR, Gollapalli PK and Albadrani M (2024) Decomposing the inequalities in the catastrophic health expenditures on the hospitalization in India: empirical evidence from national sample survey data. Front. Public Health . 12:1329447. doi: 10.3389/fpubh.2024.1329447

Received: 29 October 2023; Accepted: 18 March 2024; Published: 04 April 2024.

Reviewed by:

Copyright © 2024 Sriram, Verma, Gollapalli and Albadrani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shyamkumar Sriram, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Núria Mercadé-Besora 1 , 2 , 3 ,
Xintong Li 1 ,
Raivo Kolde 4 ,
Nhung TH Trinh 5 ,
Maria T Sanchez-Santos 1 ,
Wai Yi Man 1 ,
Elena Roel 3 ,
Carlen Reyes 3 ,
http://orcid.org/0000-0003-0388-3403 Antonella Delmestri 1 ,
Hedvig M E Nordeng 6 , 7 ,
http://orcid.org/0000-0002-4036-3856 Anneli Uusküla 8 ,
http://orcid.org/0000-0002-8274-0357 Talita Duarte-Salles 3 , 9 ,
Clara Prats 2 ,
http://orcid.org/0000-0002-3950-6346 Daniel Prieto-Alhambra 1 , 9 ,
http://orcid.org/0000-0002-0000-0110 Annika M Jödicke 1 ,
Martí Català 1
1 Pharmaco- and Device Epidemiology Group, Health Data Sciences, Botnar Research Centre, NDORMS , University of Oxford , Oxford , UK
2 Department of Physics , Universitat Politècnica de Catalunya , Barcelona , Spain
3 Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol) , IDIAP Jordi Gol , Barcelona , Catalunya , Spain
4 Institute of Computer Science , University of Tartu , Tartu , Estonia
5 Pharmacoepidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences , University of Oslo , Oslo , Norway
6 School of Pharmacy , University of Oslo , Oslo , Norway
7 Division of Mental Health , Norwegian Institute of Public Health , Oslo , Norway
8 Department of Family Medicine and Public Health , University of Tartu , Tartu , Estonia
9 Department of Medical Informatics, Erasmus University Medical Center , Erasmus University Rotterdam , Rotterdam , Zuid-Holland , Netherlands
Correspondence to Prof Daniel Prieto-Alhambra, Pharmaco- and Device Epidemiology Group, Health Data Sciences, Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; daniel.prietoalhambra{at}ndorms.ox.ac.uk

Objective To study the association between COVID-19 vaccination and the risk of post-COVID-19 cardiac and thromboembolic complications.

Methods We conducted a staggered cohort study based on national vaccination campaigns using electronic health records from the UK, Spain and Estonia. Vaccine rollout was grouped into four stages with predefined enrolment periods. Each stage included all individuals eligible for vaccination, with no previous SARS-CoV-2 infection or COVID-19 vaccine at the start date. Vaccination status was used as a time-varying exposure. Outcomes included heart failure (HF), venous thromboembolism (VTE) and arterial thrombosis/thromboembolism (ATE) recorded in four time windows after SARS-CoV-2 infection: 0–30, 31–90, 91–180 and 181–365 days. Propensity score overlap weighting and empirical calibration were used to minimise observed and unobserved confounding, respectively.

Fine-Gray models estimated subdistribution hazard ratios (sHR). Random effect meta-analyses were conducted across staggered cohorts and databases.

Results The study included 10.17 million vaccinated and 10.39 million unvaccinated people. Vaccination was associated with reduced risks of acute (30-day) and post-acute COVID-19 VTE, ATE and HF: for example, meta-analytic sHR of 0.22 (95% CI 0.17 to 0.29), 0.53 (0.44 to 0.63) and 0.45 (0.38 to 0.53), respectively, for 0–30 days after SARS-CoV-2 infection, while in the 91–180 days sHR were 0.53 (0.40 to 0.70), 0.72 (0.58 to 0.88) and 0.61 (0.51 to 0.73), respectively.

