health economics research paper

• Open access
• Published: 12 May 2023

Global trends in the scientific research of the health economics: a bibliometric analysis from 1975 to 2022

• Liliana Barbu   ORCID: orcid.org/0000-0003-0641-7483 1  

Health Economics Review volume  13 , Article number:  31 ( 2023 ) Cite this article

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Health science is evolving extremely rapidly at worldwide level. There is a large volume of articles about health economics that are published each year. The main purpose of this research is to explore health economics in the world's scholarly literature based on a scient metric analysis to outline the evolution of research in the field.

The Web of Science repository was used to get the data (1975–2022). The study explores 1620 documents from health economics. CiteSpace software was used to provide network visualisations. Four thousand ninety-six authors, 1723 institutions, 847 journals and 82 countries were involved in the sample. The current research contains a descriptive analysis, a co-authorship analysis, a co-citation analysis, and a co-occurrence analysis in health economics.

Drummond M.F (author), the USA (country), University of London (institution) and Value Health (journal) are among the most important contributors to the health economics literature. Co-authorship analysis highlights that cooperation between authors, institutions and countries is weak. However, Drummond M.F. is the most collaborative author, the USA is the most collaborative country, and University of York is the most collaborative institution. The study offers an image about the most co-cited references (Arrow K.J., 1963), authors (Margolis H.) and journals (British Medical Journal). The current research hotspots in health economics are “behavioural economics” and “economic evaluation”. The main findings should be interpreted in accordance with the selection strategy used in this paper.

All in all, the paper maps the literature on health economics and may be used for future research.

Introduction

The health economy is a branch of the economy that deals with concerns of the production and consumption of health services and healthcare that relate to efficiency, effectiveness, value, and behaviour. Applying economic ideas, concepts, and methods to institutions, actors, and activities that have an impact on people's health is known as health economics [ 1 ]. The health economy is studying how to allocate limited resources to meet human desires in the medical industry and disease care. The health economy often tries to meet the most pressing challenges facing the health system. Studies in health economics provide to decision-makers precious information about the effective use of resources that are available to maximize health benefits.

The health economics is a component of public health, a component that It can be used to examine health issues and medical treatment. Health economists consider the origin of their discipline to Petty W. (1623–1687) [ 2 ] who propose valuation of human life based on a person’s contribution to national production. Arrow K. is credited with creating the field of health economics in a work where he conceptually distinguished between health and other goods [ 3 ]. Since Arrow K.'s fundamental publication on health economics from 1963, the scale of the healthcare sector, the share of public budgets allocated to healthcare, and the body of research on health economics have all increased significantly [ 4 ].

The current pandemic context has proved the need for a functioning public health system capable of meeting any challenges. The World Health Organization report for 2020 presents an examination of 190 nations' global health spending from 2000 to 2018. The report shows that global health spending has increased consistently between 2000 and 2018, reaching $ 8.3 trillion, or 10% of world GDP [ 5 ]. At the level of OECD Member States, the latest estimates show an average increase in health spending of about 3.3% in 2019, whereas health spending as a percentage of GDP stayed about where it had been in prior years, at 8.8% [ 6 ]. These indicators rose sharply in 2020, as economies faced a pandemic. The increases were driven by an increase in the level of allocation of government resources for health, while private spending on health tended to decline. At EU level, the public sector plays a major role in funding health services. In 2/3 of Member States, more than 70% of health spending is funded by the public sector [ 7 ]. In 2020, the EU's overall public health spending was €1.073 billion, or 8.0% of GDP ( https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Government_expenditure_on_health ). For governments, public spending on health is one of the spending categories with the quickest growth.

Health economics is the application of economic theory, models, and empirical techniques to the analysis of decision-making by individuals, health care providers, and governments regarding health and health care. Even though the methodologies are distinct in terms of health care, health economics aims to apply the same analytical tools that would be applied to any good or service that the economy provides [ 8 ]. By offering a clear framework for decision-making based on the efficiency principle, health economics seeks to simplify decision-making [ 9 ]. Extensive government interference, insoluble uncertainty in many dimensions, asymmetric knowledge, barriers to entry, externality, and the presence of a third-party agent are all characteristics that set health economics apart from other fields [ 10 ].

Health economics is the field were interdisciplinarity bring additional value for society. Health economics development has not been without controversy. Health economics refers to a variety of elements that interact to affect the expenses and spending of the healthcare sector. Its controversy rises from the roles of people, healthcare providers, insurers, governmental bodies, and private companies in influencing the healthcare sector expenses. The parties that interact in this field have some conflicting goals. On the one hand, health care policymakers and public hospitals have as objective to provide real value to the patients, to balance public interest and economic restrictions. On the other hand, private hospitals, insurance companies aim to obtain profit for their shareholders. There are several weaknesses that should be rectified in the future. Among weaknesses it can be found deficiencies in the supply of health economists [ 11 ], a lack of financial resource independence between the local and central levels, the key macroeconomic variables' unfavourable behaviour, and the difficulty in developing new financing alternatives [ 12 ]. In addition to having too close relationships to national institutions and sponsors of health economics research, health economics also has excessively loose connections with general economic theory [ 13 ]. Considering increased demands in healthcare services and limited health care budgets, health economics faces real challenges in providing decision making frameworks and there will always be challenging healthcare decisions. Although it has not always been an impartial instrument, health economics does give useful information for policy [ 14 ]. Regarding how well economics integrates with promoting health, there is scepticism, and public health has mixed feelings on the subject. Health economics has been accused of focusing more on the consumption of healthcare services than the creation of healthcare [ 15 ]. Despite several methodological limitations, health economics can provide helpful concepts and principles that aid in comprehending the effects of resource allocation decisions [ 9 ]. All practitioners must have a elementary comprehension of some economic concepts to both understand the helpful ideas the field may provide and recognize its inadequacies.

The main purpose of the research is to examine the health economics literature published worldwide based on a scient metric analysis to outline the development of the field's research. The existence of a multitude of articles published on health economics determines the need to address and measure it quantitatively. Such an analysis is justified by the need must be aware of the current trends and future directions of research in the field of health economics. Health science is evolving extremely rapidly at worldwide level. There is a large volume of articles about health economics that are published each year. Another argument is that there several computer programs which allows for scient metric analysis of health economics publications. This article contributes to the bibliometric literature on health economics by offering answers to the subsequent research inquiries: How scientific production has evolved in health economics? Who are the most important authors and publications in health economics? What are the geographical and institutional hubs of knowledge production in health economics? What kind of collaboration between authors, organizations, and nations are there in the field of health economics research? Which are the most cited authors and the most cited papers, and which are the most attractive journals for publishing research results in health economics? What are the most debated conceptual approaches in health economics?

The remainder of the paper is structured as follows. The second section introduces a short literature review. Research methodology and data collection are presented in Sect. 3. Section 4 contains the quantitative and qualitative scient metric analysis on health economics by using CiteSpace software (descriptive analysis, collaboration analysis, co-citation analysis and keywords co-occurrence analysis). The last part concludes the analysis, presents the research limitations, and describes future directions of research.

Literature background

Although there are thousands of articles published on health economics, very few articles aim for bibliometric analysis of the field and use computer programs. A first article published by Rubin, R. M. and Chang, C. F. (2003) aims at the study of 5,545 indexed articles, in the period 1991–2000, in the EconLit database, in the Health Economics section [ 16 ]. The second study is published by Wagstaff, A. and Culyer, A. J. in 2012 and extends the previous bibliometric research done by Rubin and Chang also based on the articles indexed in EconLit on health, over 40 years [ 17 ]. The third study, published by Moral-Munoz J.A et all in 2020, focuses on articles indexed in the Web of Science, between 2010 and 2019, which have the word "health" and do not use scientometric software [ 18 ].

It would be worth mentioning a descriptive analysis of the field conducted by Jakovljevic M. and Pejcic A. in 2017, but without the use of bibliometric indicators. The authors quantitatively analyze health economics publications by querying the PubMed, Scopus, WoS and NHS economic evaluation Database between 2000 and 2016 and conclude with the existence of an upward flow of health economics publications [ 19 ]. In this context, the proposed research is characterized by focusing on WoS articles that refer strictly to "health economics" and their computer processing to obtain maps and connections between studies.

Research methodology

Research methods.

In the current paper two research methods were used: bibliometric analysis and knowledge mapping. Regarding the first one, it should be mentioned that bibliometric research methods are used delivering quantitative analysis of textual works, in this case publications about health economics. This method allows bibliographic overviews of scientific production in the field. In the scientific community, the technique is increasingly employed to provide details regarding relationships between various groups [ 20 ]. Bibliometric analysis uses statistical tools and different metrics as part of the analysis (frequency/ count, co-citation, co-authorship, co-occurrence, betweenness centrality, citation burst, modularity, centrality, sigma, Silhouette etc.). Bibliometric analysis naturally presents itself as a tool to qualify, then quantify, the study conducted [ 21 ].

Regarding the second one, bibliometric analysis uses a large quantity of information that should be transformed in knowledge. This is done by using data visualization and knowledge maps. An enormous and complex collection of knowledge resources can be more easily accessed and navigated by using knowledge mapping strategies [ 22 ]. Knowledge mapping is the process of making knowledge maps, it makes explicit knowledge graphic and visual. Knowledge maps are static, they are a “snapshot in time” that aids in understanding and organizing knowledge flow for researchers [ 23 ]. A process, method, or instrument called “knowledge mapping” is used to analyse knowledge to find traits or meanings and perceive knowledge in an understandable and transparent way [ 24 ]. One of the advantages of knowledge mapping includes the freedom to combine without restriction, i.e., without restrictions on the number of connections and concepts that can be established [ 25 ].

Data source and search strategy

For this analysis we decided to use one of the most reliable databases: Web of Science (WoS) because it contains a data for large period. The data was retrieved from the Web of Science Core Collection by using title search tool TI = (health economics). The primary literature data were downloaded on 7th of October 2022. The query objective was to integrate in this analysis all research papers related to health economics. We did not introduce any restrictions regarding the topic or time span for searching documents. We intend to have a comprehensive view of the research area and to see its evolution over time. As a result, 2340 documents were retrieved. Among publications about health economics, the most numerous documents are the articles (37.6%), followed by editor materials (19.8%), meeting abstracts (13.9%) and book reviews (13.3%). There are also review articles on the subject, proceeding papers, letters, books, and book chapters which were kept in the sample. The other types of documents were removed resulting a sample of 2305 publications. The language of almost all publications is English (91.4%), followed by German (4.3%). The percentage of publications produced in other languages, such as French, Spanish, Portuguese, Russian etc. is less than 1.5% for each of them. Publications in other languages than English were eliminated, remaining 2108 documents in the sample.

