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Valuation: Measuring and Managing the Value of Companies, 7th edition

At the crossroads of corporate strategy and finance lies valuation. This book enables everyone, from the budding professional to the seasoned manager, to excel at measuring and maximizing shareholder and company value.

John Wiley & Sons, 2020 | Tim Koller, Marc Goedhart, David Wessels

Valuation: Measuring and Managing the Value of Companies , celebrating 30 years in print, is now in its seventh edition (John Wiley & Sons, June 2020). Carefully revised and updated, this edition includes new insights on topics such as digital; environmental, social, and governance issues; and long-term investing, as well as fresh case studies.

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Tim Koller is a partner in McKinsey's Stamford, Connecticut, office, where he is a founder of McKinsey's Strategy and Corporate Finance Insights team, a global group of corporate-finance expert consultants. In his 35 years in consulting, Tim has served clients globally on corporate strategy and capital markets, mergers and acquisitions transactions, and strategic planning and resource allocation. He leads the firm's research activities in valuation and capital markets. Before joining McKinsey, he worked with Stern Stewart & Company and with Mobil Corporation. He received his MBA from the University of Chicago.

Marc Goedhart is a senior expert in McKinsey's Amsterdam office and an endowed professor of corporate valuation at Rotterdam School of Management, Erasmus University (RSM). Over the past 25 years, Marc has served clients across Europe on portfolio restructuring, M&A transactions, and performance management. He received his PhD in finance from Erasmus University.

David Wessels is an adjunct professor of finance at the Wharton School of the University of Pennsylvania. Named by Bloomberg Businessweek as one of America's top business school instructors, he teaches courses on corporate valuation and private equity at the MBA and executive MBA levels. David is also a director in Wharton's executive education group, serving on the executive development faculties of several Fortune 500 companies. A former consultant with McKinsey, he received his PhD from the University of California at Los Angeles.

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An Overview of Corporate Valuation

• Benedicto Kulwizira Lukanima 2  
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Valuation is one of the important topics in the business field, and it plays a key role in many areas of finance such as corporate finance, mergers and acquisitions (M&A), and portfolio management. Although the term “valuation” seems to be common, many people may be unaware of its core components. There are different ways in which valuation can be performed—hence, it is not an absolute process. Therefore, different analysts recommend different values for the same company or equity, simply because of different valuation approaches depending on valuation purposes and the suitability of valuation approaches. From this point of view, the definition of value (or valuation) depends on the valuation approach—for example, we have intrinsic value, relative value, market value, and book value.

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Bibliography

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DePamphilis, D. M (2019), Mergers and Acquisitions Cash Flow Valuation Basics, In: Mergers, Acquisitions, and Other Restructuring Activities: An Integrated Approach to Process, Tools, Cases, and Solutions 10th ed. , Elsevier Inc., 177 – 205, doi: https://doi.org/10.1016/C2017-0-02823-9

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Fabozzi, F. J., Focardi, F. M., & Jonas, C. (2017), Equity Valuation: Science, Art, or Craft ? Issue 4, CFA Institute.

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Fischoff, S. (2021), Business Valuation Is More Art Than Science, Available vis Financial Poise, https://www.financialpoise.com/art-of-business-valuation/ .Accessed on July 1, 2021.

Mackintosh, J. (2022). Value Investing Is Back. But for How Long? A bounce in bond yields is good news for dividend payers. Available via The Wall Street Journal. https://www.wsj.com/articles/value-investing-is-back-but-for-how-long-11643726540 . Accessed February 01, 2022.

Nelson, B. (2022). Investing’s First Principles: The Discounted Cash Flow Model. Available via CFA. https://blogs.cfainstitute.org/investor/2022/01/19/investings-first-principles-the-discounted-cash-flow-model/ . Accessed January 19, 2022.

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Analysis of Academic Literature on Environmental Valuation

Francisco guijarro.

1 Research Institute for Pure and Applied Mathematics, Universitat Politècnica de València, 46022 Valencia, Spain

Prodromos Tsinaslanidis

2 Department of Economics, University of Western Macedonia, 52100 Kastoria, Greece

Environmental valuation refers to a variety of techniques to assign monetary values to environmental impacts, especially non-market impacts. It has experienced a steady growth in the number of publications on the subject in the last 30 years. We performed a search for papers containing the term “environmental valuation” in the title, abstract, or keywords. The search was conducted with an online literature search engine of the Web of Science (WoS) electronic databases. A search of this database revealed that the term “environmental valuation” appeared for the first time in 1987. Since then a large number of studies have been published, including significant breakthroughs in theory and applications. In the present work 661 publications were selected for a review of the literature on environmental valuation over the period 1987–2019. This paper analyzes the evolution of the leading methodologies and authors, highlights the preference for the choice experiment method over the contingent valuation method, and shows that relatively few papers have had a strong impact on the researchers in this area.

1. Introduction

Environmental valuation has traditionally been considered in the context of non-market valuation. Its aim is to obtain a monetary measure of the benefit or cost to the welfare of individuals and social groups of environmental improvement interventions or the consequences of environmental degradation [ 1 , 2 ]. However, the ultimate goal is not to value a (non-market) environmental good in monetary terms, but to provide decision-makers with the necessary tools to take the appropriate political initiatives to efficiently allocate resources, impose taxes and design compensation schemes [ 3 , 4 ], even after assuming the difficulties of developing theoretically grounded practical policy tools and avoiding political manipulation [ 5 ].

Environmental valuation methods have been used to determine the benefits and costs related to the use of environmental goods, improving their conditions or remedying environmental damage and must consider the complexity of the area. For example, the economic benefits of national parks extend beyond tourism; natural amenities and recreation facilities often serve to attract and retain people, entrepreneurs, businesses, and retirees [ 6 ]. On the other hand, some researchers have provided evidence of how worsening environmental conditions can affect the value of other goods. For example, noise and air pollution from road traffic have been reported to negatively impact real estate prices [ 7 , 8 ], and [ 9 ] reported that 55% of those surveyed in Brisbane (Australia) considered that noise adversely affected the value of their property.

Economists have traditionally developed tools to measure environmental values by estimating individuals’ willingness to pay to benefit from environmental goods. The costs associated with environmental deterioration are measured by the loss suffered by the individuals who benefited from the damaged good, and deciding the appropriate compensation for losing the benefit (willingness to accept) [ 10 , 11 ].

The general approach of Total Economic Value (TEV) combines all the different values, which are grouped according to the service provided by the environmental good ( Figure 1 ). The use values are those derived from the actual use of the resource, while the non-use values are not related to its present use. The former includes the direct use value—the value derived from the direct use and exploitation of the environmental good, the ecological value—defined by the benefits that environmental goods provide to support forms of life and biodiversity and the option value—related to future use opportunities of the good. Non-use values are composed of the existence value—the value that individuals give to environmental goods for their mere existence—and the bequest value—the value estimated by individuals when considering the use of goods in the future by their heirs.

The concept of Total Economic Value of environment, taken from [ 12 ].

The aim of environmental valuation methods is to measure the values included in TEV. Although some authors have classified valuation methods from a more general perspective [ 13 ], the methods specifically related to environmental valuation can be classified as follows:

• - Contingent valuation method. Values are estimated in a hypothetical market based on surveys in which respondents are asked how much they are willing to pay for the use and conservation of an environmental good. The purpose of contingent valuation is to estimate individual willingness to pay for changes in the quantity or quality of environmental goods or services [ 3 ].
• - Choice experiment method. This method provides the respondents with alternative choices in which different environmental goods are defined by their attributes. According to [ 14 ], “the most significant advance in environmental valuation may be to move away from a focus on value and focus instead on choice behaviour and data that generate information on choices.”.
• - Travel cost method. Values are estimated by accounting for the cost incurred by people who travel to visit an environmental good. The method assumes that the willingness to pay must be at least as large as the travel cost incurred.
• - Hedonic price method. Values are computed from the prices of traded goods. This approach is frequently used when the price of traded goods is influenced by environmental factors [ 8 ].

The field of environmental valuation has recently expanded both from a theoretical and practical point of view [ 15 ]. This paper aims to outline the advances made by researchers according to their impact on the research area and highlights the key aspects covered by leaders in this field.

To determine the most important topics and assess the academic impact of environmental valuation, we performed a bibliometric analysis considering publications in the Web of Science from 1987 to 2019. We assessed their productivity through their historical evolution and the distribution of papers by journal. The units of analysis were ordered by the citation and co-citation structure and the results gave insights into the organization and future trends on research in environmental valuation.

We performed a search for papers containing the term “environmental valuation” in the title, abstract or keywords. The search was conducted on the online literature search engine of the Web of Science electronic databases. On 17 December 2019 we obtained 661 results from the search engine covering the period 1987–2019, including articles, book chapters, proceedings papers and reviews of 1442 authors. Table 1 shows the protocol followed to perform the data collection and some key figures.

Procedure for the data collection and key figures.

The dataset is analysed in the following section on R [ 16 ], a free software environment for statistical computing and graphics. The bibliometrix [ 17 ] package was used to compile most of the tables in this paper.

3.1. Environmental Evaluation Publication History

The number of publications per year is depicted in Figure 2 . The first known paper on environmental valuation, published in 1987, was followed by a steady increase in number of environmental valuation-related publications over time.

Distribution of Environmental valuation publications by year (1987–2019).

Although the research was published in a wide range of journals, the 4 most popular were: Ecological Economics, Environmental & Resource Economics, Environmental Values, and Journal of Environmental Management– with nearly 30% of the studies ( Table 2 ). Ecological Economics stands out as the most prolific source on this subject with 109 papers, which represents 16.5% of the total sample. Not surprisingly, the top Journals are particularly involved with environmental and ecological issues. The first 6 Journals are grouped into Environmental Sciences or Environmental Studies categories from the Journal Citation Reports of the Web of Science. When taking the impact factor into consideration, the top 6 Journals were ranked into the first quartile of their corresponding categories in 2018, while the rest of Journals are between the first and second quartile in most cases.

Most relevant journals that have published the greatest number of environmental valuation papers.

3.2. Leading Topics in Environmental Valuation Research

The most common keywords used by researchers include “environmental evaluation”, “willingness to pay”, and “ecosystem services” ( Table 3 ). The keyword “environmental valuation” was used in 38% of the publications analyzed. The following keywords give useful insights into the evolution of the research topic and the methods developed and applied to value environmental goods and damage. The second most often used keyword is “willingness to pay”, which is commonly found in publications related to stated preference methods. The two abovementioned approaches to this group of environmental valuation methods occupy positions 4 (choice experiment) and 5 (contingent valuation). The choice experiment method also appears in the 7th position as “choice experiments”. The total of both alternatives (78) comes just after the “environmental valuation” keyword.

The 10 most used keywords by number of publications related with environmental valuation.

As the search procedure is automatic, the system differentiates “Choice experiment” from “Choice experiments”. In order to consider all the possible synonyms, we conducted a new experiment by searching for individual terms in the keywords ( Table 3 ). For example, the word “choice” was used to collect all the papers with a keyword related to the choice experiment method. This provided similar expressions to those given in Table 3 : Choice modeling, Choice modelling, Choice model, Choice experiment method, etc. The analysis showed that keywords related to the choice experiment method appeared in 165 papers, while other methods had a lower frequency (contingent valuation method, 69; hedonic price method, 18; travel cost method, 11).

The relevance of choice experiments as a prominent keyword used by researchers has increased over time. We show the evolution of four keyword categories through 3 equally spaced subperiods: 1987–1997, 1998–2008 and 2009–2019 ( Figure 3 ). The first two subperiods were dominated by keywords associated with contingent valuation methods (with labels “contingent valuation” and “contingent valuation method”) and cost-benefit analysis. However, a sudden change was found in the trend during the subperiod 2009–2019. During this time the choice experiments (with labels “choice experiment”, “choice experiments” and “choice experiment model”) dominated the researchers’ interest, closely followed by the willingness to pay keyword.

Evolution of the main keywords used by researchers in environmental valuation.

The popularity of the choice experiment method –over the contingent valuation method—was predicted by Adamowicz [ 14 ]: “The most significant advance in environmental valuation may be to move away from a focus on value and focus instead on choice behaviour and data that generate information on choices.” We can suggest several reasons to support the observed trend. First, the design of both methodologies makes the choice experiment method to extract more information than the contingent valuation method does. Results from contingent valuation are elicited by asking respondents for their willingness to pay (or willingness to accept). In a bidding game, the respondent is asked if he is willing to pay a specific amount of money. If the answer is yes, a higher amount is asked and, if the answer is no, a lower amount is proposed. The questionnaire is repeated until an initial yes changes to a no or vice versa. However, the choice experiment method uses attributes to define alternatives and information of the willingness to pay is obtained by observing the choices made by respondents [ 18 ]. As stated by Hoyos [ 15 ], the choice experiment method allows estimating the mean willingness to pay and also the marginal willingness to pay for the different attributes. Handling with more alternatives and attributes makes the application of the choice experiment more complex. However, its implementation has been facilitated by the development of statistical software. Furthermore, web-based surveys are becoming popular and easy to implement and the number of connected people to the internet keeps increasing, which limits biased sampling, then allowing presenting the choice set in a friendly manner [ 19 ]. An additional benefit from using the choice experiment method is related with the sensitivity to scope. This is one of the main concerns about the contingent valuation method, where the use of labels in the choice experiment may mitigate the lack of sensitivity to the scope [ 19 ].

3.3. The Most Influential Authors in Environmental Valuation

The most prominent authors in an area of research can be identified by citation analysis. Of the top 10 most influential publications on environmental valuation according to the number of citations, Boxall and Adamowicz [ 20 ] leads with 527 citations ( Table 4 ). The authors use a latent class model to evaluate choice behaviour as a function of observable attributes of the choices and latent heterogeneity in the respondents’ characteristics. Although it has the highest number of citations, the paper by Lancsar and Louviere [ 21 ] received more cites on a yearly basis. The choice experiment model dominates the top ranked papers of Table 4 in which the authors introduce different environmental valuation examples to illustrate their proposals. Some of the top ranked papers are devoted either to the demonstration of case studies or to a review of the literature.

The 10 most frequently cited papers on environmental valuation.

We have analyzed the relevance of different authors in the topic according to the number of publications and the number of citations per year. Figure 4 gives one line to each author, where the extremes represent the year of the first (left circle) and last publication (right circle). Hanley was cited for the longest period, which was 25 years (1995–2019). The diameter of the circles varies in proportion to the number of papers published each year and the colour denotes the number of cites received. For example, the paper by Hanley et al. [ 22 ] has the highest number of citations per year (21.9) in the table. Although this is not the most cited paper according to the bibliographic analysis, it appears in the figure because Hanley is the most prolific researcher.

Relevance of authors according to their production and the number of citations.

The figure distinguishes two groups of authors. The first incorporates those who have been publishing on the topic for roughly 20 years: Hanley, Adamowicz, Boxall, Spash and Brouwer. The other group contains those who published between 2007 and 2019: Meyerhoff, Schaafsma, Hoyos, Mariel and Thorsen.

It should be noted that a few papers are responsible for a high percentage of the citations ( Figure 5 ). gives the number of citations in descending order. Only 7 papers received more than 300 citations for the whole period analyzed, while 55.7% received 10 or fewer. This shows that only a few papers influenced this research topic during this period.

Distribution of citations per paper.

Lastly, there is another interesting point related to the authors’ affiliation country; Figure 6 separates the papers whose authors’ affiliations are all located at the same country (Single Country Publications, SCP) and those with authors’ affiliations from different countries (Multiple Country Publications, MCP). The UK and the USA dominate the research on environmental valuation according to the number of papers published during the analyzed period. There are only 5 European countries in the top 10, while China is the only Asian representative. China is also in the last position in the top 10. Regarding the collaboration between authors from different countries, researchers from the UK and Spain are the most likely to collaborate in multinational publications, while Brazilian and Chinese affiliations produced the fewest publications with contributions from foreign authors.

Most productive countries in environmental valuation.