Conclusions COVID-19 vaccination reduced the risk of post-COVID-19 cardiac and thromboembolic outcomes. These effects were more pronounced for acute COVID-19 outcomes, consistent with known reductions in disease severity following breakthrough versus unvaccinated SARS-CoV-2 infection.

Epidemiology
PUBLIC HEALTH
Electronic Health Records

Data availability statement

Data may be obtained from a third party and are not publicly available. CPRD: CPRD data were obtained under the CPRD multi-study license held by the University of Oxford after Research Data Governance (RDG) approval. Direct data sharing is not allowed. SIDIAP: In accordance with current European and national law, the data used in this study is only available for the researchers participating in this study. Thus, we are not allowed to distribute or make publicly available the data to other parties. However, researchers from public institutions can request data from SIDIAP if they comply with certain requirements. Further information is available online ( https://www.sidiap.org/index.php/menu-solicitudesen/application-proccedure ) or by contacting SIDIAP ([email protected]). CORIVA: CORIVA data were obtained under the approval of Research Ethics Committee of the University of Tartu and the patient level data sharing is not allowed. All analyses in this study were conducted in a federated manner, where analytical code and aggregated (anonymised) results were shared, but no patient-level data was transferred across the collaborating institutions.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/heartjnl-2023-323483

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WHAT IS ALREADY KNOWN ON THIS TOPIC

COVID-19 vaccines proved to be highly effective in reducing the severity of acute SARS-CoV-2 infection.

While COVID-19 vaccines were associated with increased risk for cardiac and thromboembolic events, such as myocarditis and thrombosis, the risk of complications was substantially higher due to SARS-CoV-2 infection.

WHAT THIS STUDY ADDS

COVID-19 vaccination reduced the risk of heart failure, venous thromboembolism and arterial thrombosis/thromboembolism in the acute (30 days) and post-acute (31 to 365 days) phase following SARS-CoV-2 infection. This effect was stronger in the acute phase.

The overall additive effect of vaccination on the risk of post-vaccine and/or post-COVID thromboembolic and cardiac events needs further research.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

COVID-19 vaccines proved to be highly effective in reducing the risk of post-COVID cardiovascular and thromboembolic complications.

Introduction

COVID-19 vaccines were approved under emergency authorisation in December 2020 and showed high effectiveness against SARS-CoV-2 infection, COVID-19-related hospitalisation and death. 1 2 However, concerns were raised after spontaneous reports of unusual thromboembolic events following adenovirus-based COVID-19 vaccines, an association that was further assessed in observational studies. 3 4 More recently, mRNA-based vaccines were found to be associated with a risk of rare myocarditis events. 5 6

On the other hand, SARS-CoV-2 infection can trigger cardiac and thromboembolic complications. 7 8 Previous studies showed that, while slowly decreasing over time, the risk for serious complications remain high for up to a year after infection. 9 10 Although acute and post-acute cardiac and thromboembolic complications following COVID-19 are rare, they present a substantial burden to the affected patients, and the absolute number of cases globally could become substantial.

Recent studies suggest that COVID-19 vaccination could protect against cardiac and thromboembolic complications attributable to COVID-19. 11 12 However, most studies did not include long-term complications and were conducted among specific populations.

Evidence is still scarce as to whether the combined effects of COVID-19 vaccines protecting against SARS-CoV-2 infection and reducing post-COVID-19 cardiac and thromboembolic outcomes, outweigh any risks of these complications potentially associated with vaccination.

We therefore used large, representative data sources from three European countries to assess the overall effect of COVID-19 vaccines on the risk of acute and post-acute COVID-19 complications including venous thromboembolism (VTE), arterial thrombosis/thromboembolism (ATE) and other cardiac events. Additionally, we studied the comparative effects of ChAdOx1 versus BNT162b2 on the risk of these same outcomes.