The next step is to identify and remove duplicates by using Excel function (Conditional Formatting – Highlight Duplicate Values), therefore 8 duplicates were removed. In the sample under analysis, a multitude of types of documents indexed in WoS and referring to the concept of health economics can be observed. During the step of checking for duplications, it was found that there are too many duplicates of documents’ title, most of them due to editorial materials or book reviews. This led to a thorough analysis of publication by type of document (eg there are more than 10 reviews for one book or more than 10 editorial materials signed by the same editor). We identified some publications which are irrelevant for the purpose of our analysis. One hundred eighty-six editorial materials without citations and all 286 book reviews were removed resulting 1628 publications. We kept the editorial materials with citation because some of them have more than 100 citations. We searched for anonymous publications, more exactly we looked for incomplete data (author’s name is missing) and we removed 8 documents.

For the remaining documents the "Full Record and Cited References" was downloaded on 13th of October 2022 (txt files) and used as original data for the proposed bibliometrics analysis and science mapping. The final data collection, which consists of 1620 publications, is supported by 16,755 citing articles (excluding self-citations) and has been cited 18,504 times (excluding self-citations), giving it an H-index of 59. The data are statistical analysed by using annual distribution of publications, authors, journals. Co-authorship analysis focuses on collaboration between authors, institutions, and countries. Cited references, cited authors, and cited journal are used in co-citation analyses, and finally, the co-occurrence will integrate keyword in this research.

The graphical representation of selection procedure can be seen in Fig.  1 .

Selection procedure flow chart. Source: Authors

Visualization tools

Bibliometric method needs a certain amount of data to be statistically credible. This is the reason for that computerized data treatment is needed. Moreover, databases contain hundreds or thousands of entries which are analysed by using computer software. There is many bibliometric software, each of them has particularities and weaknesses. CiteSpace was chosen in this study because it is very user friendly, intuitively, and easy to use. CiteSpace 6.1.R2. available for free download at https://citespace.podia.com . A variety of networks created from scientific publications, such as collaboration networks, author co-citation networks, and document co-citation networks, are supported by structural and temporal analysis in CiteSpace. CiteSpace can produce knowledge domain X-rays. The CiteSpace parameters for this investigation were as follows: time-slicing was from 1975 to 2022, years per slice was 1 year, Look Back Years (LBY) = -1, Link Retaining Factor (LRF) = -1. For text processing and links, we preserved the default settings. We used several nodes (authors, institutions, journal, references, keywords) and metrics (such as citation burstiness, Sigma, Silhouette, rad Q, betweenness centrality) depending on the study that was done. Top N% is set to be equal to 100%, Top N is set to be 50, and g-index is set to be 25.

Statistical analysis

The first step to follow in the scient metric analysis is to analyse the evolution of publications’ number in the researched field. The way in which they are distributed over the years indicates the attention that the field of health economics has benefited from and the speed at which its conceptual development took place. The first 3 papers about health economics were published in 1975, indicating the lowest number of annual publications, but also a concept that has existed for over 4 decades. From Fig.  2 , a general upward trend of health economics publications can be observed, but with numerous upward and downward fluctuations, generating sinusoidal cycles with an average duration of 3–4 years. The period 1975 – 1986 is characterized by a very low number of publications, 98 publications written by 110 authors in 12 years, representing 6% of the total sample. The next two decades (1987 – 2006) are characterized by a slightly increasing trend in the number of publications, with an annual average of approximately 23 publications on health economics, reaching a total of 454 publications written by 826 authors and representing 28% of the total number of analysed publications. Cyclical evolution is highlighted by booms in 1987, 1990, 1995, 1999, 2001.

Literature production related to health economics between 1975 and 2022. Source: Authors

The following period, 2007 – 2022 (16 years) is characterized by an upward evolution of the number of health economics publications, 1068 publications with an annual average of 67 articles (3261 authors involved), meaning 2.3 times more numerous as in the previous two decades and representing 66% of the total sample. In 2017, 86 studies on health economics were published, reaching the highest value in the analysed period. The quantitative evolution of publications in health economics it is explained by a higher interest of the researchers and policymakers to explore the benefits of health economics. The need to identify the ways in which health economics contributes to the healthcare system development represent a solid motivation to continue intensive research in the field.

The evolution of the citations’ number follows, like a shadow, the evolution of publications’ number. The upward trend is maintained, also respecting the previously presented temporal distribution, but without cyclical and sinusoidal fluctuations. The evolution of the citations’ number indicates the growing interest of specialists in researching the field, especially after 2000 when a constant and galloping annual increase in citations begins. The last 5 years show a very high interest of researchers and academics in health economics research, with a maximum point in 2021, with over 2000 citations, an evolution argued by the emergence of the global pandemic. All the figures and observations indicate a constant interest in the conceptualization of health economics and foresee a deeper development in the future.

Geographical analysis allows a better understanding of the field. The 1620 publications involved the work of authors from 82 countries. Among them, the first 10 states with significant contributions in the field of health economics stand out: the USA (605 papers), England (400), Canada (115), Australia (103), Netherlands (75), Scotland (64), Germany (59), Switzerland (57), France (47) and Italy (43). 96.8% of all publications were produced by top-10 countries. According to statistics, the USA is the top nation. 37% of all analysed documents are written by American authors, which is 1.5 times more than values recorded by England (rank 2) and 5.2 times more than Canada, rank 3. There are 49 nations where there are fewer than or equal to 5 publications during entire period.

In our study, a sum of 4096 different authors were identified, and they individually published between one and 16 papers, but only 170 persons are co-authors of more than 3 papers. Table 1 lists the top 10 authors with publications about health economics. Drummond M.F. is the leader, even if he published Essentials of Health Economics with his co-author, Mooney G.H., in 1982. He is affiliated to University of Yor (the UK). The top ten most productive authors published 107 articles, which represents 6.6% of the total publications. The most authors (95.8% of all authors) contributed to the health economics research with less than two papers. It should be noted that the number of authors is 2.5 times over the number of papers., which means that publications are made by cooperation between researchers.

From the point of view of affiliation, the 4096 authors belong to 1723 institutions. The top 10 organizations with many health economics articles are University of London (91 publications), University of California System (54), University of York (51), Harvard University (45), University of Birmingham (41), University of Pennsylvania (34), University of Oxford (30), University of Aberdeen (28), University of California Los Angeles (28) and University of Washington (28). The list is dominated by institutions from the UK and the USA. The top-10 institutions contributed to health economics research field by 230 papers which represents 26.5% of total publications.

It is very important to see which journals have published the most articles about health economics. Regarding the publication’s titles, 847 distinct journals published all 1620 documents related to health economics. It should be mentioned that 782 journals (92.3%) published from one to three articles on health economics during 1975 – 2022. Table 2 lists the top 10 most prolific journals, and together they have published 364 articles, which means 43% of all publications in the sample. The leading journal is the Value in Health (Impact Factor = 5.156) with 160 papers meaning 9.8% of all publications from the sample.

Co-authorship analysis

Co-authorship networks and social network analysis are becoming more and more effective techniques for evaluating collaboration patterns and locating top scientists and institutions [ 26 ]. The author collaboration network can help identify authors with high contributions and reveal the co-operative relationships between the authors. By using CiteSpace, the co-authorship network was created without pruning the sliced networks. Co-authors network has 1028 nodes and 1166 links. Figure  3 presents the network between the most collaborative authors in health economics, all of them published 4 or more publications as co-authors. As indicated by the node name, each node represents a different author, and the font size corresponds to the number of publications for each author. The connections made by the co-authorship of researchers are represented by the interconnections between each pair of nodes. The degree of cooperation between the two authors is indicated by the thickness of the link.

The network of authors’ collaboration in health economics. Source: Authors

Co-authors’ map shows that there are not strong collaboration relationships between authors, the network density level is 0.0022. Moreover, they are divided in small research groups and cooperation for research in health economics is insignificant. Top five collaborative authors are Drummond M. (20 publications), Mooney G. (16), Trosch R. (8), Marchese D. (8) and Fuchs V. (8). They are followed by Basu A. (7), Edwards R. (7), Coast J. (7), Peeples P. (7) and Comella C. (6).

In Fig.  3 it can be seen the cooperation between two research teams. These research teams are formed around key authors in health economics and integrated as most collaborative ones. First research team is created around Drummond M. and Mooney G. They published in 1982 and 1983, in British Medical Journal, 9 papers about different aspects of health economics [ 27 , 28 ]. The second research team is created around Trosch R. and Marchese D., who participated between 2012 and 2015 at several annual meeting, conferences, and congresses to present their work about clinical and health economics outcomes registry in cervical dystonia [ 29 , 30 ]. There are 72 scholars as co-authors in at least 3 publications showing a weak cooperation in health economics. From the perspective of citation burst, there are 5 bursting authors with a burst duration between 2 and 8 years: Drummond M. 1981–1999, Mooney G. 1982–1986, Marchese D. 2012–2015, Trosch R. 2012–2015, and Peeples P. 2018–2020. Bust analysis confirms the existence of the two research teams and their period of activity.

We continue exploring the co-authorship analysis by studying the level of cooperation between institutions. For this purpose, we generated a network where the nodes are the institutions, and we did not used pruning methods. The level of cooperation is revealed by the thickness between institutions’ nodes. The network contains 751 nodes, 944 links, and a density of 0.0034. In Fig.  4 are labelled the institutions with more than 4 collaborative papers, the label size is depending on the number of collaborative publications. No institution has a large value of centrality, meaning that cooperation among the analysed institutions is weak, the links are very transparent because of an insignificant number of publications written by collaboration between organizations or universities.

The network of institutions’ collaboration in health economics. Source: Authors

As seen in Fig.  4 , the top-10 most collaborative institutions in health economics area are: University of York (28 publications), University of Oxford (23), University of Pennsylvania (21), University of Washington (20), University of Birmingham (17), Erasmus University (16), Harvard University (16), Bangor University (15), University of California Los Angeles (13) and University of Toronto (12). There are six institutions for which there was identified citation burst as follows: University of Oxford 2016–2020, University of Pennsylvania 2017–2022, University California Los Angeles 2013–2016, King’s College London 2006–2011, London School of Hygiene & Tropical Medicine 2008–2010, University of Washington 2015–2018. Cooperation among institutions is depending on cooperation among authors. It is understood that poor collaboration at the individual level is followed by an identical one at the organizational level.