3.4. Co-Citation Analysis

This subsection begins with some comments about what productivity is in the field of research publication. Of course this is a wide field of debate, but some preliminaries must be established before proceeding with the co-citation analysis. According to [ 29 ], there are several measures to account for productivity. The most basic bibliometric measure is the number of papers published, which provides the raw data for all citation analysis. Another measure is the number of citations, which determines the recognition and influence of a paper. Then we can distinguish between citations received from papers published in Journals indexed in WoS, or citations received for other Journals not considered in WoS. As stated by [ 29 ], a measure of association between highly cited papers is used to form clusters: “That measure is the number of times pairs of papers have been co-cited, that is, the number of later papers that have cited both of them”. Hence, co-citation implies that two papers are cited in a third paper and assumes that both papers are related. We have performed a co-citation analysis by differentiating 3 main clusters in different colours ( Figure 7 ). The references of cluster 1 are represented by the book by Mitchell and Carson [ 30 ], in which the authors describe the contingent valuation method and claim that “the contingent valuation (CV) method offers the most promising approach for determining public willingness to pay for many public goods”. However, the positivist perspective in Mitchell and Carson [ 30 ] is contested by other prominent works in the same group. The report in Arrow et al. [ 31 ] indicate several drawbacks to the contingent valuation method and gives some guidelines to be used if the proposal is to produce useful information for natural resource damage assessment. The research in Kahneman and Knetsch [ 32 ] reports the most serious shortcoming of the CV method. According to these authors: “the assessed value of a public good is demonstrably arbitrary, because willingness to pay for the same good can vary over a wide range depending on whether the good is assessed on its own or embedded as part of a more inclusive package”. There is a more recent relevant book in this group, Bateman et al. [ 33 ], which gives a general approach to stated preferences techniques with application to different non-market goods and services.

Co-citation network analysis.

The cluster 2 (in red) elicited from the co-citation analysis is led by the paper by Boxall et al. [ 26 ], “A comparison of stated preference methods for environmental valuation”. This paper introduces an empirical comparison of the contingent valuation method and choice experiments. Most papers in this group follow the approach in Boxall et al. [ 26 ]. For example, Adamowicz et al. [ 34 ] examine the choice experiment as “an extension or variant of contingent valuation”. The paper in Adamowicz et al. [ 35 ] had earlier compared a stated preference model and a revealed preference model for recreational site choice. The earliest work in the group is the book by Ben-Akiva et al. [ 36 ], which analyzes the discrete choice method from a more general perspective.

And lastly, the cluster 3 covers different references related to choice modelling approaches but with a different approach to the publications in the second group. Again, a single book is the leader in number of cites: Louviere et al. [ 37 ]. Interestingly, this book is not the only reference which gives a survey of choice modelling. The paper by Hoyos [ 15 ] provides a review of the state of the art of environmental valuation with discrete choice experiments; Hanley et al. [ 22 ] examine the choice modelling approach to environmental valuation. The authors state that this methodology “can be considered as an alternative to more familiar valuation techniques based on stated preferences such as the contingent valuation method”; Hanley et al. [ 23 ] also outline choice experiments and analyze its roots in Lancaster’s characteristics theory of value; while the paper by Lancaster [ 38 ] is another relevant work in this group.

4. Discussion

Environmental valuation is intrinsically difficult because realistic environmental valuation situations are rarely observed, and singularities in environmental assets impede a uniform treatment of those values outlined by the Total Economic Value. Notwithstanding the difficulties, a plethora of papers have been published during the last decades.

As a result of this research it can be concluded that revealed preferences methodologies are surpassed by works focused on stated preference methods for the analyzed period as a whole. The research discloses the relevance of stated preference methods over revealed preferences methods, with a clear dominance of choice experiment over any other environmental valuation method, as predicted by Adamowicz [ 14 ]. The complexity of the choice experiment method has resulted in new challenges and research lines for academics. Choosing and implementing experimental designs, interpreting standard and more advanced random utility models, and estimating measures of willingness-to-pay are some of the issues covered by researchers [ 39 ].

Differences on the environmental valuation have been also revealed by the co-citation analysis, which reports different clusters by considering the methods used in the environmental valuation process. Despite its past influence, none of the travel cost and hedonic price methods is in the 10 most popular methods of environmental valuation, according to the keywords in the dataset used. In addition, the leading Journals in the publication of environmental valuation papers are ranked in prominent positions by WoS in their corresponding categories. The paper also distinguish two groups of authors according to the time they have published on the topic. The first group initiates the growth of the area in the mid-1990s, while the second group concentrates its impact mainly from 2010.

The abovementioned differences in the use of the environmental valuation methods do not imply that one method is unequivocally better or worse than another since its appropriateness depends on a particular situation. In other words, no single method is suitable in all valuation scenarios. Rather, the choice of the valuation method is context-specific. Revealed preference methods can be prioritized when budget and time are constrained. Stated preference methods require a complex questionnaire development and data analysis, which translates into an additional need of resources (both money and time). Conversely, revealed preference methods can only capture use values, while stated preference methods can estimate both use and non-use values. In addition, using multiple methodologies can be appropriate in some situations. For example, the combination of revealed and stated preference methods can improve benefit estimation of a single component [ 35 ]. This approach can be useful when a revealed preference method is utilized as the main valuation instrument, but some environmental values are more accurately estimated by using another method and the result is aggregated. In this case, the researcher must be careful to avoid double counting if the components of value captured by the different methods overlap [ 40 ].

5. Conclusions

From the evolution of environmental valuation publications in the last 30 years, we can assert that the discipline has been consolidated. Papers related to choice experiments have dominated academic production in the last decade. In the current stage of environmental valuation researchers will have to cope with new challenges and emerging trends. As in other research areas, the increasing ability to collect enormous amounts of data facilitates the creation of the available massive databases, which can be used to take environmental valuation methodologies to the next stage in their evolution by incorporating machine learning techniques in the valuation process. However, this evolution should not be restricted to new applications of the well-known valuation methods only. Researchers must develop new approaches to deal with new elements in the valuation process. We expect that climate change, as one of the defining challenges of the 21st century, will attract most attention from researchers to propose new approaches in environmental valuation [ 41 , 42 ]. As knowledge and perception are subjective, the intangible aspects must be explicitly considered in the new valuation methods [ 13 ]. In this regard, we may conclude that the future path of environmental valuation is not necessarily related to new methodologies, but to the inheritance and assimilation of consolidated techniques commonly used in other scientific areas.

Acknowledgments

We would like to thank three anonymous referees for their constructive comments and suggestions that substantially improved this article.

Abbreviations

The following abbreviations are used in this manuscript:

Author Contributions

Conceptualization, F.G. and P.T.; methodology, F.G. and P.T.; software, F.G.; validation, P.T.; formal analysis, F.G.; investigation, F.G. and P.T.; resources, P.T.; data curation, F.G.; writing—original draft preparation, F.G.; writing–review and editing, P.T.; visualization, F.G. and P.T.; supervision, P.T. All authors have read and agree to the published version of the manuscript.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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Article Contents

1. empirically grounded models of subjective beliefs, 2. expectation formation in asset pricing, 3. macro and monetary economics connections, 4. macro and monetary drivers of asset prices, 5. intermediary asset pricing and beyond, 6. intermediary asset pricing: assessment and future directions, 7. international finance, 8. an asset pricing view of exchange rates, acknowledgement.

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Review Article: Perspectives on the Future of Asset Pricing

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Markus Brunnermeier, Emmanuel Farhi, Ralph S J Koijen, Arvind Krishnamurthy, Sydney C Ludvigson, Hanno Lustig, Stefan Nagel, Monika Piazzesi, Review Article: Perspectives on the Future of Asset Pricing, The Review of Financial Studies , Volume 34, Issue 4, April 2021, Pages 2126–2160, https://doi.org/10.1093/rfs/hhaa129

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The field of asset pricing is a rich and diverse discipline that has contributed to many areas of discourse, including those of fundamental importance to policy makers, investors, and households. 1 As we look ahead during a time of substantial economic and political change, it is apparent that society faces many pressing questions, both new and old, that the field is uniquely suited to informing.

To contribute to this conversation, the NBER Asset Pricing program convened a panel discussion on “Perspectives on the Future of Asset Pricing” at its November 8, 2019, meeting that took place at Stanford University. The objective of the panel was to identify some of the important questions the field could productively address in the next five to 10 years. The panelists, consisting of experts in several subfields of asset pricing, were invited to share their views on these questions with an eye toward innovative research topics that are ripe for exploring, and the metrics the field could be using to gauge progress.

This article summarizes these views. The list of topics covered by the panelists is by no means exhaustive. We hope that this article shall nevertheless serve as a productive conduit for identifying some key questions for investigation and generating a concerted research effort toward answering them.

Beliefs are central to asset pricing. 2 Asset prices are forward-looking, and essentially any asset-pricing model implies that investors price assets based on their beliefs about the joint distribution of some stochastic discount factor (SDF) |$M_{t+1}$| and payoffs |$X_{t+1}$|⁠ . An observer outside the field of asset pricing might therefore guess that a major part of the research efforts in asset pricing are devoted to understanding how investors form beliefs. This is, at least so far, not the case.

The vast majority of theoretical and empirical work in asset pricing is based on the rational expectations (RE) paradigm. Under RE, as in Lucas (1978) , investors are assumed to know the economy’s underlying model and the model parameters, and to forecast rationally.

Within the RE paradigm, there is no role for the study of beliefs: theoretically, beliefs are implied by the model; empirically, an econometrician can recover investor beliefs from the large-sample empirical distribution of |$M_{t+1}$| and |$X_{t+1}$|⁠ .

RE-based approaches have been useful to establish a benchmark, but asset prices have not yielded easily to RE-based explanations. Seeing the increasingly complicated dynamics in preferences and endowment processes researchers use to reverse-engineer better-fitting RE models, it is natural to wonder whether more progress could be made by treating belief dynamics as an object of theoretical and empirical study.

I propose that such a research program should be organized around the following three principles:

Research focus should be on motivating, building, calibrating, and estimating models with non-RE beliefs rather than on merely rejecting RE models. To make further progress, we need structural models of belief dynamics that can compete with RE models in explaining asset prices and empirically observed beliefs.

Deviating from RE does not necessarily imply assuming irrationality. For example, models of Bayesian learning relax the RE assumption that agents know the model of the world and its parameter values, while retaining the rational forecasting assumption. Exploration of cognitive limitations, bounded rationality, and heuristics that relax the rational forecasting assumption may have promising insights to offer as well, but even models of rational learning can produce asset-price properties that are quite different from those in an RE setting. 3

Belief dynamics should be disciplined with data on beliefs and micro decisions. While reverse-engineering of preferences and technology to fit asset prices is common in the literature, I would argue that we should not follow this approach with beliefs. Taking beliefs seriously as an object of scientific study also means bringing in empirical data that helps pin down their dynamics.

In line with these principles, a number of areas seem promising for future research:

1.1 Belief measurement

The availability of beliefs data has improved substantially in recent years, but for beliefs data to become a standard ingredient of asset-pricing research, further progress on measurement is necessary.

So far we do not have a good understanding of investor expectations of firm cash flows. Existing work on expectations in asset pricing has often focused on return expectations, but return expectations alone, especially ones limited to relatively short forecast horizons, do not provide a complete picture of how beliefs dynamics explain variation in price levels. Forecasts of earnings and dividends by analysts, used in De la O and Myers (2019) , and by CFOs in the Graham and Harvey (2011) survey are a start, but there is more to be done.

Collecting more data on long-run expectations would be useful. Much of the currently available expectations data is focused on short forecast horizons such as one year. Asset prices, however, depend on expectations over much longer horizons.

In addition to perceived first moments, investors’ subjective perception of risk are also of obvious relevance to asset pricing. 4 Data on beliefs about the perceived downside tails of the distribution would be particularly interesting. Since crashes and disasters are infrequent, objective historical data does little to pin down these tails, leaving lots of room for subjective judgements. Eliciting beliefs about the shape of distributions is a challenging problem, but further research on belief elicitation methods may bring progress in this area, too ( Manski, 2018 ).

Finally, in many asset-pricing applications, we are interested in dynamics of beliefs at frequencies of the business cycle, or even lower. This means that we need long time series. The time series available in survey data have lengthened substantially, but there are still potential benefits from innovations that can help to extend beliefs data backwards in time, for example, with proxies constructed from textual analysis of media, somewhat akin to what Manela and Moreira (2017) have done to extend the VIX index.

1.2 Beliefs and actions

If a respondent in a survey states a belief, this does not necessarily mean that the respondent is ready to act in accordance with this stated belief. In addition to a decision-relevant signal, stated beliefs likely contain measurement noise. For example, it is unlikely that respondents deliberate as carefully when stating beliefs as they would if they actually had to take an action. Moreover, even if stated beliefs truly reflect respondents’ perceptions, actual or cognitive costs of taking an action may prevent respondents from perfectly aligning their choices with these beliefs. To sort out these issues, more research on the connection of beliefs and actions is needed. 5

For asset pricing, we also need to understand the properties of this measurement error when we aggregate across respondents. If measurement error averages out within the population, or within demographic groups, then matching asset-pricing models with beliefs moments based on such aggregates may be fine, even if the links between stated beliefs and actions are distorted by measurement error at the individual level. To the extent that more sophisticated agents play a bigger role in financial markets, how the belief-action relationship varies with sophistication is an important issue as well. 6

Another important question to sort out is whose beliefs matter for pricing. Individual investors’ beliefs can differ from those of professional investors. The relative importance of their beliefs in influencing asset prices likely differs by aggregation level: For allocation decisions at the asset class level (e.g. stocks vs. bonds), it seems likely that individuals exert substantial influence because the investment products they choose from often have predetermined allocations to an asset class; at the individual stock level, fund managers have discretion; at the investment-style level (e.g., small vs. large stocks) there is probably a mix of predetermined choices by individuals and managerial discretion. Sorting this out empirically would also help in linking belief-based approaches with a demand-system analysis as in Koijen and Yogo (2019) .

1.3 Modeling belief formation

Models of investor belief dynamics need to take a stand on the sources of information that investors rely on when they form their beliefs and how they digest this information to produce forecasts. Two questions in this regard seem particularly interesting.

How do investors form beliefs when faced with high-dimensional prediction problems? Existing asset-pricing models with parameter learning typically consider settings with a small number of predictor variables. Reality, however, looks different. For example, to value stocks, investors must forecast cash flows. They observe a vast number of potential predictor variables, but they do not know the precise functional relationship between predictors and future cash flows and need to learn it from observed data. In a simple linear setting with homogeneous investors, Martin and Nagel (2019) show that this can give rise to return predictability and a “factor zoo.” Further efforts to close the gap between the simplistic environments investors face in asset-pricing models and the messy real world seem necessary.

The second question concerns the role of memory. Data can shape beliefs only if it is remembered. In theory, one could imagine a Bayesian learner that takes into account “all available” history when learning about pricing-relevant stochastic processes. But when mapping these models into the real world, it is not clear what “all available” means. Some implementations of learning models set time zero to 1926, because this is where the CRSP database starts, but this is obviously not the true starting point of investors’ learning process. Moreover, there are reasons to expect that memory could be limited. More research, both empirically and theoretically, is needed to better understand investors’ formation of memory, including those of institutions (e.g., through maintenance of data sets or establishment of decision rules). 7

1.4 Beyond asset pricing: Macro-finance

The drivers of stock price dynamics emphasized in asset pricing research are largely disconnected from the drivers of the business cycle that macroeconomists focus on (see, e.g., Cochrane 2017 ). This question should be revisited through the lens of models with non-RE belief dynamics. Shocks to beliefs are potential source of links between asset prices and macro quantities. Exactly how such links could play out is an open question. 8

Beliefs effects could operate in ways that are quite different from time-varying preferences that macro and asset-pricing research has already explored in various ways. For example, belief effects can be specific to technologies or markets. An individual could be optimistic about the housing market but, at the same time, pessimistic about the stock market. Beliefs data will be important to sort out the commonalities and differences between different sectors and markets.

Interactions of beliefs with frictions are potentially interesting. For example, belief heterogeneity can interact with frictions in a way that amplifies shocks ( Caballero and Simsek (2020) ). The housing market seems to be a particularly interesting area to explore these types of mechanisms, as it features substantial frictions and plays a big role in the macroeconomy.

1.5 Conclusion

Asset prices express investors’ beliefs about the future. Our understanding of how investors form these beliefs, how they evolve over time, and how we can measure them is still limited. Empirically grounded research on investor beliefs holds promise to unlock some of the mysteries of asset pricing.