Data sources

We used four routinely collected population-based healthcare datasets from three European countries: the UK, Spain and Estonia.

For the UK, we used data from two primary care databases—namely, Clinical Practice Research Datalink, CPRD Aurum 13 and CPRD Gold. 14 CPRD Aurum currently covers 13 million people from predominantly English practices, while CPRD Gold comprises 3.1 million active participants mostly from GP practices in Wales and Scotland. Spanish data were provided by the Information System for the Development of Research in Primary Care (SIDIAP), 15 which encompasses primary care records from 6 million active patients (around 75% of the population in the region of Catalonia) linked to hospital admissions data (Conjunt Mínim Bàsic de Dades d’Alta Hospitalària). Finally, the CORIVA dataset based on national health claims data from Estonia was used. It contains all COVID-19 cases from the first year of the pandemic and ~440 000 randomly selected controls. CORIVA was linked to the death registry and all COVID-19 testing from the national health information system.

Databases included sociodemographic information, diagnoses, measurements, prescriptions and secondary care referrals and were linked to vaccine registries, including records of all administered vaccines from all healthcare settings. Data availability for CPRD Gold ended in December 2021, CPRD Aurum in January 2022, SIDIAP in June 2022 and CORIVA in December 2022.

All databases were mapped to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) 16 to facilitate federated analytics.

Multinational network staggered cohort study: study design and participants

The study design has been published in detail elsewhere. 17 Briefly, we used a staggered cohort design considering vaccination as a time-varying exposure. Four staggered cohorts were designed with each cohort representing a country-specific vaccination rollout phase (eg, dates when people became eligible for vaccination, and eligibility criteria).

The source population comprised all adults registered in the respective database for at least 180 days at the start of the study (4 January 2021 for CPRD Gold and Aurum, 20 February 2021 for SIDIAP and 28 January 2021 for CORIVA). Subsequently, each staggered cohort corresponded to an enrolment period: all people eligible for vaccination during this time were included in the cohort and people with a history of SARS-CoV-2 infection or COVID-19 vaccination before the start of the enrolment period were excluded. Across countries, cohort 1 comprised older age groups, whereas cohort 2 comprised individuals at risk for severe COVID-19. Cohort 3 included people aged ≥40 and cohort 4 enrolled people aged ≥18.

In each cohort, people receiving a first vaccine dose during the enrolment period were allocated to the vaccinated group, with their index date being the date of vaccination. Individuals who did not receive a vaccine dose comprised the unvaccinated group and their index date was assigned within the enrolment period, based on the distribution of index dates in the vaccinated group. People with COVID-19 before the index date were excluded.

Follow-up started from the index date until the earliest of end of available data, death, change in exposure status (first vaccine dose for those unvaccinated) or outcome of interest.

COVID-19 vaccination

All vaccines approved within the study period from January 2021 to July 2021—namely, ChAdOx1 (Oxford/AstraZeneca), BNT162b2 (BioNTech/Pfizer]) Ad26.COV2.S (Janssen) and mRNA-1273 (Moderna), were included for this study.

Post-COVID-19 outcomes of interest

Outcomes of interest were defined as SARS-CoV-2 infection followed by a predefined thromboembolic or cardiac event of interest within a year after infection, and with no record of the same clinical event in the 6 months before COVID-19. Outcome date was set as the corresponding SARS-CoV-2 infection date.

COVID-19 was identified from either a positive SARS-CoV-2 test (polymerase chain reaction (PCR) or antigen), or a clinical COVID-19 diagnosis, with no record of COVID-19 in the previous 6 weeks. This wash-out period was imposed to exclude re-recordings of the same COVID-19 episode.