Progress in any field can be achieved only by communication. Analysing country co-authorship may lead to identification of leading states in health economics research. The visualisation map for country collaboration reveals a network of 202 nodes, 710 links and 0.035 density. It should be noted that country co-authorship network has a density 10 times larger than institutions co-authorship network. The map was generated in CiteSpace without pruning parameter. In Fig.  5 are displayed the countries having more than 5 collaborative health economics-related publications.

The network of countries’ collaboration in health economics. Source: Authors

As can be observed, the biggest nodes correspond to the most prominent and cooperative nations. The collaboration between institutions from these nations is shown by the links between the nodes. The discrepancies between the first two countries and the other states are obvious. The network of the most collaborative country, the USA, consist in 521 publications. It is followed by England with 344 publications. It is obvious that these two nations played a crucial part in worldwide academic exchanges in health economics area. The third and the fourth most collaborative countries are Canada (105 publications) and Australia (100 publications), which shows a degree of cooperation 5 times lower than that of the leading country. The top-10 most collaborative countries continue with the following nations: Netherlands (74 publications), Germany (58), Switzerland (56), Scotland (48), France (46) and Italy (43). Citation burst was identified for 4 countries: the USA 1975–1981, Scotland 1982–2003, Switzerland 1999–2006, and China 2020–2022. Citation burst analysis reveals that China, which stated to published research in health economics in 2006, faces an upward trend in the last two years.

Co-citations analysis

The following step of our current analysis is to find the most frequently cited publications in health economics sector. Co-citation reference analysis help to identification of the most important references in health economics. 16,755 references are linked to our sample. We obtain a co-citation network of 1550 nodes and 7240 links with a density of 0.0060. The network map was obtained without pruning parameter. In Fig.  6 are labelled the papers with more than 5 co-citations. Table 3 lists the top 10 articles in the field of health economics by the number of citations.

Visualization of reference co-citation networks for health economics research. Source: Authors

As we expected, the most influential paper is published by Arrow K.J. in 1963. In his paper, the author investigates and studies the unique distinctions between medical care and other goods and services in normative economics. He focuses on medical-care industry and its efficacy by rethinking the industry from economics perspective. This publication is the basic brick in the conceptualization of health economics. Unfortunately, this part of analysis reveals some basic limitation in bibliometric analysis: incomplete and compromised database because of incorrect data filled by authors. As it can be seen in Fig.  6 , the second most influential paper belongs to an anonymous author who wrote in 1996 a paper about cost effectiveness. A manual search in references database revealed the possibility to correlate the anonymous publications to a book written by Gold M.R., Siegel J.E., Russell L.B. and Weinstein M.C. The authors published in 1996 a book about cost effectiveness in health and medicine and there are several book reviews about it. The third and the fourth most co-cited publications are signed by Drummond M.F. and his co-authors. In fact, it is about a book entitled “Methods for the Economic Evaluation of Health Care Programmes”, first published in 1987 at and then renewed in the following editions: 1997 (2nd), 2005 (3rd) and 2015 (4th). Regardless the edition number, the book is a worldwide bestseller and it very cited in health economics research. It should be mentioned that the 2nd edition of the book appears twice in the database because some authors incorrectly cited Drummond. There are many book reviews for this book because it describes techniques and tools for evaluation of health care programs. It provides syntheses of new and emerging methodologies, and it is less concerned with the theoretical and ethical foundations of the methodologies (Drummond M.F et all, 2005). The book promotes basic health economic concepts and theories.

The citation burst was checked to see the period when a document citation increases sharply in frequency. There are 12 cited papers with citation burst fluctuating from 3.95 for Volpp K.G (2008) and 9.58 for Arrow K.J. (1963). Ten of twelve papers with citation burst are the ones from Table 3 , the most co-cited documents in health economics. The top-10 papers by burst are Arrow K.J. 1963 (period 2012–2018, citation burst 9.58), Drummond M.F. 1997 (2000–2008, 8.76), Anonymous 1996 (1999–2011, 8.86), Drummond M.F 2005 (2008 – 2019, 8.42), Kahneman D. 2011 (2013–2022, 5.03), Williams A. 1985 (1986–1998, 4.44), Lakdawalla (2018–2022, 4.44), Kahneman D. 1979 (2019–2022, 4.38) and Grossman M. 1972 (2016–2019, 4.35).

Two of Kahneman D.’s works stands out. One of them is represented by a book, another worldwide bestseller, entitled “Thinking, Fast and Slow” published in 2011 in London. His psychological book is appreciated because it aids in the public understanding of issues related to engineering, medicine, and behavioural science. The second paper is written by Kahneman D. and Tversky A. in 1979 and presents opponents of the anticipated utility theory as a framework for risky decision-making and introduces an alternative model called prospect theory.

We can find highly cited authors whose work is well known in the health economics research community by using author co-citation networks. CiteSpace configurations are the same. The network of co-cited writers has 1422 nodes, 12,462 linkages, with a density of 0.0123. The node size reflects the number of co-citations by author. In Fig.  7 the nodes with co-citations over 14 are labelled by the corresponding first author. Once again there are incomplete data in the database. We face with an anonymous person as the most cited author in health economics research. This author without name was 300 time co-cited. We manually checked the database to find additional information about this anonymous author. According to the findings we assume it is about Margolis H. who published in 1982 a book about selfishness, altruism, and rationality. Margolis H. is a professor at the University of Chicago and in his book about social choice propose and argue a distinction between self-interest and group-interest for a person, and he also develop an equilibrium model for his theory [ 41 ].

Visualization of authors co-citation networks for health economics research. Source: Authors

Drummond M.F. is on the second position, positioning himself with two publications in the top-10 most co-cited authors. Once again it is about his publication with Mooney G.H. about Essentials in Health Economics which was already mentioned in the paper. Williams A. is the third co-cited author, followed by Culyer A.J and Arrow K.J. It should be noted that World Health Organization’s (WHO) publications are ones of the most co-cited document in health economics research. Unfortunately, it is hard to identify the titles of WHO’s publications from 1993 and 2009 (see Table 4 ) because there is more than one publication per year for this international organization. However, we assume that it is about an anonymous publication focused on tuberculosis as a worldwide problem [ 42 ] (published in 1993) and a publication about health risk at the global level [ 43 ] (published in 2009).

There are no scholars who have a betweenness centrality greater than zero. This indicates that there is no author more influential than other scholars, and no one exert a significant influence on the evolution of health economics research. The evolution of health economics theory was influenced by all the authors discussed in this paper.

In terms of burstiness, there are 35 cited authors with citation burst between 9.26 and 3.90. It means that their papers were intensively cited during a specific period. The top-10 cited authors by bursts is Drummond M. 1988 (bursts of 9.26, period 1995–1999), Maynard A, 1982 (8.60, 1998–2003), WHO 2009 (8.09, 2009–2015), OECD 2013 (7.77, 2013–2022), Williams A. 1982 (7.63, 1986–2003), Johannesson M. 1996 (7.59, 1996–2003), Kahneman D. 2000 (7.55, 2016–2022), WHO 1993 (7.02, 2011–2022), Cutler D.M. 2007 (6.97, 2012–2016) and Donaldson C. 1995 (6.94, 1995–2003). Even if they are not included in the previous ranking, the following cited authors should be mentioned because their burstiness periods exceeds 10 years: Fuchs V.R. 21 years (bursts of 4.54, period 1977–1998), Williams A. 17 years (7.63, 1986–2003), Mooney G. 14 years (5.29, 1995–2009), Dolan P. 14 years (4.84, 2003–2017) and Weinstein M.C. 13 years (4.14, 1999–2011).

The same way as previous maps, the cited journal visualization map for health economics research (Fig.  8 ) was created in CiteSpace, but this network has 1273 nodes (cited journals), 25,008 linkages, and a density of 0.0309. The cited journals with more than 38 citations are labelled in the network.

Journal co-citation network visualization for health economics research. Source: Authors

The top ten journals by citations in health economics are presented in Table 5 . The BMJ – British Medical Journal (381 citations) is the journal published by British Medical Association and the most prominent cited journal in health economics area. It is followed by the New England Journal of Medicine (306 citations) and The Lancet (257 citations). The journal published by American Medicinal Association ranks on the fourth place. A journal that receives a lot of citations and has a high citation burstiness score has garnered the interest of academics recently.

The citation surge affects 70 cited journals. The cited journal with the strongest citation bursts is Plos One (21.79, 2014–2022), which is not the most cited one. It is followed by British Medical Journal (20.22, 1982–2006), Value Health (13.15, 2018–2022), BMJ Open (12.48, 2017–2022), Applied Health Economics and Health Policy (10.38, 2017–2022), BMC Health Services Research (10.16, 2019–2022), Frontiers in Public Health (9.99, 2020–2022), Cost Effectiveness and Resource Allocation (9.66, 1998–2005), JAMA Internal Medicine (9.24, 2019–2022) and BMC Public Health (8.71, 2016–2022). It should be noted that 8 cited journals of the ranking are bursting to the present. British Medical Journal (24 years), American Journal of Psychiatry (15 years), The Journal of Health Services Research and Policy (14 years), The New England Journal of Medicine (13 years) and Medical Care (12 years) are the cited journals with the longest periods of bursting, even if the interest in these journals is currently low. It must be added that four of the most cited journals in health economics research are on a top-10 list of journals with the highest JIF in 2021. All these journals are one of the most influential journals in health research.

Co-occurrence analysis

In this section of the analysis, we can pinpoint the key ideas and areas of interest in health economics research. To discover the primary study subjects in many scientific research domains, keywords are generally regarded as one of the most crucial elements of any research paper [ 44 ]. Co-occurrence analysis is used to identify the conceptual structure of the field. Without any pruning, the network of related keywords is shown in Fig.  9 . The network of co-occurred keyword has 694 nodes (keywords), 2823 links (connections), and a density of 0.0117. One percent of all keywords, those with a frequency greater than or equal to five, are labelled.

Keywords co-occurrence network for health economics research. Source: Authors

Table 6 presents the top 30 keywords which are used and connected in the 1620 analysed papers. “Health economics” and “cost effectiveness” are the most co-occurred items in health economics research, they have been connected for 121 times. “Care” follows them as the second high-count keyword with a frequency of 115. One crucial statistic used in the analysis of the keyword co-occurrence network is centrality. Centrality shows a keyword's strength, influence, or other specific characteristics. In this analysis all the keywords have a null betweenness centrality.

By using bursts detection, we tried to identify research hotspots in health economics. Surprisingly, there are only two keywords with citation bursts during 1975–2022: “behavioural economics” and “economic evaluation”. The keyword with the strongest bursts is “behavioural economics” (5.57) and it caught scholars’ attention between 2019 and 2022. The second keyword by citation bursts is “economic evaluation” (4.62). This item is bursting from 2020 to 2022. It can be observed that both research themes have short periods of bursts, and they continue bursting to present.