The conventional agenda in the asset pricing literature studies quantitative rational expectation models. 9 This approach is particularly well suited for the study of recurring patterns, such as the comovement of price-dividend ratios on stocks with the business cycle or the seasonality in the housing market during a calendar year. (Volume and prices are above trend during the summer, while activity in housing markets slows down in the winter.) In rational expectation models, the expectations of agents reflect these recurring patterns and are consistent with the equilibrium dynamics of the model. The model is successful if the distributions of equilibrium prices and quantities are consistent with those in the data.

An advantage of this approach is that agents’ expectations do not introduce free parameters. Rational expectations impose cross-equation restrictions on agents’ expectations and equilibrium dynamics that constrain these parameters to be the same. This approach imposes a welcome discipline on the model if there are no data on expectations. By design, the approach assumes that all agents have the same expectations. To study differences in expectations, a researcher has to specify the source of information that only some agents may receive, while others do not.

Recent years have witnessed a massive effort to collect expectational data. These new data enable researchers to be more agnostic about how agents arrive at their expectations. It is now possible to discipline expectations with direct observations on households and firms. More and more surveys that ask respondents about their expectations also ask them about their characteristics (e.g., household or firm age, income, or sales) and choices (e.g., investments). These data allow researchers to study the joint distribution of expectations, characteristics, and choices. Now a model is successful if it can match the observed joint distribution in the data.

Freeing up expectations is especially appealing to study unique episodes that are associated with structural change. In these instances, it is often not clear how agents were forming expectations at the time. For example, what explains postwar house price booms? Two major boom-bust episodes stand out in the United States, because they coincide with booms in other countries (e.g., chapter 4.5 in Piazzesi and Schneider 2016 ). The first boom occurred during the late 1970s and early 1980s, while the second boom occurred in the early 2000s; both episodes had unique features. An important contributor to the first boom was the Great Inflation. How did households form expectations about future inflation during this unique event? Low interest rates during the second boom made it cheap for households to borrow and increased the value of houses, computed as the present value of a stream of future housing services. When house prices collapsed in 2007, interest rates came down further and remained close to zero. Did households during the boom foresee the low rates after the collapse? Did home buyers at the peak of the boom expect house prices to further appreciate, or were they aware that house prices were about to decline? What did renters expect during these years? Again, surveys help us understand households’ expectations and actions during this unique episode. It is difficult to think about these house price booms as recurring patterns.

2.1 Big data collection efforts

Central banks have recently pioneered massive data collection efforts to improve the foundations of their economic models and ultimately their conduct of monetary policy. Many private companies, such as Vanguard, contribute to this effort because they want to better understand their clients. The surveys ask individual households or firms about their expectations for the future. Some surveys ask respondents to forecast aggregate variables: macro-economic indicators (e.g., inflation, GDP growth) or financial variables (e.g., stock returns, bond returns). Other questions ask about individual-specific variables such as income. More and more, surveys ask the same respondents about their expectations and actual choices. For example, households are asked about their stock return forecasts and stock holdings. Other surveys ask firms about their current sales and sales forecasts.

Examples of such surveys include the European Community Household Panel by the European Central Bank and its member banks, which asks a panel of households about their income and living conditions. There are modules in the survey that ask about household expectations. Since 2011, the Bundesbank has been conducting the Panel of Household Finances, which asks households about their expectations and their choices. Since 2013, the Federal Reserve Bank of New York has the Survey of Consumer Expectations, and the Federal Reserve Board has the Survey of Household Economics and Decision Making. The Bank of Canada has conducted the Canadian Survey of Consumer Expectations since 2015.

There has been considerable progress in how to frame the survey questions in a way that enables people without formal training in statistics to express their perceived uncertainty about these forecasts. This progress is often made by academics who are directly involved in the survey. For example, Bachmann et al. (2019) ask German firms about their future sales growth in the Ifo Business Tendency Survey. To get a measure of the uncertainty that firms perceive, the researchers formulate a survey question that asks firms to provide a best- and worst-case scenario for their future sales growth. The span between these scenarios provides a useful measure of uncertainty, since most of these firms routinely use scenario analysis. Bachmann et al. (2020) document that firms find it difficult to express their uncertainty with a probability distribution. Many other surveys also involve researchers directly. For example, the Bundesbank solicits questions from academics for its Online Survey of Consumer Expectations. The Atlanta Fed Survey of Business Uncertainty involves researchers in its survey design. Companies like Vanguard allow researchers to ask questions for a subset of their investors.

2.2 How do survey expectations compare with rational expectations from conventional models?

The conventional agenda has worked hard to come up with models that are successful at generating investor expectations that are consistent with predictability regressions for asset returns. In the data, high ratios of asset valuations relative to fundamentals tend to be followed by low returns on the asset compared with the risk-free rate. For stocks and housing, regressions of excess returns over the next, say, five years on the current price-dividend or price-rent ratio have a negative slope coefficient that is statistically significant. For bonds, the regression is on the difference between the price on a long bond compared with a short bond. Expectations that capture this pattern describe investors who have low return expectations in booms. The reason why assets are highly valued despite this pessimistic outlook is that investors may be less risk averse in asset booms. Alternatively, investors may perceive less risk in booms.

Survey evidence challenges this view. A growing number of papers documents high return expectations in booms. For stocks, Greenwood and Shleifer (2014) provide evidence that investors predict high excess returns on stocks during booms. De la O and Myers (2019) show that high price-dividend ratios are associated with high cash flow expectations in surveys, while expected returns do not change that much over time. In bond markets, Piazzesi et al. (2018) document that forecast errors can account for a substantial component of cyclical movements in bond risk premia. During the postwar period, the Great Inflation is the one episode in which risk premia on long nominal bonds were high.

Similarly, for housing, Case and Shiller (2003) document that households that bought a house at the peak of the housing boom in 2003 were expecting double-digit appreciation rates for houses not only over the next year, but over the next decade. Piazzesi and Schneider (2009) document that there is only a small fraction of households (roughly 10 |$\%$| of all households) that believed it was a good time to buy a house during the early phase of the housing boom (during the years 2000–2003). This fraction doubles during the years 2003–2006. Since there are few housing transactions overall — less than 10 |$\%$| of the housing stock turns over in any given year — a boom in house prices is easily supported by a small fraction of households that are optimistic. The Case-Shiller evidence suggests that these optimistic households select themselves into these few transactions and sustain high valuations.

The conventional wisdom is still dominant. Research that relies on survey answers has to argue that they provide direct evidence about expectations. A question is whether survey respondents are the right people to ask about their expectations — they may not be marginal investors. Some surveys address this issue by focusing on people who recently bought the asset, as does the Case-Shiller survey of recent home buyers. Another question is whether the survey respondents really understand what they are being asked. There has been recent progress on this front by researchers who are involved in the survey design, as I already mentioned. Another important concern is whether survey answers reflect the career concerns of professional forecasters. 10 Finally, surveys may reflect risk-neutral forecasts. Adam et al. (2019) provide evidence against this argument for stocks. These concerns are important because they improve survey design and will lead to better survey evidence in the future.

2.3 Belief heterogeneity

Why do households have heterogeneous expectations? Some differences in beliefs can be explained with informational advantages by certain households. During the recent housing boom in Germany, for example, renters have on average higher rent and house price expectations than owners who severely under-predict these variables ( Kindermann et al. 2020 ). This pattern is consistent with the idea that housing is a unique asset, where non-owners (renters) may have more precise signals about housing dividends (in units of numeraire consumption) than owners of the asset who consume the housing dividend but may not know how much it is worth.

Will we be able to explain all the cross-sectional variation in household expectations? The answer to this question will likely be no. The same observable characteristics (e.g., age, income, and wealth) that have high |$R^2$| s in explaining other choices that households make (e.g., housing tenure) have rather low |$R^2$| s in explaining their expectations.

A more humble approach, which is still very interesting, is to admit that we do not know how households get to their expectations. Successful papers along these lines are Landvoigt (2017) , Lenel (2018) , and Giglio et al. (2019) . Even with a more humble approach, we can describe clusters of people (e.g., Piazzesi and Schneider 2009 ) and study how these clusters evolve over time (e.g., Burnside et al. 2016 ). These approaches may help us to make progress in our understanding of volume in asset markets, which is one of the most important open issues in finance.

2.4 How to use survey beliefs in models?

One way to use survey beliefs as an input into our models is to work with a temporary equilibrium concept (for an introduction, see chapter 3.4 in Piazzesi and Schneider 2016 ). Suppose heterogeneous agents solve dynamic optimization problems given some expectations that may be functions of time |$t$| variables. The outcome of these optimization problems will be an excess demand system for goods and assets at time |$t$|⁠ . Equilibrium prices set this system of equations to zero at time |$t$|⁠ .

A rational expectations equilibrium is a special case of a temporary equilibrium, that requires the expectations to be consistent with equilibrium dynamics. An alternative approach is to use survey forecasts to discipline expectations. This approach deals with unique episodes, which are reflected in the survey answers. A successful model then matches equilibrium prices and quantities as well as expectational data. An example of such an approach is Landvoigt et al. (2015) , who study the role of credit conditions and expectations during the housing boom of the early 2000s. Another is Leombroni et al. (2020) , who study the role of heterogeneous inflation expectations and inflation uncertainty for house prices and stock prices during the Great Inflation.

The field of finance and asset pricing has the potential to go through a transformational period by enriching other fields of economics and adopting new solution techniques. 11 Four important areas come to mind. First, asset pricing and financial frictions have become the centerpiece of modern monetary and macro economics. Central banks are key drivers of asset prices. This is especially true after the global financial crisis and during the COVID-19 global pandemic. We have witnessed unprecedented central bank activism: Most central banks heavily intervened in asset markets. Nowadays they not only determine the short-term interest rates but also impact many asset prices via quantitative easing programs, active yield curve management, funding for lending programs, and repo programs. Central banks are also not shy to conduct large-scale experiments with the economy, creating an “El Dorado” for empirical researchers. In addition, many interest rates turned negative calling for new fixed income models. Second, safe assets are the focal point of recent debate to understand the low interest rate puzzle and sudden flight-to-safety phenomenon. Safe is not necessarily the same as risk-free (or default risk-free). Third, new quantitative tools, such as neural networks and deep learning algorithms, now enable researchers to numerically solve nonlinear continuous-time macro-finance models with many state variables. These new methods allow us to solve models in which we disaggregate the financial sector and study the impact of various policies and shocks on banks, insurance companies, pension funds, and asset managers separately. Fourth, finance and money are in the middle of a technological revolution. New technology allows us to exploit big data and link various data sources. This makes digital money more attractive than physical cash and mitigates liquidity frictions. Hence, the asset feature of money has become more prominent, and many assets can gain some of the liquidity benefits of money. The main part of these remarks outlines four trends in more detail.

In New Keynesian (NK) macroeconomic models, arguably the dominant school in monetary economics (see, e.g., Woodford 2011 or Galí 2015 for nice textbooks), the key friction is price/wage stickiness. The central equation is the Euler equation, which describes the savings-consumption choice, and the most important price is the risk-free interest rate. In contrast, in macro-finance models, the focus is on financial frictions in form of borrowing frictions like in Kiyotaki and Moore (1997) or Bernanke et al. (1999) or incomplete markets like in Brunnermeier and Sannikov (2014 , 2016) . The latter embeds an intermediate asset pricing model in a macro setting in which resource allocation and economic growth are endogenous. The portfolio choice between a risky asset and safe asset, often in the form of money, is key. The price of risk and risk premia are at least as important as the risk-free rate. Resource allocation and the endogenous growth rate feed back into portfolio choices, the risk-free rate, endogenous volatility, and the price of risk. The term premium contains a risk premium, as do credit spreads — that is, credit spreads reflect risk attitudes as well as expected losses. The risk premium is the product of price of risk times the sum of exogenous and endogenous risk. Endogenous risk is subject to amplification and spirals and can reflect risk-on-risk-off phenomena. In short, the price of risk affects and is affected by the resource allocation and endogenous growth of the economy. All variables interact with monetary policy that tries to affect not only the risk-free rate but also risk premia. The emphasis on endogenously time-varying risk and price of risk, and the portfolio choice, is in sharp contrast to Heterogeneous Agent New Keynesian (HANK) models ( Kaplan et al. 2018 ) in which the risk premium is either zero or constant.

Another future trend that affects asset pricing is the recognition of the special role of safe assets. Brunnermeier and Haddad (2014) stress two important characteristics of a safe asset. First, a safe asset is like a good friend, who is around when you need her. Similarly to a good friend, a safe asset is valuable and liquid whenever you need it, at a random horizon. In contrast, a risk-free asset pays off a fixed amount at a prespecified horizon. Second, there is the “safe asset tautology.” A safe asset is safe because it is perceived as safe. Investors coordinate to fly into certain assets in times of crisis. This points in terms of economic modeling to settings with multiple equilibria and/or bubbles. For example, Swiss government debt is considered as a flight-to-safety asset and appreciates in crisis times, while the Swedish krona typically depreciates in value during crises. In “The I Theory of Money” models, money or government bonds are the safe asset and are a bubble.

Another trend that will change asset pricing relates to new modeling and especially numerical techniques. Neural network techniques will open up a new research avenue in macro-finance. Existing macro-finance models aim to keep the number of state variables that describe the evolution of the dynamical system low. This is especially true for macro-models with nonlinearities due to amplifications and runs since they cannot be solved simply by log-linearizing around the steady state. The limitation to a few state variables has prevented researchers from incorporating the richness of the financial intermediary sector. Typically, the financial sector is summarized by a single sector, even though we know that banks, insurance companies, asset management firms, pension funds, and so on have very different risk exposures across the various risk factors. Using novel deep learning techniques will endow researchers with the tools to fully explore the heterogeneity within the financial sector. Duarte (2018) and Lauriere et al. (2020) provide early advances in this area.

Finally, the fintech revolution will also alter asset pricing research. Big data has the potential to fundamentally reshape our economies, including financial intermediation and payment. So far, studies on payments have only played a role on the sidelines. However, with the recognition that payments deliver valuable data that can feed recommender systems and improve various scores, in particular credit scores, payments are moving to center stage. The current “banking-centric” industrial organization structure of financial activities might be replaced with a “payment-centric” structure. Platforms might be at the center rather than deposit-taking and lending banks. Social networks will play a more important role. Such a shift has important implications for money and also asset pricing. Money will become more digital. So far, bank accounts (inside money) are of course already digital. But only a part of outside money, central bank reserves, is digital, whereas cash is not. Digital money has the advantages that it can be linked to digital platforms, it can be traded automatically using smart contracts, and it is more convenient to use. Hence, as we already see in several countries, cash is losing its importance. Central banks have become increasingly concerned over the potential loss of monetary sovereignty— that is, the power to effectively conduct monetary policy. Among the three roles of money, the unit of account, store of value, and medium of exchange, ensuring that central bank-issued money remains a unit of account is essential for retaining monetary sovereignty. In models with incomplete markets, if debt is primarily denominated in the national currency, monetary policy can redistribute wealth (ex post) and be a risk-sharing tool (ex ante), as, for example in the case of the I Theory of Money. This reduces the price of risk and lowers endogenous risk. If transactions are increasingly conducted in and debt is denominated in new forms of digital currencies, for example those introduced by TechGiants, a process labeled as “digital dollarization” kicks in; see Brunnermeier et al. (2019) . Central banks lose their grip on monetary policy to smooth out the business cycle, and seigniorage revenue fades away. New “Digital Currency Areas” can emerge whose borders are more governed by connections to particular platforms and data regulation rather than national boundaries. For these reasons, central banks are seriously considering introducing their own “digital cash” in the form of Central Bank Digital Currencies (CBDC). Foreseeing the transition of the financial sector is not easy, but it is likely that currencies like Bitcoin and recently Libra will serve as catalysts in the same way Napster did for the music industry about 20 years ago.

This document identifies four trends that are likely to have an impact on research in asset pricing. First, central banks have become important players in asset markets, calling for more studies in which asset prices and monetary policy interact in a meaningful way. Second, flight-to-safety phenomena during a switch from a risk-on to a risk-off mood stress the importance of studying the role of safe assets. Third, new numerical techniques based on deep-learning machine-learning algorithms allow us to solve and estimate macro-finance models that reflect the heterogeneity of the financial intermediary sector. Finally, fintech and big data put digital money and payments at center stage with the emergence of digital currency areas.