Post-COVID-19 outcome events were selected based on previous studies. 11–13 Events comprised ischaemic stroke (IS), haemorrhagic stroke (HS), transient ischaemic attack (TIA), ventricular arrhythmia/cardiac arrest (VACA), myocarditis/pericarditis (MP), myocardial infarction (MI), heart failure (HF), pulmonary embolism (PE) and deep vein thrombosis (DVT). We used two composite outcomes: (1) VTE, as an aggregate of PE and DVT and (2) ATE, as a composite of IS, TIA and MI. To avoid re-recording of the same complication we imposed a wash-out period of 90 days between records. Phenotypes for these complications were based on previously published studies. 3 4 8 18

All outcomes were ascertained in four different time periods following SARS-CoV-2 infection: the first period described the acute infection phase—that is, 0–30 days after COVID-19, whereas the later periods - which are 31–90 days, 91–180 days and 181–365 days, illustrate the post-acute phase ( figure 1 ).

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Study outcome design. Study outcomes of interest are defined as a COVID-19 infection followed by one of the complications in the figure, within a year after infection. Outcomes were ascertained in four different time windows after SARS-CoV-2 infection: 0–30 days (namely the acute phase), 31–90 days, 91–180 days and 181–365 days (these last three comprise the post-acute phase).

Negative control outcomes

Negative control outcomes (NCOs) were used to detect residual confounding. NCOs are outcomes which are not believed to be causally associated with the exposure, but share the same bias structure with the exposure and outcome of interest. Therefore, no significant association between exposure and NCO is to be expected. Our study used 43 different NCOs from previous work assessing vaccine effectiveness. 19

Statistical analysis

Federated network analyses.

A template for an analytical script was developed and subsequently tailored to include the country-specific aspects (eg, dates, priority groups) for the vaccination rollout. Analyses were conducted locally for each database. Only aggregated data were shared and person counts <5 were clouded.

Propensity score weighting

Large-scale propensity scores (PS) were calculated to estimate the likelihood of a person receiving the vaccine based on their demographic and health-related characteristics (eg, conditions, medications) prior to the index date. PS were then used to minimise observed confounding by creating a weighted population (overlap weighting 20 ), in which individuals contributed with a different weight based on their PS and vaccination status.

Prespecified key variables included in the PS comprised age, sex, location, index date, prior observation time in the database, number of previous outpatient visits and previous SARS-CoV-2 PCR/antigen tests. Regional vaccination, testing and COVID-19 incidence rates were also forced into the PS equation for the UK databases 21 and SIDIAP. 22 In addition, least absolute shrinkage and selection operator (LASSO) regression, a technique for variable selection, was used to identify additional variables from all recorded conditions and prescriptions within 0–30 days, 31–180 days and 181-any time (conditions only) before the index date that had a prevalence of >0.5% in the study population.

PS were then separately estimated for each staggered cohort and analysis. We considered covariate balance to be achieved if absolute standardised mean differences (ASMDs) were ≤0.1 after weighting. Baseline characteristics such as demographics and comorbidities were reported.

Effect estimation

To account for the competing risk of death associated with COVID-19, Fine-and-Grey models 23 were used to calculate subdistribution hazard ratios (sHRs). Subsequently, sHRs and confidence intervals were empirically calibrated from NCO estimates 24 to account for unmeasured confounding. To calibrate the estimates, the empirical null distribution was derived from NCO estimates and was used to compute calibrated confidence intervals. For each outcome, sHRs from the four staggered cohorts were pooled using random-effect meta-analysis, both separately for each database and across all four databases.

Sensitivity analysis

Sensitivity analyses comprised 1) censoring follow-up for vaccinated people at the time when they received their second vaccine dose and 2) considering only the first post-COVID-19 outcome within the year after infection ( online supplemental figure S1 ). In addition, comparative effectiveness analyses were conducted for BNT162b2 versus ChAdOx1.

Supplemental material

Data and code availability.

All analytic code for the study is available in GitHub ( https://github.com/oxford-pharmacoepi/vaccineEffectOnPostCovidCardiacThromboembolicEvents ), including code lists for vaccines, COVID-19 tests and diagnoses, cardiac and thromboembolic events, NCO and health conditions to prioritise patients for vaccination in each country. We used R version 4.2.3 and statistical packages survival (3.5–3), Empirical Calibration (3.1.1), glmnet (4.1-7), and Hmisc (5.0–1).