CiteSpace allows a cluster analysis of keywords to identify topics that have captured the attention of researchers. By applying clustering tool, the keywords network has been divided in 14 clusters, labelled by keywords. Table 7 presents the top 10 keywords clusters, in descending order of their size, and the most used keywords in the analysed sample of publications. There are 14 clusters with different sizes, from 80 research topics in health economics to 4 research topics. Their Silhouette values varies from 0.757 to 0.995 which means that keywords match well to their own cluster. Figure  10 show that the clustering configuration is appropriate.

Keywords clusters. Source: Authors

The largest cluster (#0) is labelled “Health economics” and has 80 components. It contains publications about health economics, cost effectiveness, quality of life, and management. Cost effectiveness analysis and health technology assessment are subjects in the second largest cluster (#1). It is labelled “Value framework” and has 78 topics. The third cluster (#2) “Economic evaluation” contains 75 topics and the most important are care, economic evaluation, outcome, and benefits. Other research topics refer to behavioural economics, demand, cost, quality of life, risk, cancer, public heath, financial incentives, therapy, etc.

The evolution over time of the keywords can be seen in Fig.  11 , structured by cluster. CiteSpace restricts the time pane analyses to the period 1990 – 2022. Figures  11 and 12 present how interest of researchers in health economics has evolved over time. In Fig.  12 are labelled the keywords with a frequency larger than 10. In the 1990s the hot topics of research in health economics were “care”, “impact”, “health economics”, “cost”, “cost effectiveness”, “quality of life”, “outcome”, “economic evaluation”. The most debated research topics in the 2000s were “children”, “air pollution”, “patient”, “management”, “people”, “public health”, “choice”, “therapy and “risk”. In the 2010s focus is on “behavioural economics”, “population”, “obesity”, “uncertainty”, “ technology”, “health policy”, “health system”. How future research in health economics looks? It cannot be estimated with certainty, but some directions are drawn as follows: “inequality”, “care expenditure”, “health technologies”, “analysis plan”, “adaptative design”, “transparency”, “biodiversity”. These topics may shape the future literature in health economics.

Timeline view of keywords clusters in health economics between 1990 and 2022. Source: Authors

Time zone view of keywords clusters in health economics between 1990 and 2022. Source: Authors

The performed literature analysis enables us to respond to the research queries that were addressed in the paper's introduction, as follows:

How scientific production has evolved in health economics?

It can be observed a general upward trend of health economics publications, but with numerous upward and downward fluctuations, generating sinusoidal cycles with an average duration of 3–4 years. The period 1975 – 1986 is characterized by a very low number of publications. The next two decades (1987 – 2006) are characterized by a slightly increasing trend in the number of publications, with an annual average of approximately 23 publications on health economics. The following period, 2007 – 2022 is characterized by an upward evolution of the number of health economics publications, 1068 publications with an annual average of 67 articles. The evolution of the citations’ number indicates the growing interest of specialists in researching the field, especially after 2000 when a constant and galloping annual increase in citations begins. The last 5 years show a very high interest of researchers and academics in health economics research, which is justified by the existence of worldwide Covid pandemic period.

Who are the most important authors and publications in health economics?

In our study, 4096 different authors were identified, and they individually published between one and 16 papers. Among the most important authors in health economics are Drummond M.F., Jonsson B., Coast J., Donaldson C. and Edwards R.T. Regarding the publication’s titles, 847 distinct journals published all 1620 documents related to health economics. Value Health, Health Economics, British Medical Journal, Pharmacoeconomics and Health Policy are among journals with high interest in health economics publications.

What are the geographical and institutional hubs of knowledge production in health economics?

The analysed publications involved the work of authors from 82 countries. The states with significant contributions in the field of health economics are the USA, England, Canada, Australia, and Netherlands. From the point of view of affiliation, the authors belong to 1723 institutions. The institutions with a high number of publications about health economics are University of London, University of California System, University of York, Harvard University and University of Birmingham.

What kind of collaboration between authors, organizations, and nations are there in the field of health economics research?

There are not strong collaboration relationships between authors. They are divided in small research groups and cooperation for research in health economics is insignificant. The most collaborative authors are Drummond M., Mooney G., Trosch R., Marchese D., and Fuchs V. There are two research teams created around Drummond M. and Mooney G., on the one hand, and around Trosch R. and Marchese D., on the other hand. Cooperation among institutions is depending on cooperation among authors. It is understood that poor collaboration at the individual level is followed by an identical one at the organizational level. The most collaborative institutions in health economics area are University of York, University of Oxford, University of Pennsylvania, University of Washington, and University of Birmingham. Regarding collaboration between countries, the USA and England played a key role in worldwide academic exchanges in health economics area, followed by Canada, Australia, and Netherlands.

Which are the most cited authors and the most cited papers, and which are the most attractive journals for publishing research results in health economics?

The most influential paper is published by Arrow K.J. in 1963, entitled “Uncertainty and the Welfare Economics of Medical Care”. The second most influential paper belongs to an anonymous author who wrote in 1996 a paper about cost effectiveness. We assume that is a book written by Gold M.R., Siegel J.E., Russell L.B. and Weinstein M.C., entitled “Cost-Effectiveness in Health and Medicine”. The third and the fourth most cited publications are signed by Drummond M.F. and his co-authors. In fact, it is about a book entitled “Methods for the Economic Evaluation of Health Care Programmes”, first published in 1987 at and then renewed in several editions. Another influential book was written by Kahneman D., entitled “Thinking, Fast and Slow” and published in 2011. The most cited author is Margolis H., who published in 1982 a book about “Selfishness, Altruism, and Rationality”. Drummond M.F. is on the second position with the publications about “Essentials in Health Economics”. Williams A. is the third cited author, followed by Culyer A.J and Arrow K.J. It should be noted that World Health Organization’s (WHO) publications are ones of the most cited document in health economics research. The most cited journals in health economics are The BMJ – British Medical Journal, The New England Journal of Medicine, The Lancet, Journal of American Medicinal Association and Health Economics. Beside them, other very influential journals are Plos One, Value Health, BMJ Open, Applied Health Economics and Health Policy and BMC Health Services Research.

What are the most debated conceptual approaches in health economics?

“Health economics”, “cost effectiveness” and “care” are the most debated concepts in health economics. But the current research hotspots in health economics are “behavioural economics” and “economic evaluation”.

Discussions and conclusions

The current bibliographic analysis was done for a specialized literature: health economics. This analysis contributes to the evaluation of the progress of the global knowledge in health economics and to the evaluation of the interest in health economics research. Moreover, the research allows the identification of the authors who contributed to the theoretical conceptualization of health economics, but also the identification of the most cited works in the field. A bibliometric analysis of the health economics research topic was produced, based on 1620 papers that were published between 1975 and 2021 and indexed in WoS. According to the tables and figures above, we have identified the important authors, publications, nations, organizations, keywords, and references.

By giving information on the current state of the art and identifying trends and research possibilities through the selection and analysis of the most pertinent publications published in the subject of health economics, the current study completes the body of existing research.

Through an extensive field mapping, the study increases the added value for the study of health economics theory. The development patterns of health economics are described by identifying trends in research production in that field and the most productive nations. The identification of top contributors’ points to possible collaborators (universities and researchers) for additional research projects. Finding the most appealing source names reveals publishing prospects for health economics-related articles. Leading thematic areas and developing research areas can be found to help academics identify research gaps in health economics.

Limitations and future research directions

Even though the bibliometric analysis and mapping visualization on articles relevant to health economics in the current research have produced numerous fascinating results, this methodology has several drawbacks. These limitations are due to the bibliometric analysis and quality of database. A quantitative analysis reduces the influence of subjective judgments. In several parts of the analysis, we were forces to use manual search because of inadequate or incomplete data. Maybe, manual analysis is required to learn additional specifics about different aspects of health economics theory by using a systematic review analysis.

The following limitations of the current study should be considered. First, the search strategy leads to a lost in publications which do not contain the query word in the publication title. Therefore, the main findings should be interpreted in accordance with the selection strategy used in this paper. The dataset is downloaded only from WoS, maybe multi-source searching is more convincing. Publications in other languages were not analysed. For some publications the name of author was missing. Some journals change their title in time, and they appear twice as being different journals. In this analysis it was used an inhomogeneous sample due to the type of publications.

Therefore, these restrictions remain issues that need to be resolved in additional research. To sum up, our analysis cannot cover every crucial publication concerning health economics, but we believe that the results allow us to have reliable insight into the knowledge domain. This study could be carried out in the future utilizing new search criteria, time periods, or bibliometric analytic parameters.

Availability of data and materials

The data can be extracted from Web of Science. All data are available upon application.

Abbreviations

European Union

Gross Domestic Product

Journal Impact Factor

Organisation for Economic Co-operation and Development

Science Citation Index Expanded

Social Science Citation Index

The United Kingdom

The United States of America

World Health Organization

Web of Science

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Barbu, L. Global trends in the scientific research of the health economics: a bibliometric analysis from 1975 to 2022. Health Econ Rev 13 , 31 (2023). https://doi.org/10.1186/s13561-023-00446-7

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El-Dahiyat, F., Obaid, D., El Refae, G. (2023). Health Economics. In: Encyclopedia of Evidence in Pharmaceutical Public Health and Health Services Research in Pharmacy. Springer, Cham. https://doi.org/10.1007/978-3-030-50247-8_8-1

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Health and economic scarcity: Measuring scarcity through consumption, income and home ownership indicators in Norway

It is widely recognised that income alone may not accurately reflect people's economic circumstances. In recent years, there has been increasing focus on multidimensional measures of economic scarcity. This study employs the newest survey data from Consumption Research Norway to explore the relationship between economic scarcity and self-reported health (SRH) in Norway. It defines economic scarcity by identifying disadvantaged social groups in terms of consumption, income and wealth/homeownership. Using propensity score matching, we compare health outcomes for economically disadvantaged and advantaged social groups – finding that consumption measures of scarcity are significantly associated with health, while there is no significant relationship between health and homeownership. When using matching estimators, health scores differ significantly between people with higher and lower incomes, but the associations are weakened when other socioeconomic variables are controlled for. This study applies empirical evidence from Norway to the existing health literature and contributes to a relatively new analytical approach by incorporating consumption into the prediction of health outcomes.

• • Consumption measures of scarcity have a significant association with health.
• • There is no significant relationship between health and homeownership.
• • Health scores differ significantly between people with higher and lower income when using propensity score matching estimators.
• • But the associations are weakened when controlling for other socioeconomic variables.