A long intellectual history in economics considers the state of the macroeconomy among the most important drivers of asset markets, be these stock markets, bond markets, or housing markets 12 . At the same time, vast literatures in macroeconomics have argued that the aggregate state is itself profoundly influenced by the operations of monetary authorities around the globe. Ultimately, the question of whether and to what extent fluctuations in aggregate economic activity and/or monetary policies matter for asset pricing is an empirical one; thus, addressing it requires an empirical research agenda. As we ponder the exciting paths forward for the field of asset pricing, it is worth reflecting on where we are with this agenda.

On the question of whether macroeconomic risk matters for asset pricing, the evidence is mixed. Some research has found that it does, 13 while other research has found that it does not. 14 Confronted with these conflicting findings, it may be tempting to proceed as if macroeconomic conditions are unimportant for asset pricing. And yet, the stock market appears to react strongly to macroeconomic news (e.g., Boyd et al. 2005 ; Ai and Bansal 2018 ; Baker et al. 2019 ), a global financial crisis from 2007 to 2009 laid bare the important feedback loops between financial markets and the real economy, and it is hard to imagine that the tremendous structural change of the past several decades—slowing growth, rising profit shares, growing inequality, low and declining real interest rates—has not affected the pricing of risky assets.

Regarding monetary policy, there is ample evidence that unanticipated actions and announcements by central banks have important consequences for long-lived asset markets. 15 But surprisingly little attention has been given to understanding how this can occur, when all available evidence suggests that monetary policy shocks have transitory effects on the economy.

4.1 What’s the macroeconomy got to do with it?

There are many possible reasons why evidence cited above might be mixed: our models are gross simplifications of reality; the data are mismeasured and limited; our estimation tools are sometimes restrictive; information sets are unobserved. But one feature that is shared by all of the evidence cited earlier is their representative agent perspective, which presumes that growth in aggregate (average) consumption is an appropriate measure of systematic risk. At least when it comes to the pricing of equity, it is worth remembering how at odds this perspective is with even the most basic facts of stock market ownership. According to the Survey of Consumer Finances, just 52 |$\%$| of households owned equity in any amount or any form in 2016. More significantly, because stock market wealth is so heavily concentrated at the top (the top 5 |$\%$| of the stock wealth distribution owned 76 |$\%$| of the stock market in 2016), participation rates on a wealth-weighted basis are much lower than 52 |$\%$| and trending down since 2004. We might reasonably ask whether the representative agent framework is just too much of an abstraction.

Macro-finance trends also suggest an important role for heterogeneity. Indeed, the ratio of market equity for the corporate sector to three different measures of broad aggregate economic activity has trended up over time and is at or near its postwar high by the end of 2017. By contrast, the ratio of market equity to after-tax profits (earnings) for the sector is not trending up and is not near a postwar high. (See Greenwald et al. 2019 for plots.)

One response to these facts is to revisit an earlier literature that stressed the importance for equity pricing of limited stock market participation and heterogeneity ( Mankiw and Zeldes 1991 ; Vissing-Jørgensen 2002 ; Aït-Sahalia et al. 2004 ; Guvenen 2009 ; Malloy et al. 2009 ). Lettau et al. (2019) (LLM), and Greenwald et al. (2019) (GLL) do so by studying the empirical implications of a heterogeneous agent model characterized by two types of agents and imperfect risk sharing between them: wealth is concentrated in the hands of a few investors, or “shareholders,” while most households are “workers” who finance consumption primarily out of wages and salaries. In contrast to the earlier limited participation/heterogenous agent literature, the results in LLM and GLL suggest the relevance of frameworks in which investors are concerned about shocks that have opposite effects on labor compensation and shareholder payout. Such redistributive shocks play no role in the traditional limited participation/heterogeneous agent literature.

As regards the relevance of representative agent frameworks, LLM find that exposure to growth in the capital share of national income is an important determinant of return premia in the cross-section, while, conditional on this, exposure to aggregate consumption growth is not. GLL focus on understanding the factors that drive the real (adjusted for inflation) level of the stock market over time. During the past 30 years, a time when the market grew precipitously, GLL find that the most important driver of the market has been a string of “factor share shocks” that reallocated the rewards of production without affecting the size of those rewards. The realizations of this shock persistently reallocated rewards to shareholders and away from labor compensation, with no effect on economic growth. Economic growth contributed just 25 |$\%$| to the market’s rise since 1989, which could be compared to the period 1952 to 1988 when economic growth powered the stock market, accounting for more than 100 |$\%$| of its increase. But that 37-year period created less than half the equity wealth generated over the 30 years since 1989. These findings suggest not that the macroeconomy is irrelevant for the stock market, but that distributional shocks may be more imporant than aggregate ones.

Important questions remain. Why have factor shares changed so persistently? Will these trends continue? Do the reasons for the changes matter? (I suspect so.) How are these trends related to the broader trends in wealth and income inequality, in economic growth, and real interest rates?

4.2 The how, why, and whether of monetary policy

If the real values of long-term financial assets respond to the actions and announcements of central banks, the question is why? Asset pricing theories can generally rationalize such large responses only if something related to the conduct of monetary policy will have a persistent influence on real variables. Yet the notion that monetary policy could have long-lived effects on real variables is contravened by an agglomeration of foundational New Keynesian macro theories ( Galí 2015 ), and empirical evidence appears consistent with this ( Christiano et al. 2005 ). But if this is so, how does monetary policy influence long-lived assets?

One possibility is that some components of monetary policy do in fact have long-lasting, first-order effects on the aggregate economy, on short-term real interest rates, and on equity market return premia. Such are the implications of evidence reported in Bianchi et al. (2016) (BLL). BLL solve and estimate a novel New Keynesian framework with two key departures from the prototypical model. The first allows for changes in the conduct of monetary policy that take the form of shifts in the parameters of the nominal interest rate rule. Such changes are conceptually distinct from those generated by a monetary policy shock, an innovation in the policy rate that is uncorrelated with inflation, economic growth, and shifts in the policy rule parameters. The second allows the evolution of beliefs about long-term trend inflation to be potentially influenced by both an adaptive learning component as well as a signal about the central bank’s inflation target, with the belief rule disciplined by observations on survey expectations of inflation over time.

With the estimates in hand, one may identify movements in real variables that are attributable solely to the conduct of monetary policy—that is, to regime changes in the policy rule. These estimates imply that changes in the conduct of monetary policy generate large and persistent fluctuations in the short-term real interest rate that last for decades, in contrast to monetary policy shocks, which have far more transitory effects. The reason is that expectations of inflation are found empirically to be highly adaptive, and as a consequence, the central bank must spend a long time “convincing” households that the policy rule has changed. One interpretation of the evidence on central bank announcements is that these announcements are, in part, noisy signals about the possibility of a regime change in the conduct of monetary policy.

This evidence also speaks to the question of why real interest rates have been declining for decades. Specifically, almost all of the downward drift in the real interest rate since 1980 can be explained by regime changes in the conduct of monetary policy. This happens because the policy rule parameters exhibit a decisive shift toward more hawkish values around the time of Paul Volcker’s appointment to the Federal Reserve, but then exhibit an equally decisive shift back to more dovish values in the aftermath of the near collapse of Long Term Capital Management, the tech bust in the stock market, and the 9/11 terrorist attacks. The conduct of monetary policy has remained dovish since, with the exception of a brief interlude from 2006:Q2 to 2008:Q2. Finally, shifts to a more dovish policy rule are associated with declining equity return premia, consistent with a “reach for yield” in equity markets.

As earlier, important unanswered questions remain. Why does the central bank change the conduct of monetary policy in the first place? One possibility is that it does so in part in reaction to markets ( Cochrane and Piazzesi 2002 , Cieslak and Vissing-Jørgensen 2017 ), leaving us in a circuitous loop. Why is a more dovish monetary policy associated with a decline in equity return premia? The BLL model is silent on the mechanisms that could explain their finding in this regard. As a start, the literature could look to recent intermediary-based frameworks with a banking sector ( Drechsler et al. 2018 , Piazzesi and Schneider 2015 , Piazzesi et al. 2018 ). But we must keep in mind that equities are not the heavily intermediated asset class for which reach-for-yield-type phenomena are typically documented. On the contrary, a significant fraction of the equity market is held by wealthy households and retail investors. To fully understand these findings, we must ultimately account for their role too, whatever that may be.

Most capital invested in financial markets flows through the hands of intermediaries. 16 While there is little disagreement that intermediation frictions have some impact on asset prices, 17 and perhaps in particular during times of financial stress, it remains unclear how much various agency, behavioral, and regulatory frictions matter quantitatively.

Recognizing the potential importance of institutional investors, and motivated by recent asset pricing theories featuring intermediaries, a vibrant empirical literature emerged that tests the Euler condition of a particular group of intermediaries using an empirical proxy for their marginal value of wealth, such as the leverage of broker-dealers. 18

However, by testing the Euler equation of a particular group of investors, we can at best establish that their asset demand curve is correctly specified, but not their importance for asset prices. For instance, we do not learn how asset prices would change if we were to shock the leverage of broker-dealers. To make progress on the central question of how much intermediaries matter for asset prices, we need to impose market clearing and understand the asset demand curves of all investors, that is, the asset demand system.

The modern asset demand system consists of households allocating capital to various intermediaries, such as mutual funds, pension funds, and insurance companies and to financial markets directly. Likewise, intermediaries invest in other intermediaries (for instance, pension funds invest in mutual funds) and allocate capital directly. An important goal of asset pricing is to understand investors’ capital allocation decisions, that is, the asset demand curves of households and intermediaries.

5.1 Intermediary asset pricing and beyond: A demand system approach

To estimate models and to test theories of the asset demand system, it is natural to use data on portfolio holdings. This modeling approach to asset pricing and macroeconomics has its roots in the 1960s and 1970s (see, for instance, the work by Brainard and Tobin 1968 ), and has been revived recently by Koijen and Yogo (2019) (KY19).

A central question is how investors’ demand responds to price changes and to changes in asset characteristics, and how investors substitute across various assets and asset classes. For instance, if an intermediary is forced to liquidate some of its holdings due to a binding constraint, we need to know how much prices have to fall for other intermediaries and households to step in to ensure that markets clear.

Two major obstacles in the earlier asset demand literature that resulted in its long period of hibernation were limited data on portfolio holdings as well a lack of instruments to credibly estimate demand elasticities and cross-elasticities.

The first obstacle has been resolved with the improved disclosure of portfolio holdings by institutions over time in many countries, such as, for instance, the 13F filings in the case of U.S. equities. A quick look at these data reveals several basic facts, as documented in KY19, that appear puzzling in the context of modern portfolio theory. First, institutions hold relatively few stocks. The median institution holds 67 stocks in the period from 2015 to 2017. Second, these choice sets are quite persistent over time, even as prices and asset characteristics change. Third, investors’ portfolio holdings, across stocks, are not well explained by standard characteristics that capture risk and expected returns. The residuals, labeled latent demand, are important drivers of prices 19 , and changes in latent demand are important drivers of returns.

To address the second obstacle, we need an instrumental variable. While the Euler equation approach allowed the asset pricing literature to sidestep thorny identification questions, this is no longer possible if one is interested in estimating the demand system. The identification challenge is no different than in the fields of industrial organization or macro-economics. 20 KY19 propose to exploit the exogenous variation in investment mandates to isolate an exogenous component of demand, but it may be possible to construct other instruments. Identifying demand elasticities is a central goal in this literature.

By taking a demand system perspective to asset pricing, a coherent research agenda emerges. For empiricists, the goal is to credibly estimate demand curves for different investors and to uncover which investor characteristics matter, such as institutional type, size, funding structure, regulatory environment, agency frictions, and informational differences. In estimating demand, it is important to explain both the extensive (that is, explaining the sparse and persistent choice sets of investors) and intensive margin (that is, the determinants of latent demand). Existing theories suggest that latent demand may be related to heterogeneity in beliefs or constraints, and it may be interesting to explore whether observable measures of beliefs (such as those from analysts or surveys) or constraints can explain latent demand.

In terms of asset pricing theory, an important avenue for future research is to develop models that explain which agency, behavioral, or regulatory frictions may give rise to sparse portfolios, low elasticities of demand, and volatile latent demand. By taking a structural approach, portfolio holdings can also be used to directly test these theories and to quantify the importance of various frictions. Part of the research agenda can be decentralized by studying a group of intermediaries in isolation, such as mutual funds or pension funds, if more granular data are available.

In summary, a successful asset pricing model must explain not only prices, but both prices and quantities, including portfolio holdings and flows, 21 as is common practice almost anywhere else in economics. Interestingly, the recent work on demand systems suggests that investors do not behave as our models suggest, and understanding the quantitative importance of such deviations may deliver valuable insights to improve our asset pricing theories. Moreover, by understanding investors’ demand, asset pricing becomes more measurable and tangible, and we can hopefully reduce the “dark matter” in modern asset pricing theories ( Chen et al., 2019 ). Instead of abstractly referring to “arbitrageurs,” “intermediaries,” and “noise traders” in our theories, we actually know who they are, what their asset demand curves look like, how large they are, and what their contribution is to fluctuations in asset prices.

5.2 Broader implications

The benefits of developing an asset pricing model that is consistent with prices and quantities extends beyond the academic curiosity of asset pricing researchers. Indeed, many of the salient policy and regulatory questions involve quantities: What is the impact of large-scale asset purchases by central banks? What would happen to credit spreads if a large fraction of BBB bonds are downgraded? What is the impact of growing environmental, social, and governance (ESG) mandates on asset prices? What is the impact of changing the risk regulation of banks or insurance companies? The recent COVID-19 crisis has highlighted once more the importance of being able to answer these questions quantitatively.

Understanding the asset demand system is essential to provide credible answers to these questions. Elasticities and cross-elasticities are often not targeted directly in modern asset pricing models, yet these models are used to address these questions involving large changes in portfolio holdings.

Furthermore, the asset demand of investors depends on firms’ characteristics such as payout policy, leverage, investment and innovation policies, and profitability as they capture growth expectations and the riskiness of future cash flows. By combining models of the asset demand system with models of corporate decision making, 22 we obtain an integrated model of asset pricing and corporate finance that should target to explain asset prices, investors’ portfolio holdings, macro quantities, and firms’ corporate policies. 23

Obviously, this research agenda using asset demand systems only just (re)started. But given the wealth of data on portfolio holdings that is available across countries and asset classes, there is a lot of scope to make progress on this key question in asset pricing using a demand system approach.

Intermediary asset pricing (IAP) seeks to understand the role of financial intermediaries in explaining fluctuations in asset prices. 24 It shifts the focus from a household’s Euler equation, as in consumption-based approaches to asset pricing, to the pricing condition of a trader in a financial intermediary. In so doing, IAP elevates factors such as regulatory and corporate financing considerations that affect financial intermediaries. The big questions in this research agenda are: How much do intermediaries matter for asset prices? In which markets are these effects most pronounced? In which states of the world are the effects most pronounced? What are the central underlying factors driving intermediaries’ effects on asset prices, and how do they vary across types of intermediaries?

IAP should be seen as a branch of heterogeneous agent approaches to asset pricing ( Constantinides and Duffie 1996 ; Heaton and Lucas 1996 ). In these models, all households are on their consumption Euler equations. However, households differ in their preferences and endowment risks. As a result, factors such as the cross-sectional distribution of household income shocks and the wealth distribution drive asset market returns.

In IAP, the financial investments of some households are directed through intermediaries into asset markets. The trader at the intermediary is on her investment Euler equation, while the household who delegates investment to this trader may or may not share this Euler equation. Other households that directly invest in asset markets are on their Euler equations. The action in IAP theory is about the wedge between the Euler equations of the household that delegates and the trader at the intermediary.

Let me turn to corporate finance. A financial intermediary is a firm, with decisions made by the workers (management, traders, etc.) at the firm, accountable to the firm’s shareholders, and raising financing from equity and debt holders. An additional stakeholder, the government, looms large and affects decisions in many financial firms.

If the Modigliani and Miller (1958) propositions apply to this firm, then the intermediary is but a veil and the IAP model collapses back to the heterogeneous agent asset pricing model that focuses on the delegating households. If, however, Modigliani-Miller fails, then the separation between ownership and control has relevance for asset prices. The content of the IAP model is in the specification of how Modigliani-Miller fails. Asset demand from intermediaries then is a function of these frictions. In general equilibrium, asset demand from intermediaries plus asset demand from the direct-investing households clear the asset market.