Patient and public involvement

Owing to the nature of the study and the limitations regarding data privacy, the study design, analysis, interpretation of data and revision of the manuscript did not involve any patients or members of the public.

All aggregated results are available in a web application ( https://dpa-pde-oxford.shinyapps.io/PostCovidComplications/ ).

We included over 10.17 million vaccinated individuals (1 618 395 from CPRD Gold; 5 729 800 from CPRD Aurum; 2 744 821 from SIDIAP and 77 603 from CORIVA) and 10.39 million unvaccinated individuals (1 640 371; 5 860 564; 2 588 518 and 302 267, respectively). Online supplemental figures S2-5 illustrate study inclusion for each database.

Adequate covariate balance was achieved after PS weighting in most studies: CORIVA (all cohorts) and SIDIAP (cohorts 1 and 4) did not contribute to ChAdOx1 subanalyses owing to sample size and covariate imbalance. ASMD results are accessible in the web application.

NCO analyses suggested residual bias after PS weighting, with a majority of NCOs associated positively with vaccination. Therefore, calibrated estimates are reported in this manuscript. Uncalibrated effect estimates and NCO analyses are available in the web interface.

Population characteristics

Table 1 presents baseline characteristics for the weighted populations in CPRD Aurum, for illustrative purposes. Online supplemental tables S1-25 summarise baseline characteristics for weighted and unweighted populations for each database and comparison. Across databases and cohorts, populations followed similar patterns: cohort 1 represented an older subpopulation (around 80 years old) with a high proportion of women (57%). Median age was lowest in cohort 4 ranging between 30 and 40 years.

View inline

Characteristics of weighted populations in CPRD Aurum database, stratified by staggered cohort and exposure status. Exposure is any COVID-19 vaccine

COVID-19 vaccination and post-COVID-19 complications

Table 2 shows the incidence of post-COVID-19 VTE, ATE and HF, the three most common post-COVID-19 conditions among the studied outcomes. Outcome counts are presented separately for 0–30, 31–90, 91–180 and 181–365 days after SARS-CoV-2 infection. Online supplemental tables S26-36 include all studied complications, also for the sensitivity and subanalyses. Similar pattern for incidences were observed across all databases: higher outcome rates in the older populations (cohort 1) and decreasing frequency with increasing time after infection in all cohorts.

Number of records (and risk per 10 000 individuals) for acute and post-acute COVID-19 cardiac and thromboembolic complications, across cohorts and databases for any COVID-19 vaccination

Forest plots for the effect of COVID-19 vaccines on post-COVID-19 cardiac and thromboembolic complications; meta-analysis across cohorts and databases. Dashed line represents a level of heterogeneity I 2 >0.4. ATE, arterial thrombosis/thromboembolism; CD+HS, cardiac diseases and haemorrhagic stroke; VTE, venous thromboembolism.

Results from calibrated estimates pooled in meta-analysis across cohorts and databases are shown in figure 2 .

Reduced risk associated with vaccination is observed for acute and post-acute VTE, DVT, and PE: acute meta-analytic sHR are 0.22 (95% CI, 0.17–0.29); 0.36 (0.28–0.45); and 0.19 (0.15–0.25), respectively. For VTE in the post-acute phase, sHR estimates are 0.43 (0.34–0.53), 0.53 (0.40–0.70) and 0.50 (0.36–0.70) for 31–90, 91–180, and 181–365 days post COVID-19, respectively. Reduced risk of VTE outcomes was observed in vaccinated across databases and cohorts, see online supplemental figures S14–22 .