1. Introduction

In recent years, consumption-based indicators have become increasingly central to debates on poverty, life quality and health. Consumption has been recognised as a better measure of a household's economic situation than income, especially for households with few resources ( Attanasio & Pistaferri, 2016 ; Deaton, 1992 ; Meyer & Sullivan, 2003 ). One advantage of using consumption is that it not only captures the objective aspect of the economic condition, but also involves a social and comparative component, which accounts for deprivation that is disproportionate to resource. In other words, it reflects the health outcomes for those who have lower levels of resources than the majority of society ( Townsend, 1979 ). Therefore, by focusing on comparative levels of living standards, studies using consumption-based indicators can incorporate objective economic situations with subjective wellbeing under certain cultural and structural circumstances ( Townsend, 1979 ).

Conceptualising the term ‘economic scarcity’, we aim to capture broader aspects of socioeconomic marginalisation in this paper. We combine objective economic conditions, such as income and wealth, with consumption to reflect on relative deprivation and socially defined poverty. Using the 2019 Norwegian Deprivation Survey, this research explores whether economic scarcity, measured by consumption, income and wealth, is associated with health in Norway. It also compares the coefficient sizes of the three measures, attempting to determine which of the economic components are more important for the health of the economically disadvantaged: income, wealth or consumption .

This study contributes to the existing literature on the relationship between economic conditions and health by including consumption in the analysis of health, alongside traditional measures of income and wealth. By comparing different measures of scarcity, the research provides a more comprehensive picture of the economically disadvantaged and their health in Norway.

2. Theories

2.1. economic scarcity.

Economists have defined scarcity as a long-term shortage of natural resources, which occurs when a need is not satisfied ( Christiansen, 1998 ; Norgaard, 1990 ). Economists often examine scarcity based on supply and demand analyses, emphasising individual rational choices. The term is much used in studies of economic growth, technological substitutions and labour and capital changes for extracting resources ( Barbier, 2013 ; Howe, 1979 ).

A broader sociological definition of scarcity may embrace multidimensional understandings of economic hardship –incorporating objective economic resources of income and wealth with available consumption resources in certain customs and social activities. By looking at income and wealth, scarcity may be examined among those at the minimum income level adequate for living or those lacking sufficient purchasing power for their daily needs. By focusing on social custom and activities, we can capture a wider range of social groups that are, not only economically disadvantaged, but also socially excluded.

This study defines economic scarcity as the perception that one has fewer economic resources than required, as based on the majority valuation in society. Both this perception and this valuation focus on those who ‘lack the resources to obtain the types of diet, participate in the activities, and have the living conditions and amenities which are customary, or at least widely encouraged or approved, in the societies to which they belong’ ( Townsend, 1979 , p. 31). They amplify a person's resource scarcity in comparison to a broader notion of living style and convey consumption types that express normative deprivation.

2.2. Scarcity and health

Research on economic scarcity and health has been dominated by three approaches, all of which incorporate socioeconomic status into the analysis. The neo-material approach stresses the impact of material access and economic resources on health and often measures an individual's objective economic position by education, occupation, absolute income and wealth ( Gravelle, 1998 ; Lynch et al., 2004 ; Rodgers, 2002 ). The social psychological approach underlines that people's subjective economic status plays an important role in health ( Lundberg, Åberg Yngwe, Kölegård Stjärne, Björk, & Fritzell, 2008 ; Subramanian & Kawachi, 2004 ; Wilkinson & Pickett, 2017 ). Such studies often use measures of relative income or inequality to analyse health. Accordingly, unequal societies face problems, such as low social status, poor social cohesion and harmful health behaviours ( Marmot & Wilkinson, 2001 ; Pickett & Wilkinson, 2015 ).

The relative deprivation approach can be distinguished from the first two by its focus on relatively disadvantaged social groups. Embodying elements of both income and expenditure, consumption is central for these studies. Scholars research affordability within various types of custom and social activities and often use deprivation indexes based on customary amenities and activities, selected according to lifestyles and social contexts (see, e.g., Saunders, Naidoo, & Griffiths, 2008 ; Townsend, 1979 ). Consumption requires an objective, material basis. This is especially important for a person's health when considering life necessities (food, clothes, living, etc.) and living conditions ( Case & Deaton, 2003 ; Meyer & Sullivan, 2003 ). Insufficient resources can produce a certain pattern of thoughts, behaviours and priorities called the ‘scarcity mindset’. It may affect decision-making processes by placing increased attention on the scarce resource but, at the same time, neglecting other information and reducing mental bandwidth ( Mullainathan & Shafir, 2013 ). Relative deprivation may also lead to mental illness due to feelings of shame and stress ( Chase & Walker, 2015 ; Gubrium, 2015 ; Wilkinson, 2006 ). For example, the emerging literature on poverty and shame has demonstrated a vicious circle of lowered self-esteem, reduced capacity and fewer possibilities to change one's economic situation ( Gubrium, 2015 ). Being deprived can lead to social exclusion and a sense of shame. It may also cause mental health problems. In turn, mental illness reduces a person's capability to participate in the labour market and social life, which reinforces material deprivation and social exclusion.

All three perspectives have a common denominator of resources, ‘by which people can control and consciously direct the conditions of life, and as such they are all likely to be of vital importance to health’ ( Lundberg et al., 2008 , p. 74). As it embeds deprivation, low levels of income/wealth and the inability to participate in consumer society, economic scarcity is, therefore, an important factor in people's health outcomes.

3. Previous literature and research hypotheses

3.1. income and health hypothesis.

In health inequality studies, income is often used to indicate social position and stratification. Income affects economic position and material conditions, which, combined, contribute to exclusion and poor health ( Lundberg et al., 2008 ; Subramanian & Kawachi, 2006 ). This relationship between income and health has been widely confirmed. Researchers have long noted a positive correlation between income and both physical and psychological health ( Feinstein, 1993 ; Kessler & Neighbors, 1986 ; Lynch et al., 2004 ; Marmot, 2002 ). Even for more egalitarian countries with higher degrees of income redistribution, mortality still declines rapidly among those with high income ( Kinge et al., 2019 ; Mortensen et al., 2016 ).

Furthermore, the socioeconomic context can explain both mortality and economic inequality ( Elstad, 2011 ; Lundberg et al., 2008 ; O’Donnell, van Doorslaer, & van Ourti, 2013 ). Poorer health is associated with living alone, being unemployed, being a single parent, having a disability, experiencing reduced labour market participation and receiving financial support (social assistance) ( With, 2017 ). Studies also debate whether there is a causal relationship between income and health. For example, Mackenbach and de Jong (2018) discuss the possibilities of reverse causation and confounding variables when examining the relationship between income and health. They suggest that assessing causal effect requires both experimental and quasi-experimental studies.

Low income is, in itself, a risk factor for developing mental health problems. It reduces people's chances of labour market participation and limits their ability to maintain social contacts ( Langeland, Furuberg, & Lima, 2017 ). People with low income are also excluded from various social arenas because they cannot afford to participate in social activities, which further impairs their psychological wellbeing. This may aggravate health problems, and individuals can become trapped in a vulnerable situation.

Therefore, we expect lower income to be negatively associated with health: H1. Economic scarcity, measured by low income, correlates with health problems in Norway.

3.2. Wealth and health hypothesis

Wealth often has a stronger impact on health than low income because people can use wealth as a buffer when losing income. Researchers argue that health studies should include wealth as an important indicator for socioeconomic position ( Pollack et al., 2007 ). However, wealth is often difficult to measure because it is hard to assess individuals' or households' total financial resources over their lifetimes ( Pollack et al., 2007 ). Nevertheless, in wealth studies, the concept of wealth based on assets or property has become increasingly central ( Doling & Ronald, 2010 ; Mathä, Porpiglia, & Ziegelmeyer, 2017 ; O'mahony & Overton, 2015 ). Homeownership, reflecting material living standards and cumulative household wealth, is often used as an indicator of socioeconomic circumstances and has been recognised as one of the most important forms of family wealth ( Kurz and Blossfeld, 2004 ; Shapiro, 2006 ; Öst, 2012 ). In the US, homeownership has become the most important contributor to household wealth ( Eggleston & Munk, 2015 ). Non-ownership strongly correlates with economic marginalisation; few renting households can afford homeownership without falling into poverty ( Bourassa, 1996 ; Dewilde & Raeymaeckers, 2008 ).

Homeownership increases intergenerational wealth transfer and becomes more important for people's life satisfaction and health ( Elsinga, 2008 ; Hohm, 1983 ; Mathä et al., 2017 ; Nettleton & Burrows, 1998 ). Renters in Finland have been shown to have higher mortality than owners, after controlling for income, occupation and education ( Laaksonen, Martikainen, Nihtilä, Rahkonen, & Lahelma, 2008 ). In the UK, housing tenure significantly relates to self-reported health (SRH), general health status, anxiety, depression and limited longstanding illness ( Ellaway, Macdonald, & Kearns, 2016 ; Munford, Fichera, & Sutton, 2017 ). This is because homeownership can give people a sense of control, autonomy and physical and emotional security ( Chapman, 2013 ; Elsinga, 2008 ). It also allows people to improve their housing conditions, such as temperature and humidity, which are health-related.

However, people living with economic hardship often cannot afford to own their homes. While the market value of housing wealth represents about two-thirds of Norwegian household financial wealth ( Grindaker, 2018 ; Statistics Norway, 2017 ), less than half of low-income households in Norway own their own homes, and the proportion of homeowners among low-income groups and welfare recipients has declined in recent years ( Revold, 2019 ).

This leads to our second hypothesis: H2. Economic scarcity of wealth, measured by non-ownership, is associated with health problems in Norway.

3.3. Consumption and health hypothesis

Levels of expenditure and consumption do not always reflect a person's level of income or wealth. According to the Deaton Paradox, a reduction in income does not cause a corresponding reduction in consumption ( Deaton, 1992 ). Consumption involves several dimensions. First, it has a material aspect . The economic situation is related to the level of utility and consumption of commodities. Consumption takes expenditure into account, and considers the cost to a household of reaching a certain level of utility at prevailing prices ( Kus, Nolan, & Whelan, 2016 ; Ravallion, 1998 ). This is particularly relevant when studying the most economically disadvantaged groups, where material deprivation often has a direct, negative effect on health ( Ravallion, 2016 ).

Second, consumption has social aspects , which are manifested in cultural values, norms and inclusion/exclusion ( Croghan, Griffin, Hunter, & Phoenix, 2006 ). Research shows that children and adolescents in low-income families participate less in important social events and arenas, such as kindergarten, before- and after-school programmes and leisure activities ( Fløtten, Hansen, Grødem, Grønningsæter, & Nielse, 2011 ). In this way, consumption may be particularly important for individuals’ psychological health.