There have been a number of well-developed specifications that trace the failure of Modigliani-Miller, borrowing from corporate finance theory, to equilibrium asset prices. I will mention two. He and Krishnamurthy (2013) develop an agency-theoretic model where the trader at an intermediary must be provided incentives when making trading decisions. This type of model shows that information frictions, such as what may arise when the trader is responsible for complex trading strategies or what may worsen during turbulent periods, will affect intermediary asset demand and equilibrium asset prices. In the agency-theoretic model, the stake of the insider (management, trader) can alleviate information frictions; thus, metrics that track this stake—for example, past returns of the firm - will affect asset prices. The agency-theoretic model also gives rise to a constraint on raising outside equity finance since such finance may dilute the stake of the insider. Thus, we can understand why equity capital and regulatory capital may affect asset demands. Andersen et al. (2019) develop a model where debt-overhang distorts the investment decision of the trader at a financial intermediary. The trader makes decisions to maximize value to shareholders, but given debt-overhang, such decisions differ from the frictionless benchmark. They show that traders under debt-overhang require a minimum return, roughly equal to the financial firm’s credit spread, to purchase assets. Their approach allows one to understand the high returns on even near riskless trades in the last decade, and the comovement between such returns and financial firms’ credit spreads. In this model, the debt-overhang friction reduces the private incentive to raise equity capital. Thus, the model also speaks to why regulatory capital requirements can affect asset demand and prices.

I view this theoretical research as a work in progress. Opening the box of the financial firm suggests that many other considerations may matter for the trading decisions of the firm. Capital allocation within firms, career concerns of traders, search for yield, and benchmarking effects all seem like considerations that may be of importance. Sorting out which of these are first-order, and in which asset markets and states-of-the-world, is the research agenda that needs to be completed.

There is by now ample evidence that Modigliani-Miller fails for financial firms, and this failure meaningfully affects asset prices (see He and Krishnamurthy 2018 for a review of the evidence). Yet there is heterogeneity within intermediaries. We need to understand better the impact of these failures on different intermediary types and in different asset markets. Commercial banks are players in loan markets, bond markets, and derivatives markets. They are financed largely by deposits, some of which is subject to government insurance and regulatory constraints. Broker/dealers are active in derivatives and market-making activities across a broad array of asset markets. They are financed in short-term funding markets including wholesale money markets and repo markets. Hedge funds engage in complex trading strategies across a range of asset markets. They are financed by insiders’ wealth, outside equity capital, and repo. Placing structure on the “asset demand” functions across these different types of intermediaries, as in the approach taken by Koijen and Yogo (2019) , can shed light on the underlying factors driving trading at these intermediaries. This too is an important research agenda, and will draw on tools from asset pricing, industrial organization, and corporate finance.

Finally, I will mention a parallel stream of research to IAP in macroeconomics. There is a large literature that studies how intermediation frictions affect credit extension, via loan supply/loan rates, and then through such channels affect aggregate investment and consumption. See Brunnermeier et al. (2011) for a survey. If intermediaries are an important driver of asset prices, then we should expect that they will also affect macro quantities. This observation has two further implications. On the theoretical side, the consumption of the direct investing household I alluded to earlier will also be affected by intermediary frictions. This point is often lost in models with exogenously specified agent endowments, as is typical in asset pricing models. On the empirical side, the data studied by macroeconomists offer further moments for intermediary asset pricing models to match. Thus, connecting IAP and macroeconomics is another important avenue in this research agenda.

Twenty years ago, international finance and finance were far apart from each other. 25 Over the past 20 years, a remarkable convergence took place. Will these two fields share a common path in the future, or will they diverge in order to meet specific challenges? I will use this question to organize my thoughts on the future of international finance.

Despite these fundamental similarities, there are differences of emphasis between the two fields. First, heterogeneity is more central to international finance than it is to finance. Heterogeneity simply cannot be avoided in international finance. In some sense, international finance starts with two countries and two investors. Second, frictions are more central to international finance than they are to finance. Indeed, and despite decades of international financial integration, there are many more frictions in financial markets across countries than within countries. Third, currencies are more central to international finance than they are to finance. It does seem to make a big difference that assets are traded in one currency or another. Fourth, the role of governments is more central to international finance than it is to finance. Governments interfere in many different ways in the trading of assets across countries, more so than within countries.

Turning to the future, it is clear that there are both common and specific long-standing problems that are likely to occupy the two fields in the coming decades. Common long-standing problems include the identification of the economic determinants of beliefs |$\mathbb{E}_{t}$|⁠ , the economic determinants of risk premia or equivalently of stochastic discount factors |$X_{t,t+1}$| and |$X_{t,t+1}^{*}$|⁠ , the economic determinants of portfolios. They also include what I will call the “disconnect” problem: the fact that it seems difficult to connect the stochastic discount factor |$X_{t,t+1}$| to an actual preference-based marginal rate of substitution |$MRS_{t,t+1}$| of a well-identified marginal investor between periods |$t$| and |$t+1$|⁠ , or the real exchange rate |$E_{t}P_{F,t}^{*}/P_{H,t}$| to the actual preference-based marginal rates of substitution |$MRS_{F,H,t}$| between foreign goods |$F$| and home goods |$H$|⁠ . 26 Other common long-standing problems include the identification of the key market failures and externalities (fire sales from financing constraints, aggregate demand externalities from nominal rigidities, search externalities, etc.) as well as the role and transmission of policy (monetary, fiscal, prudential, etc.). 27

Third, there is the large degree of home bias in portfolios across countries. Fourth, there are the destabilizing effects of volatile capital flows in emerging markets. Fifth, there are the economic determinants of government behavior in these countries. Sixth, there are the economic determinants and implications of exchange rate regimes (fixed exchange rates, floats, managed floats, etc.). 30 Seventh and finally, there are the importance of the international monetary system and the special role of the United States as the preeminent issuer of safe and liquid reserve assets, its associated role as the world banker and the exorbitant privilege that comes with it, and the resulting pattern of global imbalances. 31 How long will the dollar continue to dominate as a reserve and invoicing currency? How long will the United States keep playing a dominant role as a world lender of last resort through its network of swap lines? When will we transition to a more multipolar world? Will that transmission be smooth or turbulent? Will the supreme reign of the dollar come to an end in a Triffiin event similar to that which brought the end of the Bretton Woods system of fixed exchange rates? 32

The two fields are actively tackling these challenges. In doing so, they sometimes take a common approach to deviate from the first and simplest models that rationalized the fundamental equations. For example, both fields now make ample room for market segmentation; recognize the role of financial intermediaries; incorporate financial constraints, illiquidity, and runs; try to capture irrationality and speculation; and allow for inattention. 33 International finance also sometimes breaks away from finance to address some of the central problems that are specific to the field. For example, it makes more room for nominal rigidities and political economy frictions such as limited commitment and the like. A question that arises, as the two fields deviate from the simple unifying paradigms of the fundamental equations, is whether they will splinter into a collection of local explanations to local questions, or whether new and richer global paradigms will emerge. Another is whether the two fields gravitate toward similar local and global theories. It seems that they would probably have a better shot at the truth if they did.

It seems clear that both fields will benefit from the arrival of large quality micro-datasets. It is a safe bet that it will force the fields to recognize the importance of heterogeneity and frictions even more so than they do today. In international finance, new data already allows us to grasp a new reality on disaggregated capital flows, portfolios, and balance sheets, and to recognize the importance of financial derivatives. 34 Similarly, we are beginning to gain an understanding of the heterogeneity and dynamics of beliefs. 35 And we are also making progress on the nature of nominal rigidities using data on disaggregated prices. 36 Similar trends are at work in finance. In the two fields, reduced-form empiricism will be a temptation, and the question is whether new and better structural theories will emerge to explain all this data.

I explore how recent advances in asset pricing have contributed to our understanding of exchange rates, and I outline promising areas for future research, using the United States’ role in the international financial system as a test case. 37

8.1 Exchange rate valuation and decomposition

The exchange rate level reflects a cash flow component, the interest rate differences, and a discount rate component, the currency risk premia. All else equal, an increase in the foreign country’s interest rates will cause the foreign currency to strengthen, that is, to appreciate against the dollar, but an increase in the currency risk premium will cause the currency to depreciate.

Importantly, real interest rate differences across countries are quite persistent. Some countries have persistently low interest rates, and other countries have persistently high interest rates ( Lustig et al. 2011 ; Hassan and Mano 2019 ). According to the high interest rate currencies also need to have high currency risk premia, so that the persistent component of the interest rate differences is (partly) offset by the persistent component of the currency risk premia, if real exchange rates are to be stationary. In other words, Switzerland and Japan have to convince global bond market investors to accept negative currency risk premia to keep their currency from weakening, while Australia and New Zealand need large and positive currency risk premia to keep their currency from strengthening.

This implication of the exchange rate valuation model is borne out by the data. Investors earn an unconditional currency carry trade risk premium by going long in currencies that have high interest rates on average, even if they do not condition on current interest rates ( Lustig et al. 2011 ; Hassan and Mano 2019 ). This is often referred to as the unconditional version of the currency carry trade. In equilibrium, investors have to believe that long positions in low interest rate currencies, such as the Swiss franc and the Japanese yen, offer insurance against aggregate risk that is priced in global securities markets, while high interest rate currencies expose their portfolios to more global risk.

High interest rate currencies depreciate in bad states of the world. Lustig et al. (2011) find that high interest rate currencies depreciate when volatility in global stock markets increases. 38

8.2 The role of the United States in the international financial system

In the typical carry trade pattern of global capital flows, the United States looks like an outlier. The United States is an example of a country with low real interest rates that has been running persistent current account deficits. The United States has accumulated a large, negative net foreign asset position against the rest of the world as a result. The composition of the United States balance sheet against the rest of the world is unusual. The United States borrows by issuing Treasury bonds and other safe assets, and then the United States takes a long position in risky foreign assets. As a result, the United States manages to make money on its negative net foreign assets position. This has been referred to as the “exorbitant privilege” of the United States.

The insurance view. In the neoclassical complete markets benchmark, the exceptional role of the United States has a natural interpretation. The United States is the world’s disaster insurance provider. The United States insures the rest of the world against adverse shocks, as is argued by Gourinchas and Rey (2007) and Gourinchas et al. (2010) . In equilibrium, the least risk-averse investor, in this case the United States, insures other investors against adverse shocks. When disaster strikes, the insurance contract calls for large net transfers from the United States to the rest of the world. To generate these transfers, the dollar depreciates in real terms. 39

The insurance view of the United States role in the international financial system faces two main challenges. I will use the great financial crisis (GFC) of 2007–2008 to highlight these challenges. First, it is not at all clear that there were large net transfers from the United States to the rest of the world during the GFC. There was a striking collapse of global trade during the GFC. Any cross-country transfers that did occur were probably smaller than what would be predicted by the model. Second, the dollar tends to appreciate in the case of large adverse shocks to the global economy. This is exactly what happened during the GFC.

The safe asset view. In the safe asset view, the United States is different because it is the sole supplier of the world’s safe assets, and the dollar is the world’s reserve currency ( Farhi and Maggiori 2018 ; Caballero et al. 2008 ; Gopinath and Stein 2018 ; Caballero and Krishnamurthy 2008 ).

Part of the discount rate component in the exchange rate valuation component has been relabeled as a convenience yield term. An increase in the convenience yield |$\lambda^{\%,\ast}_{t+\tau}$| that foreign investors derive from their holding of dollar-denominated safe assets, relative to the same yield on foreign safe assets |$\lambda^{\ast,\ast}_{t+\tau}$|⁠ , causes the dollar to appreciate instantaneously. This prediction has empirical support. The variation in the U.S. Treasury basis has explanatory power for the dollar exchange rate; other bond bases do not have the same explanatory power for other bilateral exchange rates. Part of the discount rate component we measured earlier is now recast as a convenience yield component.

Jiang et al. (2018) find that these extra convenience yields foreign investors earn when holding Treasury bonds are larger than 2 |$\%$| per annum. More than 90 |$\%$| of this convenience yield is attributable to the dollar exposure, not the safety of Treasury bonds, consistent with recent evidence that international bond investors seem to be subject to dollar bias ( Maggiori et al. 2018 ).

This safe asset model has radically different implications from the benchmark model. When the world economy experiences an adverse shock, the flight-to-safety will tend to cause the dollar to appreciate. During the onset of the great financial recession in 2008, the dollar appreciated by 30 |$\%$|⁠ , and the United States generated a larger dollar amount of seignorage revenue from the sale of Treasury bonds and other safe assets. In case of a global crisis, there is a net transfer from the rest of the world to the United States.

In this safe asset model, international capital flows can obviously be destabilizing. For example, safe asset demand creates an incentive to produce more dollar-denominated safe assets in the United States, potentially giving rise to excessive leverage in the United States in the run-up to the GFC. In other countries, especially emerging market countries, issuers have an incentive to issue bonds denominated in dollars. This in turn gives rise to currency mismatch. Much more empirical work in the coming years is needed to explore and test the implications of these two different paradigms, particularly their implications for exchange rates.

This article summarizes the perspectives of Markus Brunnermeier, Emmanuel Farhi, Ralph S. J. Koijen, Arvind Krishnamurthy, Sydney C. Ludvigson, Hanno Lustig, Stefan Nagel, and Monika Piazzesi. Each subsection reflects the view of one of the authors only and this will be indicated at the beginning of each subsection.

1 The introduction is by Ralph S.J. Koijen and Sydney C. Ludvigson.

2 This subsection is the perspective of Stefan Nagel.

3 See, e.g., Timmermann (1993) , Lewellen and Shanken (2002) , Collin-Dufresne et al. (2016) .

4 Lochstoer and Muir (2019) show that subjective volatility perceptions can explain a number of asset pricing puzzles.

5 Giglio et al. (2019) is a recent example of work that looks at this question.

6 See, e.g., D’Acunto et al. (2019) for recent research of this kind.

7 Collin-Dufresne et al. (2017) , Nagel and Xu (2019) , and Wachter and Kahana (2019) are recent examples of such research.

8 As an example, work by Kozlowski et al. (2020) suggests that beliefs about disasters could play a role.

9 This subsection is the perspective of Monika Piazzesi.

10 An attractive feature of the Survey of Professional Forecasters conducted by the Philadelphia Federal Reserve is that survey respondents are anonymous, which reduces the importance of career concerns. Bluechip survey respondents are not anonymous but are serving a wide range of clients who may be either long or short in fixed income assets, which also mitigates these concerns.

11 This subsection is the perspective of Markus Brunnermeier.

12 This subsection is the perspective of Sydney C. Ludvigson.

13 Chen et al. (1986) , Lettau and Ludvigson (2001) , Parker and Julliard (2004) , Koijen et al. (2017) , Bansal et al. (2016) , and Ghosh et al. (2016) .

14 Breeden et al. (1989) , Campbell and Mei (1993) , Lewellen and Nagel (2006) , Roussanov (2014) , and Herskovic et al. (2019) .

15 Hanson and Stein (2015) , Gertler and Karadi (2015) , Boyarchenko et al. (2016) , Jarocinski and Karadi (2020) , Cieslak and Schrimpf (2019) , and Kekre and Lenel (2019) .

16 This subsection is the perspective of Ralph S.J. Koijen.

17 The recent work on covered interest parity deviations provides an example ( Du et al., 2018 ).

18 See, for instance, He and Krishnamurthy (2013) , Adrien et al. (2014) , and He et al. (2016) .

19 Koijen et al. (2019) study how much the demand of various investors, differentiated by type, size, and activeness, matters for equity valuations and long-horizon expected returns.

20 For recent advances on identification in macroeconomics, see Nakamura and Steinsson (2018) and Gabaix and Koijen (2020a) .

21 See Gabaix and Koijen (2020b) for a dynamic model of flows, quantities, and asset prices to understand the volatility of movements in the aggregate stock market.

22 A complementary literature develops heterogeneous agent asset pricing models with both an intermediary and a household sector as well as various corporate finance policies; see, for instance, Elenev et al. (2018) . Due to computational limitations, such models can currently only handle a limited amount of heterogeneity, a small number of assets, and the models are calibrated rather than estimated. However, this literature is developing rapidly, and advances in machine learning may enable researchers to estimate larger-scale models in the near future.