Similarly, the risk of ATE, IS and MI in the acute phase after infection was reduced for the vaccinated group, sHR of 0.53 (0.44–0.63), 0.55 (0.43–0.70) and 0.49 (0.38–0.62), respectively. Reduced risk associated with vaccination persisted for post-acute ATE, with sHR of 0.74 (0.60–0.92), 0.72 (0.58–0.88) and 0.62 (0.48–0.80) for 31–90, 91–180 and 181–365 days post-COVID-19, respectively. Risk of post-acute MI remained lower for vaccinated in the 31–90 and 91–180 days after COVID-19, with sHR of 0.64 (0.46–0.87) and 0.64 (0.45–0.90), respectively. Vaccination effect on post-COVID-19 TIA was seen only in the 181–365 days, with sHR of 0.51 (0.31–0.82). Online supplemental figures S23-31 show database-specific and cohort-specific estimates for ATE-related complications.

Risk of post-COVID-19 cardiac complications was reduced in vaccinated individuals. Meta-analytic estimates in the acute phase showed sHR of 0.45 (0.38–0.53) for HF, 0.41 (0.26–0.66) for MP and 0.41 (0.27–0.63) for VACA. Reduced risk persisted for post-acute COVID-19 HF: sHR 0.61 (0.51–0.73) for 31–90 days, 0.61 (0.51–0.73) for 91–180 days and 0.52 (0.43–0.63) for 181–365 days. For post-acute MP, risk was only lowered in the first post-acute window (31–90 days), with sHR of 0.43 (0.21–0.85). Vaccination showed no association with post-COVID-19 HS. Database-specific and cohort-specific results for these cardiac diseases are shown in online supplemental figures S32-40 .

Stratified analyses by vaccine showed similar associations, except for ChAdOx1 which was not associated with reduced VTE and ATE risk in the last post-acute window. Sensitivity analyses were consistent with main results ( online supplemental figures S6-13 ).

Figure 3 shows the results of comparative effects of BNT162b2 versus ChAdOx1, based on UK data. Meta-analytic estimates favoured BNT162b2 (sHR of 0.66 (0.46–0.93)) for VTE in the 0–30 days after infection, but no differences were seen for post-acute VTE or for any of the other outcomes. Results from sensitivity analyses, database-specific and cohort-specific estimates were in line with the main findings ( online supplemental figures S41-51 ).

Forest plots for comparative vaccine effect (BNT162b2 vs ChAdOx1); meta-analysis across cohorts and databases. ATE, arterial thrombosis/thromboembolism; CD+HS, cardiac diseases and haemorrhagic stroke; VTE, venous thromboembolism.

Key findings

Our analyses showed a substantial reduction of risk (45–81%) for thromboembolic and cardiac events in the acute phase of COVID-19 associated with vaccination. This finding was consistent across four databases and three different European countries. Risks for post-acute COVID-19 VTE, ATE and HF were reduced to a lesser extent (24–58%), whereas a reduced risk for post-COVID-19 MP and VACA in vaccinated people was seen only in the acute phase.

Results in context

The relationship between SARS-CoV-2 infection, COVID-19 vaccines and thromboembolic and/or cardiac complications is tangled. Some large studies report an increased risk of VTE and ATE following both ChAdOx1 and BNT162b2 vaccination, 7 whereas other studies have not identified such a risk. 25 Elevated risk of VTE has also been reported among patients with COVID-19 and its occurrence can lead to poor prognosis and mortality. 26 27 Similarly, several observational studies have found an association between COVID-19 mRNA vaccination and a short-term increased risk of myocarditis, particularly among younger male individuals. 5 6 For instance, a self-controlled case series study conducted in England revealed about 30% increased risk of hospital admission due to myocarditis within 28 days following both ChAdOx1 and BNT162b2 vaccines. However, this same study also found a ninefold higher risk for myocarditis following a positive SARS-CoV-2 test, clearly offsetting the observed post-vaccine risk.