Third, people may increase their indebtedness to maintain or improve their standards of living. Many who struggle with their financial situation must borrow to pay for their daily expenses ( Kempson & Poppe, 2018 ). International research has linked insolvency problems to physical disability and chronic health conditions, obesity and health-related behaviours, such as smoking and drinking ( Clayton, Liñares-Zegarra, & Wilson, 2015 ; Drentea & Lavrakas, 2000 ). In Sweden, scholars have observed a strong connection between insolvency and mental health problems, such as depression, anxiety and general mental illness ( Holmgren, Sundström, Levinsson, & Ahlström, 2019 ). In both Sweden and Finland, the rate of suicide attempts among the over-indebted is more than five times higher than that in the overall population ( Ahlström, Edström, & Savemark, 2014 ; Hintikka et al., 1998 ).

Therefore, the consumption-related aspects of economic scarcity might negatively influence health. This leads to the third hypothesis: H3. Economic scarcity, measured by consumption indicators of deprivation, exclusion and insolvency problems, is strongly and negatively correlated with health.

3.4. Comparing income, wealth and consumption

Although the relationship between income, wealth and health is fairly solid (see, e.g., Deaton, 2008 ; Easterlin, Angelescu McVey, Switek, Sawangfa, & Zweig, 2011 ), scholars have shown that the effects of income on illness become non-significant in people with severe economic problems, such as those who are over-indebted ( Drentea & Reynolds, 2012 ). Furthermore, disposable income does not differ between those in debt and those not in debt, and psychological factors are more important determinants of economic vulnerability ( Livingstone & Lunt, 1992 ). Similarly, when exploring the reasons for mental disorders among poor people, the correlation between lower income and mental illnesses is often mediated by consumer debt ( Jenkins et al., 2008 ).

Consumption-based economic hardship may have a stronger association with health than do income and wealth. First, it is irrelevant to examine income levels for the poorest, especially when considering the unemployed. Second, income and wealth may play a less important role in health among countries that provide more generous welfare support, such as minimum wages, unemployment benefits and universal healthcare coverage. Third, being excluded may be more strongly associated with psychological health. One example concerns the custom of drinking tea in Britain ( Townsend, 1979 ). Tea has little nutritional value but is psychologically necessary in Britain due to social customs. It contributes to a person's recognition and maintenance of social relationships. Therefore, it is important to separate physical needs from social and psychological needs. This leads to our fourth hypothesis: H4. The coefficient size of consumption on health is larger than that of income and homeownership.

4. Data and methods

4.1. data and variable.

This study uses the latest Deprivation Survey from Consumption Research Norway. The data were collected in July 2019, and respondents were selected from the Kantar Gallup Panel, which consists of individuals over 15 years of age, randomly recruited from the Norwegian population. Invitations to participate in the survey were sent to participants via an e-mail containing a link to the online survey. 2312 individuals responded to the survey, representing a 48% response rate. The dropout rate did not differ significantly for those among other Gallup surveys, which have an average dropout rate of 50%.

The survey consisted of questions that map consumption-related deprivation, household insolvency problems, SRH and information about particular life-events in the household, such as loss of a partner, divorce, sudden illness, etc. Households' and individuals’ socioeconomic backgrounds were also included and were based on the panel information from Gallup. The sample for data analysis was made up of 2045 individuals aged 18–89.

The dependent variable in this study is self-reported health (SRH). SRH is one of the most widely used indicators for measuring health. Although it may not be suitable for comparative studies of aggregate health between countries, it is still valid for a within-country comparison ( Haddock et al., 2006 ; Kuhn, Rahman, & Menken, 2006 ; Subramanian, Huijts, & Avendano, 2010 ). Scholars have found that SRH strongly correlates with objective health status, and the prevalence of all diseases is associated with poorer SRH ( Franks, Gold, & Fiscella, 2003 ; Wu et al., 2013 ). Therefore, the reliability of SRH is as good as, or even better than, that of more specific health questions ( Lundberg & Manderbacka, 1996 ).

SRH is often rated on a five-point scale ranging from poor to excellent health ( Hays, Sherbourne, & Mazel, 1993 ). Some researchers have also measured health on a scale from 0 to 100 ( Gholami, Jahromi, Zarei, & Dehghan, 2013 ; Meng, Xie, & Zhang, 2014 ) or from 0 to 10 ( Vlot-van Anrooij, Tobi, Hilgenkamp, Leusink, & Naaldenberg, 2018 ). In the Deprivation Survey, respondents were asked: ‘How would you rate your health today?’ The scale ranged from 0 to 10, where 0 denoted the poorest health and 10 denoted the best health.

The main intervention or treatment in this study is economic scarcity, measured by 1) consumption, 2) income and 3) homeownership.

To measure economic scarcity based on consumption, three indicators were developed: material deprivation, social exclusion and insolvency problems. The conceptualisations of material deprivation and social exclusion were adapted from Wong, Saunders, Ping Wong, Chan, and Chua (2012) , who defines deprivation and exclusion by mapping items of consumption. A list of 24 material items and 16 social activities was drawn up based on the Norwegian Reference Budget for Consumer Expenditures. Three follow-up questions were asked: 1) ‘Is the following item/activity essential for everyone in Norway?‘; 2) ‘Do you have/do it?‘; and 3) ‘If not, is this because you cannot afford it?’

If less than 50% of the respondents regarded an item as essential, the item is considered unimportant and was excluded from further operations. Respondents were defined as being relatively deprived if they could not afford two or more material items and relatively excluded if they did not have access to two or more social activities because they could afford them. See Bakkeli and Borgeraas (2019) and Appendix 1 for detailed information about items included in the survey and about the criteria used to identify the deprivation/exclusion thresholds. Insolvency problems were identified by the question: ‘In the past 12 months, how often did your household have trouble paying rent or your mortgage on the final due date?’ Those who chose the options ‘always’, ‘often’ or ‘sometimes’ were defined as having insolvency problems.

The survey contained information about both households' and individuals' gross income . The income variables were drawn from Gallup's background variables, which were reported by the respondents and recorded in Norwegian kroner (NOK) in discrete income intervals. To make the indicators comparable, the researchers also used dummy variables for income. The treatment group, defined as relatively poor, had a household income below NOK 400,000 (or an individual income below NOK 300,000 for a robustness check) and made up approximately 10% of the sample.

Homeownership , which was also used to approximate wealth, distinguished between owners and renters based on ownership of their current homes. The treatment group comprised those who rented their homes.

Other covariates included age (18–84), gender (female = 1), household size (1–5), education , work situation , and type of household . See Table 1 for descriptive statistics.

Descriptive statistics.

4.2. Methods

The study used propensity score matching methods to estimate the association between economic scarcity (the intervention or treatment) and SRH. In the social sciences, it is generally impossible to randomly assign units to the treatment condition or the control condition. Such data may suffer from selection bias, since people who receive the treatment may have different characteristics from those in the control condition ( Morgan & Winship, 2007 ).

In our case, the sample of economically disadvantaged and non-disadvantaged people differed, not only with respect to economic resources, but also with respect to other circumstances that could influence health. The treatment and control groups were, therefore, imbalanced; they were differently composed according to their economic situations and various other relevant characteristics.

By employing the matching method, we reduce this imbalance by constructing a matched control sample corresponding as closely as possible to the sample of economically disadvantaged people with respect to all relevant covariates. Ideally, this would result in two very similar samples, with the only difference being that people in the treatment group were economically disadvantaged, while those in the control group were not. Therefore, matching can to a large degree eliminate the confounding effect ( Morgan & Winship, 2007 ; Rosenbaum & Rubin, 1985 ). However, the problem of unobserved heterogeneity is universal to propensity scores, and there was uncertainty about the level at which the selection bias was eliminated from the estimation of the treatment effect. As it ensured equal distribution of the measured variables for the control and treatment groups, the matching method did not capture all unmeasured confounders. Therefore, although the method is useful for observational data, we need to be cautious when making a causal conclusion ( Elstad & Pedersen, 2012 ; Morgan & Winship, 2007 ; Rubin, 2001 ).

The analytical procedure was rather straightforward. First, we selected covariates to estimate propensity scores for the treatment variables. Five different samples were constructed based on the five indicators/treatments: 1) deprivation, 2) exclusion, 3) insolvency, 4) relatively poor and 5) homeownership.

The propensity score was also the probability of treatment assignment, conditional on observed covariates: e i = Pr ( D i = 1 | X i ) . To estimate propensity scores, logit models were used:

where D i represented the treatment variables, and each of the treatments was predicted by a different set of k covariates, X 1 , … X k .

Next, we assessed the common support for the propensity scores, carefully tested the balance of the covariate distribution and examined selection bias by approaching standardised biases and bias reduction. Standardised differences were assessed by calculating the mean difference in the covariate between the treatment conditions:

where m ‾ t and m ‾ c were the sample means of covariates for all cases in the treatment and control groups, respectively, and s t and s t were the standard deviations (SDs) for the treatment and control groups, respectively.

Each of the five indicators had a separate, matching sample constructed by a different set of covariates. By examining the balance for each variable included in the matching samples, that the control and treatment groups were equally distributed across all the measured matching variables. Balance was achieved by performing t-tests to compare the groups and determine whether, and to what extent, biases were reduced. For each of the covariates selected to construct the matching samples, we ensure that the means of the treatment and control groups did not differ significantly and that the bias was less than 5%. The covariates included in each of the sample constructions are given in Appendix 2 ( Table A2 a).

Different estimation techniques for predicting the association between diverse indicators on self-reported health.

Note. Coefficients that are not statistically significant at the 0.05-level are marked in bold.

When the matched samples were proven to be balanced, we employed matching methods to estimate the average treatment effects among the treatment group. Nearest neighbour matching was used to match each case in treatment group i with a case in control group j based on the closest absolute distance between their propensity scores: d ( i , j ) = | l x i − l x j | . Other matching methods included Kernel, stratification and caliper matching. The caliper bandwidth for different treatments was estimated by b = . 25 × s p ( x ) , where s p ( x ) was the SD of the matching variable x . For all matching estimations, the standard errors were obtained by bootstrapping 1000 repetitions.

Additional robustness checks included the inverse probability of treatment weighting with regression adjustment (IPTWRA) combined with Wooldridge's double-robust estimators. This was done to estimate the average treatment effect across the treatment group. We also checked estimations by combining a regression model with weighting using the propensity scores.