23 See, for instance, Baker and Wurgler (2004) .

24 This subsection is the perspective of Arvind Krishnamurthy.

25 This subsection is the perspective of Emmanuel Farhi.

26 See, e.g., Itskhoki and Mukhin (2017) and Lilley et al. (2019) ,

27 See, e.g., Jeanne and Korinek (2010) , Bianchi and Mendoza (2010) , Bianchi (2011) , and Farhi and Werning (2016) .

28 See, e.g., Itskhoki and Mukhin (2017) .

29 See, e.g., Du et al. (2018) .

30 See, e.g., Ilzetzki et al. (2017) .

31 See, e.g., Gourinchas and Rey (2007) , Caballero et al. (2008) , and Gourinchas et al. (2010) .

32 See, e.g., Farhi and Maggiori (2018) , Gopinath and Stein (2018) , He et al. (2019) , and Gourinchas et al. (2019) .

33 See, e.g., Gabaix and Maggiori (2015) and Itskhoki and Mukhin (2017) .

34 See, e.g., Maggiori et al. (2018) and Coppola et al. (2020) .

35 See, e.g., Giglio et al. (2019) .

36 See, e.g., Gopinath et al. (2020) .

37 This subsection is the perspective of Hanno Lustig.

38 Menkhoff et al. (2012) find that baskets of high interest rate currencies depreciate when global FX volatility increases. Lettau et al. (2014) find that the downside betas of high interest rate currencies are higher.

39 Maggiori (2017) reinterprets this insurance arrangement by imputing a central role to financial intermediaries. In Maggiori’s model, United States’ intermediaries are better equipped to hedge against adverse shocks than their foreign counterparts. In this risk-sharing arrangement, the United States can run large and persistent current account deficits in anticipation of surpluses during rare disasters. Similarly, Chien and Naknoi (2015) develop a model with heterogeneous agents in which the United States has a larger mass of sophisticated traders than the foreign countries. Their model has similar predictions.

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Spatial Variability of the ‘Airbnb Effect’: A Spatially Explicit Analysis of Airbnb’s Impact on Housing Prices in Sydney

Over the last decade, the emergence and significant growth of home-sharing platforms, such as Airbnb, has coincided with rising housing unaffordability in many global cities. It is in this context that we look to empirically assess the impact of Airbnb on housing prices in Sydney—one of the least affordable cities in the world. Employing a hedonic property valuation model, our results indicate that Airbnb’s overall effect is positive. A 1% increase in Airbnb density is associated with approximately a 2% increase in property sales price. However, recognizing that Airbnb’s effect is geographically uneven and given the fragmented nature of Sydney’s housing market, we also employ a GWR to account for the spatial variation in Airbnb activity. The findings confirm that Airbnb’s influence on housing prices is varied across the city. Sydney’s northern beaches and parts of western Sydney experience a statistically significant value uplift attributable to Airbnb activity. However, traditional tourist locations focused around Sydney’s CBD and the eastern suburbs experience insignificant or negative property price impacts. The results highlight the need for policymakers to consider local Airbnb and housing market contexts when deciding the appropriate level and design of Airbnb regulation.

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Valuation problems, such as valuation inaccuracies/variations, client influence, and the use of heuristics, are common problems in property valuation practice globally. These problems have generated debate in recent times under the rubric of “behavioural issues in valuation”. This paper examines valuation problems in developing countries, as well as the current efforts that are undertaken to address these problems, with a view of determining the best approach to explain and/or address them. This stems from the persistence of valuation problems despite efforts undertaken to improve the practice of valuation. The study involves a survey of registered and practising valuers in Kenya. Respondents were asked to indicate valuation problems in practice, adopted strategies, and recommendations to address the said problems. It emerged from the study that valuation problems not only result from valuer misconduct but also market-related problems/the valuation environment in developing countries. The study further found that efforts to address these problems are mainly focused on improving valuer conduct while neglecting market-related problems (problems related to the nature of the valuation environment in developing countries). Based on these findings, the study concludes that valuation problems in practice are better understood in the context of both categories, i.e., valuer conduct and market-related problems, and recommends a holistic approach to address these problems by categorising them appropriately.

Consistency and Fairness of Property Valuation for Compensation for Land and Improvements in Zimbabwe

Abstract Property valuation for compensation of expropriated properties in Zimbabwe has been characterised by inconsistencies for decades. Previous studies have noted that displaced people are dissatisfied with the compensation paid by the expropriating authority. Even though many academic works were done on expropriation and compensation in Zimbabwe, issues surrounding consistency in property valuation practices and fairness of compensation paid remain unresearched. Thus, the purpose of this paper is to close this gap. Data for this study were collected through primary sources (questionnaire surveys to members of the compensation committee, private property valuers, designated property valuers and former commercial farmers) and secondary sources (literature surveys including a review of statutes, official reports, books, journals, and newsletters). Findings reveal that there is inconsistency in property valuation for expropriation, no clear legal definition of what constitutes fair compensation, and that views on the fairness of the compensation paid for expropriated properties in Zimbabwe are divergent. The study suggests that there is a need to review existing expropriation and compensation laws in Zimbabwe to create consistency in practice, thereby improving the fairness in the amount of compensation paid to the displaced person(s).

The economics of property valuation

Barriers, drivers and prospects of the adoption of artificial intelligence property valuation methods in practice, export citation format, share document.

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• Published: 06 June 2023

Firm value in the airline industry: perspectives on the impact of sustainability and Covid-19

• Yaghoub Abdi   ORCID: orcid.org/0000-0002-7551-3068 1 ,
• Xiaoni Li 1 &
• Xavier Càmara-Turull 1  

Humanities and Social Sciences Communications volume  10 , Article number:  294 ( 2023 ) Cite this article

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To date, there has been limited research undertaken into firm value determinants in the air transport industry, one of the most essential sectors for global business. In view of this, in this study, we review and synthesise the literature that focuses on the value of firms in this sector and discuss conceptually and empirically the determinants influencing airlines’ stock values. Our main objective is to widen our understanding of the current state of research on the firm value of air transport companies. Using the systematic literature review (SLR) approach, we classify 173 papers published from 1984 to 2021. We find considerable changes in academic interest in the topic over the time period analysed, especially as a consequence of crisis-induced market crashes. In addition, we classify the main research themes relating to airlines’ market value, identify gaps, and introduce potential future research avenues in this area. Among the themes identified, the adjustment in the industry-level factors such as alliances, market structure and competition were the most common source of fluctuations in airlines’ stock value. However, we find shifting to sustainability initiatives and its consequence for stakeholders’ value as one of the most discussed topics in this context. The trend has gained attention since early 2020 due to the emergence of the Covid-19 pandemic as companies are looking for green and sustainable ways to protect the value in crisis time. Our findings assist transportation researchers and executives in addressing major value drivers of airline firms.

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Introduction

The airline industry is arguably among the world’s most widespread, fastest growing and rapidly rising industries, providing a wide variety of services for people worldwide (Belobaba et al., 2009 ). In recent years, air transport has become one of the primary modes of travel. This is confirmed by the International Civil Aviation Organisation (ICAO), which reported that in 2019 the number of air travellers rose to 4.5 billion (ICAO, 2019 ). While the industry was hit hard by the COVID-19 pandemic, suffering dramatic falls in the rate of the services provided and passenger numbers, there are evidence that the industry is recovering (McKinsey, 2021 ). According to the International Air Transport Association (IATA), in 2021 the overall figure was 47% of the number of passengers in 2019 (IATA, 2022 ), with expected increases to 83% in 2022, 94% in 2023, 103% in 2024 and 111% in 2025. Therefore, a continuation of this growth rate would mean passenger demand doubling over the next years.

Since its establishment, many aspects of air transport have undergone structural reforms involving technological advances (e.g., the emergence of commercial jet aircraft in the 1950s and the design of wide-body jumbo jets in the 1970s), administrative procedures (e.g., the deregulation process starting in the US in 1978) and financial aspects (the continual growth of airline firms listed in various stock exchanges on a global scale) (Belobaba et al., 2009 ; Malighetti et al., 2011 ). The trend follows the general acceleration of supply chain companies to gain a competitive advantage (Azadian, 2020 ). These changes have re-shaped the industry, altering previously fundamental characteristics and markedly changing perspectives in the industry, leading to an environment of market competition (Cook, 1996 ). Since starting to finance their operations from stock markets rather than relying on state support, issues such as the use of financial analyses on stock returns, cost-of-capital, and the valuation of assets and securities have become crucial (Malighetti et al., 2011 ). In this regard, the industry has been transformed into a much riskier one (Vasigh et al., 2020 ). To remain efficient, it is clear that air carriers must seriously consider financial management and the economic environment in which they operate.

Given that the ultimate objective of financial management for today’s value-minded executives is value creation and maximising shareholders’ profits (McKinsey, 2020 ), firm value has become an important consideration in stakeholders’ financial attitudes. Market value offers meaningful insight into the valuation of a company and can assist both airline executives and investors to determine the airline’s financial status. Due to the importance of this parameter, studies have focused on several factors affecting the value of airlines. However, a review of the literature shows that academics have paid insufficient attention to valuation studies (Malighetti et al., 2011 ). While many reviews have been carried out in the air transport context (Matias Ginieis et al., 2011 , 2012 ; Khudhair et al., 2019 ; Mardani et al., 2015 ; Kaps and Phillips, 2017 ; Kalemba and Campa-Planas, 2011 ; Campa-Planas and Kalemba, 2017 ; Spasojevic et al., 2018 ; Wang and Gao, 2021 ; Sun et al., 2021 ; Duval, 2013 ; Raza et al., 2020 ; Papatheodorou, 2021 ), no comprehensive and generic review of the contextual factors that affect a firm’s valuation has yet been carried out.

An analysis of academic contributions, however, shows that firm value has often been linked to different business strategies and policies, and is thereby influenced by several factors. This poses the question as to what the value determinants are and how they can be classified. The systematic review conducted by Pereira et al. ( 2021 ) appears to be the only study to map out the value-creating business activities in the aviation industry. The authors identified 114 value-creating innovations and how they add value to the industry. Based on the results, initiatives relating to efficiency, convenience, portfolio differentiation and sustainability stood out. Taking a more comprehensive overview, the present study spans determinants such as firm and industry level factors, health crises, political and economic stability, customer relationship, etc. as value drivers to identify the content relationships and approaches, and to explore the gaps in the literature. This topic is extremely important given that the COVID-19 pandemic plunged the entire industry into crisis and destroyed stakeholder value, making it crucial to fill any gaps. We theoretically document the concept of firm value, then gather related studies, commenting on the factors involved, which are grouped into corresponding themes. This approach enabled us to unpack the nature of drivers of firm value and to recognise how these elements have been represented in academic contributions, finally indicating future research directions. From this perspective, the present study sought to answer the following questions:

How has the literature on firm value evolved in the air transport industry?

What are the main research directions relating to firm value in the industry?

What are the main influential factors of firm value in the industry?

In what context has the research focus shifted from traditional value drivers to sustainability initiatives?

What are the niches for forthcoming academic investigations on this topic?

The current study answers the aforementioned study questions by means of the systematic literature review or SLR method, and via thematic scrutiny of the relevant information, using the WoS Core Collection-Clarivate & Scopus databases. There is limited documentation on review studies in the field of air transportation, with existing reviews able to be divided into two streams. First are the articles focusing on the theoretical and empirical study of the air transportation domain. They notably consider specific factors such as air travel demand (Wang and Gao, 2021 ), revenue management (Raza et al., 2020 ) and service quality (Kalemba and Campa-Planas, 2011 ) and safety (Campa-Planas and Kalemba, 2017 ), or they organise the study around the COVID-19 pandemic (Sun et al. 2021 ). Other studies revolve around reviewing the interrelationship between the airline industry and tourism (Papatheodorou, 2021 ), and the role of airlines in economic development (Lenaerts et al., 2021 ).

The aim of our review was to systematically identify and discuss the progress of academic research into firm value in the air transport context, including how this has changed over time in terms of the number of papers focusing on the topic, the host (journals) for publication, and the most productive researchers, countries, etc. The aim of this investigation was to provide a multidisciplinary analysis of the ongoing discussion on firm value in the airline sector by building bridges among different perspectives and identifying the factors impacting firm value. In addition, by mapping out these value drivers we could establish a broad discussion of problem-based solutions to the construction of knowledge from the management perspective. A summary of the identified gaps in the literature, the research questions, and the contribution of the present study is presented in Table 1 .

The paper is structured as follows. Section “State of the art” presents a theoretical framework of the main research; the section “Method” describes the research methodology, sample selection and search strategy; and the section “Assessment of the selected publications” reports the results of the review. The paper ends with some conclusions, policy reflections and research limitations.

State of the art

This section briefly reviews the business environment of airlines, the concept of a firm and its internal value factors, as well as external factors influencing market value.

Airline sector

The global aviation industry is a worldwide service provider and has a fundamental responsibility in the establishment of the world’s economy (Belobaba et al., 2009 ). The industry assumes this role through its activities and impact on related industries. Airlines receive a huge volume of funds and recruit thousands of people, all of which contribute to innovation and economic growth. Air transport is also a driver of globalisation. It improves standards of living by widening leisure and cultural experiences for the people, helps to enhance living standards, and advances sustainability via facilitating tourism and trade. The development of air transport and its technical advancements and service performance make it one of the major means of building of modern world (ICAO, 2017 ).

In order to improve the performance of the industry, it is critically important for airline companies to operate efficiently, the most efficient ones can usually offer lower prices and consequently attract more passengers (Assaf and Josiassen, 2012 ). In addition, more efficient companies can use the benefits of scale by having a larger number of travellers which contributes to better global recognition and image for their brand. Traditionally, air transport has generated some of the lowest returns across business sectors. According to the 2020 year-end economic report by the international air transport association (IATA Economics, 2020 ), even before the current pandemic crisis, equity holders had failed to gain an adequate return for their finances. As shown in Fig. 1 , IATA documents the divergence of returns on invested capital (ROIC) against the weighted average cost of capital (WACC), for the years 2007–2021. The function of ROIC is to measure the financial efficiency of a firm in which how well the invested capital under its control has been allocated to produce profitable investments. It also shows how well a company’s money has been utilised to generate revenue (Lee, 2019 ). One can see that the companies operating in this sector have rarely generated revenues as high as the WACC for the industry as a whole.

Return on capital invested in the wole airlines industry was lower than cost of capital.

Market value is an attempt to estimate the value of a property under open market conditions (Pagourtzi et al., 2003 ). In other words, it refers to the price of an asset at which a supplier and a buyer would agree to change its ownership. An agreed market value satisfies both the seller and the buyer and usually refers to the stock price of publicly listed trading companies. Stock price variation, therefore, represents a percentage change in a firm’s market value at any given time and is driven by supply and demand. Market value is estimated by applying valuation methods that reflect the nature of the asset and the underlying environment in which it could be traded in trade markets (Pagourtzi et al., 2003 ).

Book value is an accounting concept that measures the value of a firm using assets as they are recorded on a balance sheet. It represents the wealth of a company in assets as well as the value of the company’s stockholder equity, as registered on a balance sheet. Book value is considered the sum of a firm’s historical records of assets and liabilities together with documented costs and revenues carried forward to future periods (Boulton et al., 2003 ). Investors are particularly interested in the association between market and book value, considering shares selling well above the book value a target for overvalued portfolios, and those selling below it as undervalued portfolios. Market value and book value together help the company to produce insights into its business prospects. However, due to its ability to instantly reflect the growth or collapse of a firm, the market value provides a better indication of investors’ expectations regarding its business prospects. Some specific applications and issues are addressed using the book-to-market ratio, which compares the original cost of the asset and the firm’s market value as calculated by its market capitalisation. It is an important firm-level indicator of a company’s returns, irrespective of size and the geographical location in which it is operating (Cakici and Topyan, 2014 ). It dramatically highlights any growing discrepancies between book value and market capitalisation (Boulton et al., 2003 ).

Asset valuation theories have long been of interest to both investors and academics in finance. The literature on firm valuation highlights the discrepancy between a firm’s market value and its book value by means of the present value of future abnormal earnings. In this context, book-to-market value (B/M) reflects the investors’ estimate of a firm’s abnormal earnings. The Fama-French ( 1996 ) three-factor model argues that a large proportion of the discussed CAPM average-return anomalies in the literature such as size, book-to-market ratio, earnings/price, cash flow/price and past sales growth are related, and their model is capable of addressing them. Size, book-to-market ratio and excess return on the market are the three main elements of the model introduced by Fama-French. These factors are used as small minus big (SMB), high minus low (HML) and the return of the portfolio minus the risk-free rate of return. The same authors also introduced the size effect and the B/M effect as two behavioural anomalies. These two effects argue that small firm stock tends to have a higher return than large firm stock, and that firms with a high B/M (when the market value is significantly lower than the book value) tend to have continuously low earnings, respectively. Meanwhile, a low B/M (when the market value is significantly higher than the book value) is a signal for sustainable profit growth (Fama and French, 1995 ). In other words, a negative difference between stock price and book value signals a potential impairment, specifically when the discrepancy exists for a long period (Bini and Penman, 2013 ). Conversely, when the stock price exceeds the book value, it can be assumed that the firm is able to generate revenues, or its stocks have a higher market value.