COVID-19 vaccines have demonstrated high efficacy and effectiveness in preventing infection and reducing the severity of acute-phase infection. However, with the emergence of newer variants of the virus, such as omicron, and the waning protective effect of the vaccine over time, there is a growing interest in understanding whether the vaccine can also reduce the risk of complications after breakthrough infections. Recent studies suggested that COVID-19 vaccination could potentially protect against acute post-COVID-19 cardiac and thromboembolic events. 11 12 A large prospective cohort study 11 reports risk of VTE after SARS-CoV-2 infection to be substantially reduced in fully vaccinated ambulatory patients. Likewise, Al-Aly et al 12 suggest a reduced risk for post-acute COVID-19 conditions in breakthrough infection versus SARS-CoV-2 infection without prior vaccination. However, the populations were limited to SARS-CoV-2 infected individuals and estimates did not include the effect of the vaccine to prevent COVID-19 in the first place. Other studies on post-acute COVID-19 conditions and symptoms have been conducted, 28 29 but there has been limited reporting on the condition-specific risks associated with COVID-19, even though the prognosis for different complications can vary significantly.

In line with previous studies, our findings suggest a potential benefit of vaccination in reducing the risk of post-COVID-19 thromboembolic and cardiac complications. We included broader populations, estimated the risk in both acute and post-acute infection phases and replicated these using four large independent observational databases. By pooling results across different settings, we provided the most up-to-date and robust evidence on this topic.

Strengths and limitations

The study has several strengths. Our multinational study covering different healthcare systems and settings showed consistent results across all databases, which highlights the robustness and replicability of our findings. All databases had complete recordings of vaccination status (date and vaccine) and are representative of the respective general population. Algorithms to identify study outcomes were used in previous published network studies, including regulatory-funded research. 3 4 8 18 Other strengths are the staggered cohort design which minimises confounding by indication and immortal time bias. PS overlap weighting and NCO empirical calibration have been shown to adequately minimise bias in vaccine effectiveness studies. 19 Furthermore, our estimates include the vaccine effectiveness against COVID-19, which is crucial in the pathway to experience post-COVID-19 complications.

Our study has some limitations. The use of real-world data comes with inherent limitations including data quality concerns and risk of confounding. To deal with these limitations, we employed state-of-the-art methods, including large-scale propensity score weighting and calibration of effect estimates using NCO. 19 24 A recent study 30 has demonstrated that methodologically sound observational studies based on routinely collected data can produce results similar to those of clinical trials. We acknowledge that results from NCO were positively associated with vaccination, and estimates might still be influenced by residual bias despite using calibration. Another limitation is potential under-reporting of post-COVID-19 complications: some asymptomatic and mild COVID-19 infections might have not been recorded. Additionally, post-COVID-19 outcomes of interest might be under-recorded in primary care databases (CPRD Aurum and Gold) without hospital linkage, which represent a large proportion of the data in the study. However, results in SIDIAP and CORIVA, which include secondary care data, were similar. Also, our study included a small number of young men and male teenagers, who were the main population concerned with increased risks of myocarditis/pericarditis following vaccination.

Conclusions

Vaccination against SARS-CoV-2 substantially reduced the risk of acute post-COVID-19 thromboembolic and cardiac complications, probably through a reduction in the risk of SARS-CoV-2 infection and the severity of COVID-19 disease due to vaccine-induced immunity. Reduced risk in vaccinated people lasted for up to 1 year for post-COVID-19 VTE, ATE and HF, but not clearly for other complications. Findings from this study highlight yet another benefit of COVID-19 vaccination. However, further research is needed on the possible waning of the risk reduction over time and on the impact of booster vaccination.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

The study was approved by the CPRD’s Research Data Governance Process, Protocol No 21_000557 and the Clinical Research Ethics committee of Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol) (approval number 4R22/133) and the Research Ethics Committee of the University of Tartu (approval No. 330/T-10).