Appendix 2 shows the sample distribution of propensity scores before and after matching. For each of the five indicators, overlapping between the treatment and control groups is evident after matching ( Fig. A.2a ). When looking at the standardised differences in key baseline characteristics for the unmatched and matched datasets, the biases are clearly reduced ( Fig. A.2b ). This was consistent with the statistics of Rubin's B, which measured the absolute standardised differences of the means for the linear index of the propensity score between the treated and matched control groups. Rubin (2001) suggests that a sample is sufficiently balanced when this value is lower than 0.25. For all five measures of economic scarcity in our study, the Rubin's B was below 0.25, indicating balanced differences in covariates ( Table A2 b).

Table 2 shows the results based on different matching methods. The columns represent different estimation techniques to predict the associations between the five indicators and health. For example, using nearest neighbour matching, the health score was reduced by 0.78 on a scale from 0 to 10 when comparing the deprived with the non-deprived. The coefficient size was a little larger when using Kernel matching (−0.99), stratification matching (−0.90) and IPTWRA estimation (−0.90), but the estimators did not vary much from each other. The estimations for exclusion varied from −0.72 to −0.84, indicating a trend similar to that found using deprivation measurement. For people with insolvency problems, the difference was particularly large; their health was more than one score lower than those who did not have insolvency problems.

Health differences between lower and higher income groups were also statistically significant. The health of people with relatively low income was 0.43 scores lower than that of higher income groups using nearest neighbour matching. When employing caliper matching, the predicted health differences between lower and higher income groups were notably larger than when using other estimation methods. In this case, SRH was one score lower for people with lower income.

It is worth noting that we have checked the robustness of the income indicator by using different cut-off points between the relatively low- and high-income groups. The same procedure was also performed using individual income instead of household income. The results were very similar, both in terms of significance levels and coefficient sizes (not shown).

Finally, using Kernel matching, stratification matching and IPTWRA, home renters were estimated to have about 0.44 points poorer health than homeowners on a scale from 0 to 10. However, when estimated using nearest neighbour matching and caliper matching, health did not differ significantly between owners and renters. Therefore, it cannot be concluded that there was a robust association between homeownership and health scores.

A more direct way of interpreting the result was to convert the estimations into percentage changes compared to average health. For example, when considering the deprived, the excluded, the insolvent, the poorer and home renters, their respective health scores were 10.72, 11.56, 18.81, 5.95 and 3.04% lower than average health, based on the nearest neighbour matching estimations. The bottom row in Table 2 shows z-scores for Wooldridge's double-robust estimators. These were calculated to make the estimators comparable across indicators. Coefficient sizes fell as follows: insolvency problems (-7.0), deprivation (-5.3), exclusion (-3.7), low income (-2.6) and non-owners (-2.1). Consumption-based indicators clearly had stronger associations with health than did income and ownership.

A common practice is to include socioeconomic background as a control variable when examining the relationship between health and scarcity on matched samples. We conducted ordinary least squares (OLS) regression with robust standard errors, using samples before and after propensity score matching ( Fig. 1 ). For detailed information, see Appendix 3 , Table A.3 . The size of the coefficient was smaller in matched samples, since matching captured selection bias and reduced the confounding effect. As expected, several variables became non-significant due to the reduced sample size after matching. However, even when controlled for numerous important socioeconomic background variables, the association between health and deprivation, exclusion and insolvency was still strong and significant (see Models 2, 4 and 6). They predicted .55, .55 and 1.05 lower health scores, respectively, when comparing the disadvantaged with the non-disadvantaged. The coefficient size of insolvency was almost twice as large (0.238 SD) as the sizes of deprivation (0.129 SD) and exclusion (0.131 SD).

OLS regression on self-reported health, before and after matching. Note. 1. Reference for education: primary and lower secondary school (10-year schooling).

2. Reference for work: full-time employed.

3. Reference for income: income ≥ NOK 1,400,000.

The relationship between lower income and homeownership on health were not statistically significant in Models 8 and 10. The results remained robust when using different cut-off points for household income or replacing household income with individual income (not shown). However, these insignificant results may have been due to the small sample size after matching.

In addition, health did not differ significantly among various income covariates, controlled for consumption indicators. In some pieces of extant literature, income is viewed as an intermediate variable between consumption and health (e.g., Lundberg et al., 2008 ). Therefore, to detect the coefficient size between consumption and health, we have also included income intervals as control variables in the weighted models – finding that the coefficients of economic scarcity did not change much with or without controls for income categories (see Appendix 4 , Table A.4 ). Again, this might have been due to a combination of the reduced sample size after propensity score matching, as well as numerous predicators.

Other important covariates, shown in Fig. 1 , were people who are unemployed or receive unemployment or other welfare benefits. These groups had significantly poorer health in all matched samples based on the five indicators.

6. Discussion

Using the latest Deprivation Survey from Norway, we estimated the correlation between economic scarcity and SRH based on five different indicators. We found a significant association between health and all consumption-based indicators, and this relationship was strongest when measuring economic scarcity using the consumption indicator of insolvency. This supported H3 and H4 . The income indicator was significantly associated with health (using most of the matching techniques), but the coefficients became insignificant when controlling for socioeconomic background. Homeownership also did not have a robust association with health scores.

This study has confirmed the importance of incorporating consumption into health studies. Consumption involves a substantial material aspect in terms of living conditions, as well as a social component involving customs, norms and participation. Consumption also contains a more direct component of subjective feelings, and causes feelings of shame, lower self-confidence, stress, anxiety and mental and physical health problems ( Gubrium, 2014 ; Hiilamo, 2018 ). While individual feelings correlate with psychological wellbeing, previous studies have also established a firm connection between health and social comparison, relative deprivation and shame ( Buunk, Gibbons, Buunk, Gibbons, & Buunk, 2013 ; Tennen, Mckee, & Affleck, 2000 ; Yngwe, Fritzell, Lundberg, Diderichsen, & Burström, 2003 ). Therefore, using consumption-based indicators, economic scarcity is shown to be negatively connected to health.

We chose to use SRH instead of pathological or clinical measures of health. When an individual reports his or her own health, subjective considerations play a more important role than, for example, medical certificates. It would be interesting to test the relationship between economic scarcity and health based on different health indicators in future studies. Furthermore, although SRH highly correlates with objective health measurements, an individual's self-evaluation of his or her health may also correlate with his or her social background. For example, with the same SRH, respondents with higher education have healthier levels of biomarkers than lower educated groups ( Dowd & Zajacova, 2010 ). Therefore, using subjective health measurements may underestimate health inequalities. However, in this research, the respondents were matched by educational attainment. By comparing people with similar education, matching potentially reduced such biases.

Moreover, the relationship between health and economic scarcity may be reciprocal ( Lundberg et al., 2008 ; Mullainathan & Shafir, 2013 ). Although we constructed samples that showed a good match between treatment and control groups, it is difficult to determine causal directions based on a cross-sectional dataset. Therefore, this analysis could only draw conclusions about certain correlations between health and economic scarcity but cannot conclude on causality. Scholars have already shown uncertainties connected to the causal relationship between health and socioeconomic positions. A range of biological, psychological and social factors might act as important mediators, moderators and/or confounders, which may play important roles in the association between health and socioeconomic factors ( Mackenbach, 2019 ; Mackenbach & de Jong, 2018 ).

The matching method only ensures that the control and treatment groups are equally distributed across the measured matching variables. However, it cannot remove all unobserved heterogeneities; there may still be important confounders that are not considered. In the present case, one example is medical history or poor socioeconomic conditions during childhood. In addition, both SRH and the consumption-based indicators were subjective. Therefore, people with low socioeconomic status may have also suffered from being influenced by inclinations to negatively assess a range of life circumstances. Such covariates could have affected both the matching variables and health. Future studies may look closer at the cause and effect of economic disadvantage in relation to health.

This research found a significant correlation between income and health, but the strength of the correlation weakened when performing OLS regression using the propensity score matching sample. This could have resulted from the matching having removed the confounding effect of income. However, as mentioned above, another reason might be the small sample size. In addition, low education and lack of work can also cause low income. When including education and employment as control variables, the effect of income may be suppressed. This added uncertainties to our attempt to draw conclusions about the non-significant relationship between income and health.

This study has also found that homeownership did not play a significant role in health, possibly because homeownership can capture regional and structural factors, such as unemployment rates, regional social policies and demographic aspects. These variables were not included in the survey. Future studies could, therefore, incorporate contextual variables when calculating propensity scores. Moreover, the significance of house tenure could be context-specific. A comparative study among ten European countries has found that homeownership was associated with better health in the UK and the Netherlands but not in other countries ( Dalstra, Kunst, Mackenbach, & EU Working Group on Socioeconomic Inequalities in Health, 2006 ). This suggests that the importance of housing must be considered more carefully in different countries with different housing policies. In Norway, the debt and mortgage burden is rising. The median house price-to-income ratio was 3.5 in Norway in 2014. In larger cities, the median house price is more than four times greater than income ( Anundsen & Mæhlum, 2017 ). About 57% of homeowners have a loan-to-value ratio above 85%, and one in five have debt more than five times greater than their income ( Anundsen & Mæhlum, 2017 ). Therefore, homeownership may no longer be a safety net but, rather, a risk factor for individuals and their families.

Although homeownership is one of the most important aspects of wealth, wealth is often more than just ownership. Unfortunately, we did not have access to other variables that could be used to construct a more comprehensive indicator for wealth. Such an indicator may be necessary for more in-depth studies of the wealth–health relationship.

7. Conclusion

This study has revealed notable health differences between people with and without consumption-based economic scarcity, measured by material deprivation, social exclusion and insolvency problems. The correlation was strong and distinguishable. In addition to income and wealth, scarcity in this study integrated consumption as a central element of economic disadvantage. At a relatively low economic level, people do not have access to a wide representation of consumer goods, are unable to fully participate in social activities and do not share the representative style of living others possess. The economically disadvantaged are excluded from common social spheres, which may negatively correlate with their health.

Appendix 1. Norwegian Deprivation Survey

The list of items related to material deprivation and social exclusion is presented in the table below:

Items related to deprivation and exclusion

The deprivation and exclusion thresholds, as defined by Bakkeli and Borgeraas (2019) , are based on percentage numbers of items people lack because they cannot afford them. The deprivation index is conditioned on the majority opinion of what is ‘essential’. If at least 50% of the respondents regarded an item as essential, the item was considered important. In this way, three material items and one social item were considered unimportant and were filtered from further analysis.

A relatively high percentage of people had access to all items, amounting to almost 70% of the population. Therefore, the vast majority of the population did not experience deprivation. However, about 30% could not afford one or more item, and 18.44% could not afford two or more items. Again, when considering items related to social activities, most people (approximately 70% of the population) had access to all social items. Furthermore, 32% of the population could not afford one or more item, and 18.73% could not afford two or more items.