This issue has been discussed in the associated literature, which reveals that calls were made to reform accounting standards, the conventional historical cost approach having outlived its usefulness (Boulton et al., 2003 ). These calls resulted in a transition from an industrial to a fundamentally knowledge-based approach. Based on this method, intangible assets are considered the new drivers of economic activity (Skinner, 2008 ; Canibano et al., 2000 ). The valuation of intangible assets has become a significant contemporary discussion point for researchers in different fields of human knowledge in their attempt to identify relevant intangibles for management purposes and firm valuation (Fazzini, 2018 ; Lim et al., 2020 ; García-Ayuso, 2003 ). In this regard, the OECD ( 2012 ) argues that in the studies on this topic, better management is positively correlated with the disclosure of intangibles and financial performance.

Internal factors influencing firm value of airlines

Works by Li et al. ( 2004 ) and Malighetti et al. ( 2011 ) investigate the industry-level value determinants for airlines. Both studies suggest a range of possible determinants influencing firm value. For their part, Malighetti et al. ( 2011 ) collected data from 87 airlines and 24 airport companies to test the value relevancy of a broad range of variables, as summarised in Fig. 2 , showing that many internal factors including the above-explained items affect firm value. The variables considered were categorised into four potential value determinants to regress against firm value. The four value driver categories provided information about the financial status of the firm, the type of ownership, and industry-specific and control variables. Drivers of shareholder wealth were introduced into this framework, based on extensive previous literature highlighting the structure of the market, the role played by the network (e.g., flight frequency, size of the aircraft, number of routes under competition, and market share on both local and worldwide scales) and the type of business model (i.e., low-cost, or full-service) chosen by the airline. They found that the ownership structure has a direct relationship with firm value: a higher degree of ownership concentration is associated with a higher market value. This is probably due to the greater tendency to maximise firm value under these conditions. This outcome is consistent with the general industry view asserting a positive link between state ownership and both efficiency and return for firms operating with higher levels of debt and a higher equity ratio (Le and O’Brien, 2010 ). It also concurs with the results of the research conducted in this paper since the airline industry is a capital-intensive sector with a high debt-to-equity ratio. Conversely, the study found that firm size and age are negatively correlated with the firm value of airlines.

Internal annd external factors influencing market value of airlines.

External factors influencing firm value of airlines

The aviation industry is extremely sensitive to external economic, political, and social factors due to its heavy dependency on a wide range of business and industrial support. Government policies, regulations, mass media, and customers are parties having an impact on airlines’ market value. Due to their significance in terms of these factors and particularly due to bilateral trade agreements (Haanappel, 1980 ), the majority of international airlines (except in the United States) were in control of governments until the mid-1980s when this type of airline was deemed to be the best model ensuring the growth of industry (Belobaba et al., 2009 ). Therefore, the industry is highly connected with politics and government elites. Almost all state-owned air carriers suffer from distressed state airline syndrome which is a political and organisational virus affecting this type of airline due to issues such as substantial losses (e.g. large volume of collected debts, undercapitalised and indirect subsidies that hide real losses), over-politicised, bureaucratic management, poor service quality, etc. (Doganis, 2001 ).

A sizeable portion of the industry involves cross-border operations so that not only the domestic market but also economic conditions around the globe, affect the industry’s performance. In this regard, academic research has widely reflected the issues and claims that establishing strategies to utilise their firm-level strengths and neutralise external weaknesses may build sustained competitive advantage (Porter, 1985 ; Song et al., 2021 ). In this regard, the term contagion or herd behaviour is used to describe the transmission of instability or unexpected phenomena in one industry(country) to another because of trade, financial or other economic linkages between them (Gillen and Lall, 2003 ). A wide range of disasters, terror attacks, earthquakes, and aircraft crashes are highlighted in the literature as having implications for firms’ financial decision-making (Fernandez-Perez et al., 2021 ).

Studies also considered the consequences of health crises like the SARS and the current Covid-19 pandemic on firm performance. Such outbreaks challenge health care, economic, and financial systems worldwide. The problem is more severe for the airline industry because the shutdown significantly restricts people’s movement decreasing passenger demand for flights. Financial market uncertainty will be triggered by negative sentiment in the operation and business environments. Therefore, airlines lose their value in global financial markets. To summarise, this study categorises a variety of factors that changed considerably and have implications on the value of the firm. These factors range from political, environmental and social factors to the public health situation.

Systematic review is a methodology that finds existing studies, picks contributions, analyses and synthesises data, and reports to reach out clear conclusions about existing knowledge and unidentified aspects of those publications (Denyer and Tranfield, 2009 ). Alternatively, according to the document published by the Centre for Reviews and Dissemination (CRD), the method aims to identify, assess and outlines the findings of all relevant research papers to facilitate the accessibility of available evidence to policy-makers (CRD, 2009 ). It also serves in two fundamental ways; identifying gaps to suggest future research avenues and providing key information as a framework (Kitchenham, 2004 ). Therefore, reviewing the literature is an important part of every research topic. Moreover, a profound understanding of the necessary processes and skills and owing experience in the respective field are compulsory to conduct a literature review (Fisch and Block, 2018 ).

Based on the work by Tranfield et al. ( 2003 ), the systematic literature review method helps to avoid the biases of traditional reviews of the current body of literature. This method allows the researcher to summarise the to-date literature relevant to the topic of interest; analyses the topic from various perspectives; and, finally, provide reliable insight from a pool of knowledge dispersed across a broad range of studies. SLR could be very useful to evaluate available information and subsequent understanding in responding to the research objective (Kitchenham, 2004 ). In this study, based on the guidelines defined in CRD ( 2009 ) and Denyer and Tranfield ( 2009 ), the method was applied to provide a reference on international academic research related to firm valuation in the air transport industry.

Search strategy

We followed three steps to execute the SLR. The first was to set up keywords and perspective combinations of those keywords in the search. Inclusion and exclusion criteria for papers found constitute a second phase to adjust the relevance of each study to the current research paper concern. In this phase, we further evaluated selected papers based on certain characteristics. Finally, meta-analysis of selected papers, such as year-wise distribution of selected studies, identifying the most productive author, geographical setting, and co-authorship maps as well as keyword co-occurrence analysis, was a third step. We further provide a thematic analysis of sampled articles to elucidate the main research strands in the field.

Databases and software

To carry out the review, we used documents published in journals indexed in Scopus and WoS Core Collection-Clarivate. Both databases are very well known in academia and host thousands of contributions annually. Specifically, we selected Scopus to apply keyword combinations since it is considered a large abstract and citation database for peer-reviewed literature. According to Elsevier’s website ( 2021 ), Scopus provides extensive citation search results and updates scholars and institution profiles automatically, creating a rich connection between researchers, released ideas and institutions. Clarivate Analytics’ Web of Science is also one of the most important databases, offering comprehensive citation search and analytical information tools. It holds a prominent position in its association with scientific products and features across different knowledge domains (Li et al., 2017 ). Consequently, both databases are useful tools to conduct systematic reviews (Campa-Planas and Kalemba, 2017 ; Calatayud et al., 2016 ; Li et al., 2017 ; Bergiante et al., 2015 ). Using the R 3.6.0 programming package, Vosviewer and Microsoft Excel, we analysed the sampled publications to determine the evolution in the published papers over the period. Figure 3 summarises the search process.

Our initial result of 572 documents was filtered by including only peer-reviewed articles and those related to the research theme at the airline context, resulting in 173 eligible documents. (author created).

Keyword identification and sampling

As a search strategy highly contributes to a methodical extraction of papers, it is critical to determine which terms to use in the process of searching to find relevant articles and to determine how these will be specified during the strategy. The approach undertaken is based on the main research questions to encompass potentially relevant academic contributions. Particularly, based on the background reading and subject heading brainstorming, we selected the relevant keywords to ensure that the search is comprehensive considering different spelling, synonyms, variants of keywords and related concepts. As shown in Table 2 , we used a total of eight keywords to develop the search strings: ( book value , market value , firm value , stock market , valuation , air transport , airline , and aviation ). These keywords were formulated to run in both databases as: “book value” OR “market value” OR “firm value” OR “stock market” OR “valuation*”) AND (“air transport*” OR “airline” OR “aviation”. In this formula, based on the guideline by Gu and Lago ( 2009 ) the Boolean operators of OR/AND have been used between keywords to allow synonyms and to link two clusters of terms, respectively. Also, to extend the range of possible studies the study uses an asterisk at the end of some keywords, somewhat different keywords for the same concept are used in some studies (Wilding et al., 2012 ). The aim of the phase was to retrieve articles having the most relevant keywords in their title, abstract, or keyword sections for further assessment of eligibility and inclusion.

Inclusion and exclusion criteria

In this study, we used the following criteria to select relevant articles among those found in the review and filtered any non-compliant studies out of the sample. The first criterion was that the article be published following a peer-reviewed process. Therefore, we eliminated publication forms such as book series, conference proceedings, book reviews and working papers. The second criterion was that the sampled studies had to investigate the book value or market value of one or more airlines. Next, we filtered for only articles with editorial lines related to research areas: business economics, economics, business, transportation, business finance, management, and hospitality leisure sport tourism in WoS together with including articles within business and economics subject areas in Scopus. We also included articles in the fields of environmental studies and hospitality leisure tourism to cover work related to sustainability and its effect on firm performance and the value of airlines in the WoS Core Collection-Clarivate database. It is worth noting that we did not control other potential factors such as open or closed access, year, language, etc when searching in neither WoS nor Scopus databases. This means that all studies, having any firm valuation or performance measures appearing as a search result of the above-defined keywords and consistent with the criteria, are included in the analysis. The final step was to merge both file results in the biblioshiny package (using R-Studio), after removing duplicates to achieve the final sample. The review identified a total of 572 empirical studies as follows: 411 peer-reviewed, 98 conference papers, 10 conference reviews, 16 reviews,16 books and book chapters, and a further 21 documents classified as early access (9), short survey (6), note (2), business article (1), preprints (1), and erratum (1). Contributions that failed to satisfy the inclusion criteria, such as the conference papers and the book chapters, were filtered at this stage. The strategy yielded a final sample of 173 articles.

Assessment of the selected publications

The assessment of the selected articles is divided into two sections: a descriptive analysis and a thematic analysis. The former provides a quantitative description, summarising the features gleaned from the information obtained regarding the performance of authors and countries, year of publication and a keyword co-occurrence network. For its part, the thematic analysis emphasised the identification and interpretation of the organisation of the studies in the sampled articles based on the similarities and tendencies found.

Descriptive analysis

Evolution of the number of academic papers.

Figure 4 illustrates the evolution of the selected academic articles between 1984 (the year the first related article appeared in a database) and 2021. It shows that significant changes in publications took place, with ever-growing numbers of articles annually and an evolution in the publishing pattern, implying that interest in the topic has undergone significant changes. The upward trend is especially obvious between 2008 and 2018, with 91 of the 173 (52%) articles published in this period. The trend is even more evident in the last 3 years of the period, with 39 articles published. It is worth mentioning that scientific interest in the topic is in line with the ongoing status of global business. During the period 2008‒2018, the world was suffering and recovering from a financial crisis, making stock market volatility a popular topic. However, following the full recovery of the market in 2017, the issue of firm value became less significant, hence we can observe a decrease in the number of published papers in 2018 and 2019. The topic drew renewed attention in 2020 and 2021 because of the COVID-19 pandemic, which threw the airline sector into the darkest period in its history. With the sharp decline in demand and activity in the industry, airline values have fallen dramatically. In this context, the scholarly discussion to find managerial orientations that can ensure the protection of stakeholders’ wealth is unsurprising. In this regard, there is an urgent need for research to address the effect of support measures to overcome the crisis, such as suspending some business operations to reduce costs, relief on taxes and charges and the design of proactive strategies for governments to tackle the sharp changes in oil prices.

The figure shows the significant changes in term of the number of articles being published on the joint field of airline industry and firm valuation over time.

Most cited papers and sources

Some statistics from the selected papers are shown in Tables 3 and 4 . Notably, we report the most cited publications and the most productive sources, obtained from the systematic review process. The number of citations illustrates the impact of all the articles, authors and journals. The idea behind the statistics is to assess which paper has received the most attention in academia by adding the number of citations for all the articles published by each author. We also report the most productive and the most cited journals.

Tables 3 and 4 show that the most cited article is by Kang et al. ( 2010 ), entitled “Impacts of positive and negative corporate social responsibility activities on company performance in the hospitality industry”, receiving a total of 312 citations. The Journal of Air Transport Management is the journal with the highest number of publications, with 23 papers, and it also has the highest number of citations. This is to be expected since this journal is the specialised resource for all air transport issues. In terms of the number of publications, the next resource is Transportation Research Part E: Logistics and Transportation Review with seven publications, while the Journal of Financial Economics has the second-highest number of citations. These two journals issued 30% of the papers included in the sample, with the remaining journals publishing just one paper each, demonstrating that the sampled papers are not uniformly distributed among different journal publications.

Geographical scope

We also analysed the authors’ country of affiliation to identify the spatial distribution of the present research topic. Figure 5 depicts the countries contributing to the topic, with the USA as the top contributor (62 papers), followed by China (30 papers). Spain (16 papers) is the third most interesting country on the topic of valuation in the airline industry. Based on the review, the topic is North America/Europe-centric, with 74% (129 papers) of attention to the topic coming from these two continents.

Spatial distribution of sampled articles showing that USA has been the most productive country.

Co-authorship analysis among authors

Scientific collaboration could be defined as the cooperation that takes place within a social context between two or more researchers, which facilitates the sharing of meaning and the fulfilment of tasks relating to a mutually shared goal (Sonnenwald, 2007 ). In other words, co-authorship as an ongoing procedure could be characterised as separated segments that motivate people to exchange expertise, skills and information (Samitas and Kampouris, 2017 ). From an academic perspective, co-authorship advances innovation in knowledge transfer in the transition phase to an innovative partnership between universities (Chen et al., 2013 ), encouraging researchers to work together to intensify the quality and quantity of published articles (Samitas and Kampouris, 2017 ).

We used the full counting mode to identify the data selection and thresholds. To apply the method, we considered an author’s finite number of documents as 1 (minimum number of edges). Figure 6 shows the collaboration network of authors, illustrating the interplay between scholars in this field. In this figure, the number of publications by each team determines the size of the boxes. The distance between the two boxes is interpreted as an indication of the intensity of the relationship between authors (Shi and Li, 2019 ). The shorter the distance between two authors, the stronger they have co-authored with each other. When specific authors collaborate closely, their respective nodes are thicker and closer. Connected authors are commonly grouped together. For example, the cluster consisting of Zhang A, Hu Q, Zhang Y, Czerny A, Park J-H, Park N-K have collaborated closely, usually conducting joint research. Zhang A obtained the highest total link strength among the authors, having taken part in five research projects.

The largest connected co-authorship network in the dataset, analyzed using VOS clustering. The figure shows a weak collaboration among researchers publishing at this field.

The size of the nodes in Fig. 6 is directly dependant on the number of publications, while the different colours connect the different authors. From the distribution of the clusters in the graph, we can conclude that the authors have a weak collaboration tie and are barely connected, possibly indicating that the researchers attach importance to establishing more collaborative relationships. To this effect, the information flow will have a higher propensity to diffuse throughout the field.

Keyword co-occurrence analysis

The ‘co-occurrence analysis’ provides the network of conceptual relations from the perspective of researchers in the field. By placing the words in context, and in relation to other terms and concepts, the co-word map can be seen as a semantic representation of knowledge structures (Tijssen and Van Raan, 1994 ). It involves the co-occurrence of words defined by the researchers in the articles, and those defined by professional indexers. The co-occurrence of keywords happens when two or more words appear together in a research study. Figure 7 (constructed by the VosViewer software) contains the keyword co-occurrence analysis of the terms firm valuation and air transport . Some points relating to the proximity between nodes, their size and the thickness of the lines between them must be considered to interpret the figure. Regarding size, the bigger the node [word], the larger the weight. The degree of relationship among the words is also shown by the distance between nodes. A shorter distance generally means a strong connection, while a thicker line reflects a greater co-occurrence between terms.