Acknowledgments

This study is based in part on data from the Clinical Practice Research Datalink (CPRD) obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. We thank the patients who provided these data, and the NHS who collected the data as part of their care and support. All interpretations, conclusions and views expressed in this publication are those of the authors alone and not necessarily those of CPRD. We would also like to thank the healthcare professionals in the Catalan healthcare system involved in the management of COVID-19 during these challenging times, from primary care to intensive care units; the Institut de Català de la Salut and the Program d’Analítica de Dades per a la Recerca i la Innovació en Salut for providing access to the different data sources accessible through The System for the Development of Research in Primary Care (SIDIAP).

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Supplementary data.

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Data supplement 1

AMJ and MC are joint senior authors.

Contributors DPA and AMJ led the conceptualisation of the study with contributions from MC and NM-B. AMJ, TD-S, ER, AU and NTHT adapted the study design with respect to the local vaccine rollouts. AD and WYM mapped and curated CPRD data. MC and NM-B developed code with methodological contributions advice from MTS-S and CP. DPA, MC, NTHT, TD-S, HMEN, XL, CR and AMJ clinically interpreted the results. NM-B, XL, AMJ and DPA wrote the first draft of the manuscript, and all authors read, revised and approved the final version. DPA and AMJ obtained the funding for this research. DPA is responsible for the overall content as guarantor: he accepts full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish.

Funding The research was supported by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC). DPA is funded through a NIHR Senior Research Fellowship (Grant number SRF-2018–11-ST2-004). Funding to perform the study in the SIDIAP database was provided by the Real World Epidemiology (RWEpi) research group at IDIAPJGol. Costs of databases mapping to OMOP CDM were covered by the European Health Data and Evidence Network (EHDEN).

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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39 Empirical Research in Law
This is that empirical research in law is not the preserve of the academic lawyer alone, but has attracted scholars from across the social sciences, especially sociologists, economists, and psychologists. ... some now contribute as teachers of 'fringe' legal subjects—for example, criminology, sociology of law, socio-legal studies—which ...
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law.1. During 2005, during the consultation stage of the inquiry, many people who spoke to the inquiry team said they were keen to do empirical research. on law, but felt they were not equipped to do it. If lawyers, they feared that. they did not have the requisite understanding of social science research.
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Undertaking empirical research in law can be a daunting task, one for which current undergraduate and postgraduate legal education does not provide a great deal of preparation. Yet the ability to undertake such research is valuable and, some suggest, in demand. Many areas of law, its operation and effects, can be usefully informed by empirical ...
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Research Handbook on Empirical Studies in Intellectual Property Law by Estelle Derclaye (Editor) Call Number: K1401 .R4 2023. ISBN: 9781802206203. Publication Date: 2023-07-14. This comprehensive Research Handbook explores empirical legal studies of intellectual property law.
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Lee Epstein & Andrew D. Martin, An Introduction to Empirical Legal Research (2014) Mark A. Hall & Ronald F. Wright, Systematic Content Analysis of Judicial Opinions, 96 Calif. L. Rev. 63 (2008). Sarah E. Ryan, Teaching Empirical Legal Research Study Design: Topics & Resources, 23 Perspectives 152 (2015)
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The Researcher shall conduct empirical research on the following topics in law: Impact judicial decisions social behavior. Efficacy alternative dispute resolution mechanisms. Intersection law technology digital age. 2. Research Plan. The Researcher shall submit a detailed research plan to the Institution for approval.
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where, C H E i is the variable of interest for the household; μ is the mean of C H E i; and R i is the i t h ranked household in the socio-economic distribution from most disadvantaged (i.e., poorest) to the least disadvantaged (i.e., richest). The value of C I ranges between −1 and + 1, where a positive value indicates the distribution concentrated among the rich and a negative value ...
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39 Empirical Research in Law

1 Introduction

2 Burgeoning Activity

3 Why is Empiricism Found in Some Fields of Law but Not Others?

4 Main Questions and Preoccupations

5 Research Methods

5.1 Qualitative versus Quantitative Methods

5.2 Research Access

6 The Relationship to Theory

7 Influence upon Law and Social Policy

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Selected Treatises: How to Conduct Empirical Legal Research

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