The Deprivation Survey has defined the mean deprivation index based on the numbers of items lacking due to affordability, operating in the same fashion as Saunders and Wong's (2010) study. When plotting the deprivation index alongside income distribution, the crossover thresholds between the deprivation index and household income levels lay at around two items for both material deprivation and social exclusion. When material and social indicators were merged, the lowest income groups missed out completely on four items (see Figure A1 ). It is worth mentioning that Income Category 2 corresponds to a household income level of NOK 200,000–399,999. This category was selected because it contained the officially poverty line.

In this study, we adopted a threshold of two items because we tchose to separate material and social indicators.

Mean deprivation and exclusion indices by annual household income ( Bakkeli & Borgeraas, 2019 ).

Appendix 2. Constructing matching samples

Covariates used to construct matching samples

Reduction in standardised biases.

Distribution of propensity scores before and after matching for the five indicators.

Standardised biases before and after matching.

Appendix 3. OLS-regressions on self-reported health

OLS-regressions on self-reported health, before and after propensity score weighting

Note. *p < .05, **p < .01, ***p < .001.

Model 1 and 2: Samples before and after PSM weighting based on deprivation. Model 3 and 4: Samples before and after PSM weighting based on exclusion. Model 5 and 6: Samples before and after PSM weighting based on insolvency. Model 7 and 8: Samples before and after PSM weighting based on low income. Model 9 and 10: Samples before and after PSM weighting based on non-ownership.

Appendix 4. OLS-models without income controls

OLS-models without income controls, samples are weighted by propensity scores

Note. †p < .10, *p < .05, **p < .01, ***p < .001.

Appendix 5. Correlation matrix between indicators for economic scarcity

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Promoting Public Health with Blunt Instruments: Evidence from Vaccine Mandates

We study the effect of mandates requiring COVID-19 vaccination among healthcare industry workers adopted in 2021 in the United States. There are long-standing worker shortages in the U.S. healthcare industry, pre-dating the COVID-19 pandemic. The impact of COVID-19 vaccine mandates on shortages is ex ante ambiguous. If mandates increase perceived safety of the healthcare industry, marginal workers may be drawn to healthcare, relaxing shortages. On the other hand, if marginal workers are vaccine hesitant or averse, then mandates may push workers away from the industry and exacerbate shortages. We combine monthly data from the Current Population Survey 2021 to 2022 with difference-in-differences methods to study the effects of state vaccine mandates on the probability of working in healthcare, and of employment transitions into and out of the industry. Our findings suggest that vaccine mandates may have worsened healthcare workforce shortages: following adoption of a state-level mandate, the probability of working in the healthcare industry declines by 6%. Effects are larger among workers in healthcare-specific occupations, who leave the industry at higher rates in response to mandates and are slower to be replaced than workers in non-healthcare occupations. Findings suggest trade-offs faced by health policymakers seeking to achieve multiple health objectives.

Research reported in this publication was supported by the National Institute on Mental Health of the National Institutes of Health under Award Number 1R01MH132552 (PI: Johanna Catherine Maclean). John Earle also acknowledges support from the Russell Sage Foundation. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Institutes of Health or the National Bureau of Economic Research.

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Paper: policy reforms urgently needed to mitigate racial disparities in perinatal mental health conditions.

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Significant reforms in U.S. health care and economic policies are needed to mitigate the stark disparities in perinatal mental health diagnoses and treatment that place women of color at greater risk of mortality and morbidity, according to social work professor Karen M. Tabb, the senior author of a paper published in Health Affairs.

Photo by L. Brian Stauffer

CHAMPAIGN, Ill. — A team of researchers is calling for comprehensive changes to U.S. health care and social policies to improve diagnosis and treatment of perinatal mental health conditions and mitigate the dramatic disparities that put women of color at significantly greater risks of morbidity and mortality compared with white women.

In a commentary published in the journal Health Affairs, the researchers proposed seven comprehensive changes to health care and economic policies to mitigate the burden of undiagnosed and untreated perinatal mental health challenges that are greatest among racial minority populations.

Dr. Emily C. Dossett, a professor of psychiatry and of obstetrics in the Keck School of Medicine at the University of Southern California, was the first author of the group’s paper, and she was among the scholars who discussed their findings during Health Affairs’ virtual briefing on perinatal health.

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The researchers’ recommendations include a national training and certification program for health care providers; payment models that enable women to obtain services through community-based providers; paid family leave; expanded funding for perinatal psychiatry access programs; and access to safe, legal abortions and contraception. They also proposed poverty-mitigation strategies such as reinstating the federal child tax credit and implementing a universal basic income program.

The team said their recommendations are a call for reproductive justice – which includes rights to bodily autonomy, decisions to have or not have children and to live in safe, healthy environments.

During Health Affairs’ virtual briefing on April 3, University of Illinois Urbana-Champaign social work professor Karen M. Tabb Dina , the senior and corresponding author of the commentary, spoke about the urgent need for a comprehensive strategy to improve maternal health outcomes and promote equity.

“Perinatal mental health challenges are a microcosm for the U.S. health care system, bringing into focus gaps in equity, access, research data and social determinants of health,” said Tabb Dina, who is co-principal investigator on a grant-funded project that is examining the impact of racial bias and discrimination on women’s health care interactions during the perinatal period, defined as the time before and after giving birth.

While the team acknowledged that the reforms proposed are significant, they said that none of these are unattainable – “the challenges lie in who we value and how we choose to demonstrate that.”

“Broadening our understanding of what constitutes perinatal mental illness and wellness, and grounding our understanding in reproductive justice would lead to policies that close some of these gaps,” said first author Dr. Emily C. Dossett , a professor of psychiatry and the behavioral sciences and of obstetrics in the Keck School of Medicine at the University of Southern California. Dossett is also the medical director of CHAMP for Moms – Child Access to Mental Health and Psychiatry, a consultation and educational service for pediatric primary care providers based at the University of Mississippi Medical Center.

Their co-authors were Dr. Alison M. Stuebe , a professor of maternal and child health, and of obstetrics and gynecology at the University of North Carolina-Chapel Hill School of Medicine; and Twylla Dillion , the executive director of HealthConnect One, a Chicago-based nonprofit focused on training community birth workers and research.

A 2022 report by the U.S. Centers for Disease Control and Prevention indicated that mental health conditions – including suicide and overdoses associated with substance use – are the leading cause of pregnancy-related death. However, more than 80% of these deaths are preventable, the report said.

Current policy and research, which focus primarily on postpartum depression, should be expanded to include other mental health conditions that can predate conception and continue after labor and delivery or miscarriage, the team suggested. Likewise, research samples must include greater diversity in race and ethnicity, gender and sexual orientation, and non-English speaking individuals.

Women’s health care needs are often not prioritized as high as those of their infants and children by many well-funded maternal health programs such as home visits and family case managers, which tend to view the “baby as the candy and the mother as the wrapper,” Stuebe has said.

However, community- and patient-centered care, such as doulas and birth centers, has shown promise at improving maternal health outcomes. To begin scaling up these services, HealthConnect One and several other doula programs have partnered on the Doula Data + Compensation Consortium, a crowdsourced organization specifically designed to gather research data on the health outcomes associated with these services.

Community-based care may be more cost-effective, and alternative payment models such as bundled payments and capitation that prioritize value-based care over fee-for-service care would make services more accessible to women in need, the researchers proposed. Moreover, research has shown that community birth centers protect women of color against the discriminatory treatment and trauma they frequently experience in traditional clinical settings, the team said.

Additionally, they called for broader funding for perinatal psychiatry access programs that would enable nonspecialty providers to consult by phone with behavioral health clinicians for help diagnosing, treating and managing pregnant and postpartum women’s mental health care. The Health Resources and Services Administration is currently funding these programs in more than 20 states, and they have consistently demonstrated more equitable access to care and cost savings, the team wrote.

Accordingly, the team called for reinstatement of the 2021 federal child tax credit, which had striking effects on recipients’ mental health, particularly Black and Hispanic families. Almost 50% of the reduction in depressive symptoms and about 70% of the decrease in anxiety symptoms were associated with recipients’ improved capacity to afford food and housing.

Likewise, the team proposed implementing and evaluating a universal basic income program for perinatal families as research has found that these programs significantly improve recipients’ mental health. Cash-based, unconditional universal basic income programs that uncouple childbearing from the receipt of benefits also support recipients’ reproductive rights to decide to have or not have children, as well as parents’ rights to raise their families in safe, healthy environments, the researchers said.

Finally, the team advocated workplace policies that support families – specifically, paid parental leave and high-quality child care. Currently, four states offer paid family leave policies that allow parents up to 12 weeks off during the first year after birth or adoption. Preliminary data suggest these policies are associated with improvements in maternal mental health, while struggles with access to affordable child care negatively impact parents’ mental and physical well-being, the team said.

Editor’s note :    

To reach Karen Tabb Dina, email [email protected]

To reach Dr. Emily Dossett, [email protected]

The paper “Perinatal mental health: The need for broader understanding and policies that meet the challenges” is available online or from the News Bureau

DOI: 10.1377/hlthaff/2023.01455     

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ECON 3171 Causal Reasoning and Policy Evaluation I

Course description.

Course information provided by the Courses of Study 2023-2024 . Courses of Study 2024-2025 is scheduled to publish mid-June.

This course covers methods used by social scientists to identify causal relationships in data, with a focus on evaluating the effects of real-world policies. Many social science analyses--including in the economics fields of public, labor, health, and development-aim to answer these types of policy-related causal questions: What is the effect of having health insurance on someone's health? Does the death penalty reduce crime? Will lowering class sizes increase students' academic achievement? The goal of this course is to train you to become both a high-quality consumer and producer of this type of research. You will learn about several research designs and data analysis methods for identifying causal relationships in data, read and assess empirical papers that apply these methods, and apply these methods to datasets yourself.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: PUBPOL 3100 or equivalent.

Distribution Category (SBA-AS, SDS-AS, SSC-AS)

• Assess the strengths and limitations of different research designs for estimating causal effects.
• Read and assess the strengths and weaknesses of empirical research answering causal questions.
• Apply the research designs covered in the course to data-based examples.

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  Regular Academic Session.   Combined with: PUBPOL 4101

Credits and Grading Basis

3 Credits Stdnt Opt (Letter or S/U grades)

Class Number & Section Details

 5809 ECON 3171   LEC 001

Meeting Pattern

• TR 2:55pm - 4:10pm To Be Assigned
• Aug 26 - Dec 9, 2024

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Kapustin, M

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