The frequently co-occurring keywords, themes, or topics in research in the firm value at the airline context. The figure highlights three words “airline”, “finance”, and “managment” with the highest frequency in the sampled articles.

Sixteen main keywords with a minimum of four occurrences appeared on applying the co-occurrence network, highlighting the dominance of air transport and stock market terms. As expected, the word airline had the highest frequency among the analysed documents. The second most frequent concept among the sixteen terms was value , reflected in many forms such as firm value, stock price, stock market, price, valuation, market and performance. The collection of papers, therefore, stands out as a more cohesive body of literature when the subject is valuation. Accordingly, and from the joint analysis of the figure, readers can appreciate that management , finance , airline , valuation , policy , analysis , industry , stock , analysis and return are popular words when addressing valuation in the air transport context. These keywords are shown in five clusters represented by different colours. Specifically, management , finance , airline industry and stock returns are the most prominent keywords to represent the topic over the whole period. The involvement of keywords with higher centralities is because the issue dealt with by the literature is primarily a managerial one, and second because the subject is highly oriented towards the area of finance. Third, the academic interest focused specifically on the airline industry in the period was substantial, although this key term was less important than the two research keywords listed above (Management and Finance).

Thematic analysis

Based on an initial reading of the 173 sampled papers, the influence of seven major groups of factors on airline value can be defined based on major themes. Notably, this categorisation is a purely manual classification of the sample since no coding method was used. It appears that the entire transportation industry is highly influenced by the impact of system risks due to a broad range of external factors, including financial events (recession, fuel price change, etc.), natural calamities (hurricane, tsunami, polar vortex, etc.) and man-made disasters (war, terrorist attack, etc.) (Deb, 2021 ). Almost all these studies use quantitative methods to approach the topic. This is not surprising since for our sample we tested two-factor associations (firm value and another quantifiable variable such as oil price, implementing sustainability standards, etc.) and it is a variance question in nature (Hermundsdottir and Aspelund, 2021 ; Van de Ven, 2007 ). Table 5 provides the detailed categorisation and references for the main research themes.

Industry-level characteristics

We identified the subject of inconsistencies in industry-level performance, including mergers and acquisitions (M&A), aircraft crashes, alliances, inter-industry competition, etc., as the most popular topic to study the effect of these actions on firm value (37 papers). Some of these topics were widely covered in the sample. For instance, Wassmer and Meschi ( 2011 ) studied the impact of code-sharing alliance formations and terminations on the stock price of airlines, finding that the stock market reacts to these incidents. The next notable determinant is oil price volatility, which significantly impacts the operation of firms in this industry. Wang ( 2013 ) extracted three main reasons from the literature as to why the oil price is an important factor for airline firms. Their first two reasons are based on the discounted cash-flow model, highlighting a firm’s future cash-flow as a value influencer: (a) oil is an important natural resource in economic activities, influencing costs and expected cash-flows and (b) rises in oil prices leads to inflation. If the price increase is met by an anti-inflationary policy (i.e., a rise in interest rates) from the central bank, higher interest rates cause discount rate incensement, which ultimately has an adverse effect on the stock price for the firm. The third reason is that a rise in oil prices will increase commodity prices, which will ultimately lead to diseconomies of scale. In all cases, oil price increases magnify the operating costs for airlines and reduce their profits (Mollick and Amin, 2021 ). Therefore, there is a connection between oil prices and stock market returns for airlines. Furthermore, and specifically, within this category, factors such as competition, co-specification, merging, aircraft crashes, bankruptcy, accidents and the market structure have been mentioned as value influencers, matching the theory discussed by Malighetti et al. ( 2011 ). These contributions provide insights into the association between asset prices and changes in these value driver factors, which may be of interest to researchers, industry practitioners, financial managers and decision-makers. Large-scale transport for long-distance travellers has tended to be seen as a cost-benefit calculation for policy recommendations (Kristoffersson et al., 2021 ).

Firm-level value influencers

Studies in this category mainly analyse changes in firm-level performance, including an airline’s business model (low-cost and full-service), firm demand for hedging, new route announcements, bankruptcy protection, etc., as the second most popular topic to study the effect of these actions on value (36 papers). For example, Kökény et al. ( 2021 ) explore whether the stock market performance of European airlines influences their business model, finding that an airline’s business model provides insight for investors into what type of market reactions can be expected in the various stages of an operation. This also enables investors to utilise appropriate criteria and financial metrics, while making investment decisions. This means that full-service airlines show significantly better performance than their low-cost carrier counterparts when crises hit and stock markets are devastated. This hypothesis gains support from Deb ( 2021 ), who documented that two small-scale services Compass Airlines and Trans States Airlines, as well as Virgin Australia, filed for bankruptcy due to the COVID-19 pandemic. Also in this category, factors such as flight and network efficiency, launching a mobile app, leasing choices, oil refinery, CEOs’ strategic risk-taking behaviour and technical efficiency have been mentioned as value influencers, which are matched with the theory discussed by Malighetti et al. ( 2011 ). For example, in an interesting study, Manuela et al. ( 2016 ) investigate Delta Airlines’ oil refinery acquisition strategy to hedge against rising fuel prices and its effect on its financial and operational performance. The results of the study indicate that the strategy positively impacted Delta’s income and that this was rewarded on the stock market via higher share prices following the acquisition announcement.

Sustainability

Many studies (31 out of 173) evidence the association between sustainability activities and firm financial performance and value. The articles with a corporate social responsibility theme bring together topics related to concerns arising from issues linked to firms’ environmental, social and governance responsibility. Due to the heterogeneity between terms referring to environmental, social and governance issues, in the present paper we use the term sustainability to represent these issues. Over the last few decades, the topic has not only gained interest among academic researchers, industry practitioners and investors but also among policymakers. It has been described as a voluntary corporate commitment to operate aligned with broader society’s expectations, rather than just traditional profit-making corporate behaviour (Casado-Díaz et al., 2014 ). This means that firms are encouraged to contribute to the sustainable development goals (SDGs), which could be achieved by developing strategies that integrate sustainable practices into their normal daily operations, with the aim of reaching their own sustainability (Escrig-Olmedo et al., 2019 ). This corporate responsibility is described as activities to proactively contribute to the sustainability agenda from all its financial, environmental and social perspectives. The corporate sustainability domain also covers a firm’s internal business operations and productions, management and strategy, organisational units, and marketing and communications with its stakeholders (Escrig-Olmedo et al., 2019 ; Lozano, 2015 ). These studies link a firm’s financial performance and value and the level of commitment to sustainability standards in terms of professionally managing issues such as resource use, emissions, innovation, the employee-shareholder relationship, management and the board. These studies suggest that sustainability issues influence a firm’s market value as well as the financial performance of air transport companies (Abdi et al., 2021 ), which will be consequently reflected in the stock market.

Customer relationship and marketing

The next sub-group focuses on the relationship between airlines and their customers. Consumer behaviour has long been analysed in the economic literature, appearing first in the transportation literature at the beginning of this century (Pan and Zuo, 2020 ). For airlines, consumer behaviour and especially the behaviour relating to airline choice is considered an important element in planning and is a basis for their strategies (Munoz and Laniado, 2021 ). This research strand emphasises the significance of managing the association with customers and its influence on firm value. Given the highly competitive nature of the global airline business, airlines implement a range of actions to satisfy and stay connected with their main customers, the passengers. These activities include marketing strategies (e.g., from social media activities to internal operations to improve safety and service quality). Based on the findings of these studies, market value modifications are expected due to changes in the level of passenger satisfaction, investment behaviour tendencies and wage concessions (Sun and Kim, 2013b ). Operators and practitioners must consider these dimensions and attributes because these items are influential to the overall perceived quality of a firm’s services (Ojo, 2017 ).

International political and economic instability

Changes in the political and economic situation significantly affect the market value of airlines. We use political and economic instability as an umbrella for studies in this domain since changes in firm value are rooted in the current political and macroeconomic situation. On this basis, this stream of literature mainly discusses contributions to a firm’s market value and the reaction to volatility due to worldwide political and economic factors such as terrorist attacks, and the transmission of these shocks to the market. For instance, although oil price volatility is an economic phenomenon, significant changes in supply and demand are usually triggered by political disturbances such as terrorist attacks in oil producers or importer countries (Mollick and Amin, 2021 ). Although shocks of this nature affect various industries at both country and international levels, they are disproportionately felt by insurance companies and tourism (including airlines). The issue was well reflected by IATA director and CEO Pierre Jean Jeanniot a year after the 9/11 terrorist attack when he said that “we have lost more in a year than we have made in our entire history. This is an industry that is now in a deep hole. We must start looking for footholds and ways to climb quickly out of the financial abyss” (Drakos, 2004 ). These papers mainly reflect the increased uncertainty of the industry following such incidents. For instance, Gillen and Lall ( 2003 ) studied how trade linkage and airline alliances are important in the transmission of global economic shocks to market value, finding the negative impact of shocks such as the 9/11 attacks on the mean abnormal returns of airlines.

New Method to predict share price

Apart from the studies having internal and external value drivers in focus, there are contributions that introduce new methodologies to increase the prediction accuracy of stock prices. Notably, most of the studies in this sub-category could fit in the internal factor category, but since they spotlight the innovative aspects of introduced stock price forecasting rather than the correlation between specific variables with firm value, we decided to present them in a new category. Since stock exchange mechanisms are complex and the market is influenced by seasonal factors, it has been proposed that multiple components are likely to impact the estimation of models, including financial data, the way to extract those data, optimisation algorithms and prediction model parameters (Zheng and He, 2021 ). Therefore, the selection of model features could consider both technical and fundamental characteristics. To this effect, when the share price is stable attention is directed to technical features. However, in times of high fluctuations, fundamental features may be a priority. Therefore, using long-term historical data is suggested for operating airlines since they are likely to produce more accurate analyses (Zheng and He, 2021 ). The models developed in this category could enhance the accuracy of a firm’s valuation and assist investors in making timely decisions for their financial strategies and business operations.

Health crisis

The next factor influencing the market value of airlines was found to be the global health crisis. Severe acute respiratory syndrome (SARS) and COVID-19 are contagious diseases that threaten human life. Risks like these disrupt business operations in infected countries, irrespective of the industry. Given the contagiousness of COVID-19, infected countries adopted various measures to limit contact (such as stopping unnecessary movement outside the home and public transport, closing schools and universities, and strict social distancing measures). Restrictions such as these immediately affected the economy, with the airline industry the first to suffer due to the dramatic drop in passenger demand. Because of the COVID-19 outbreak, the market value of airlines shrank significantly (Maneenop and Kotcharin, 2020 ). Studies in this cluster mainly use the event-study model to analyse the influence of disasters of this kind on online stock prices. This method is popular in economics and finance for investigating the effect of news related to a particular event on stock market prices (Maneenop and Kotcharin, 2020 ).

By contrasting the situation before and after the outbreak, and the stock returns for the airline industry and the whole market return (Maneenop and Kotcharin, 2020 ), these studies investigate the extent to which the firms in this industry may suffer (Liew, 2020 ), how severe the impact is and what the impact on stock price volatility may be (Deb, 2021 ). Different events and daily data sets were selected in these studies, including crucial announcements such as days-from-first-case reports by China (January 13, 2020) and the USA travel ban announcement by President Trump (March 11, 2020). The findings of these studies are also interesting. In this regard, Liew ( 2020 ) observed the rapid decline in profit of airlines and tourism-related businesses by monitoring statistics derived from three leading consolidators, namely hotel accommodation, airline tickets and travel service services. Deb ( 2021 ) finds that COVID-19 had an unprecedentedly severe effect on the stock price movements of airlines. This author further proposed a method to predict the market reaction to similar events, especially in the short term. Last, Maneenop and Kotcharin ( 2020 ) find that airline share prices reduce more significantly than the whole return of the market. All three studies resulted in major changes in airline valuation theory. To this end, it is necessary to design a strategy to alleviate the economic side effects of the pandemic in the airline industry.

Summary, implications, and future research avenues

Through the categorisation of value determinants of airline companies, the current study provides an approach to linking the theoretical concepts and practical findings by structuring them into seven main subject areas. In summary, we find that the topic is especially in demand as a result of the COVID-19 pandemic, which has driven a radical shift in scholarly productions focused on the value issue for airlines. Looking at the sources of publication of these papers, the Journal of Air Transport Management (with 23 papers and 126 citations) is shown to be the most frequent and the most cited journal. The geographical scope analysis for authors’ affiliation showed the USA (62 papers) as the highest contributing country, followed by China (30 papers). As far as co-authorship among authors is concerned, Zhang A et al. were the biggest cluster of authors working closely together. However, most authors in this field tend to work separately. The analysis of keyword co-occurrence indicates that, as expected, airline is the most cited keyword among terms in this context.

Regarding the thematic analysis, we find that the largest group of studies examines the industry and firm-level factors. The third largest group of papers focuses on modern sustainability initiatives and relations with firms’ financial performance. Based on the findings of these studies, a drastic stock market reaction is to be expected as a result of any changes at the level of airlines’ corporate, environmental, social and governance responsibilities. The next sub-group is composed of studies that examine the impact of changes in the political and financial status of an airline on its market value performance. These contributions suggest that variations in factors such as political instability, terror attacks, oil price shocks and jet fuel prices may lead to market volatility. Issues related to the customer and marketing strategy, health threats and firm-industry level value determinants were also found as main themes in our dataset.

By analysing themes, we found that there is evidence of a shift in academic contributions to sustainability initiatives and their consequences for stakeholders’ value because the airline industry is regarded as one of the most challenging when it comes to environmental impact and sustainability issues (McManners 2016 a, 2016 b). However, this finding does not correspond to reality. According to Heeres et al. ( 2018 ), only 38% of the top 100 airlines publish their sustainability report. This may be due to uncertainty as to whether environmental sustainability is compatible with financial sustainability.

Regarding passenger issues, an interesting finding is that customer satisfaction has grown since the idea of low-cost airlines has become widespread, especially in the new millennium. As would be expected, following the deregulation of the industry, more commercial and market-leading perspectives became the norm, and knowledge of passengers and their preferences gained interest in academia (Spasojevic et al., 2018 ). There has also been a noticeable shift in scientific articles centring on the value issue, especially since 2020 with the output of COVID-19-related academic publications, totalling 31 (over 17% of sampled papers). Such considerable academic attention stands out in the tourism and hospitality field, including airlines (Chen et al., 2022 ). Studies in this domain have highlighted the need for policy designs to alleviate the impact of the pandemic on the airline industry as the one most damaged by COVID-19. However, the literature has failed to conclude with tangible practical solutions to protect the value when a negative event occurs.

Study implications

This paper contributes to the knowledge surrounding firm value determinants from both academic and industry perspectives. In terms of the first, despite the importance of valuation issues for firms, contributions to the tourism literature (including the airline context) remain scant. Considering the lack of review works as a response to an apparent gap, our study approaches the issue objectively by collecting the data from reputable journals in the WoS Core Collection-Clarivate & Scopus database. By doing so, we contribute to identifying and classifying the important value driver and influencer factors, helping to fill this gap and bring new insights by overviewing the relevant literature trends through the synthesis of the available documents. Additionally, a focus on the airline industry, which is one of the most important and rapidly growing industries, could contribute to the current body of knowledge with significant first-hand insights. To conclude, adding an in-depth systemic tendency to the wide divergent literature available in the field could benefit future researchers interested in air transportation business valuation and analysis.

The above consideration leads us to the second theoretical contribution of this study, which is the aggregation of existing knowledge on the topic of firm value in this context. Future researchers can find support for each of the concepts categorised among the analysed documents. The empirical findings confirm the theoretically anticipated firm-level financial and non-financial value drivers, together with external factors influencing the market value. This approach encourages researchers to recap understanding of the firm value topic through a new categorisation that can help to identify conceptual and empirical relations between main value-related subject areas. We observed that current contributions have turned their attention away from classic external and internal value drivers toward modern corporate social responsibility issues.

Systematic literature reviews can provide a reliable basis to formulate decisions and take action (Tranfield et al., 2003 ). In this regard, executives may also use the results to see how firm value issues are dealt with in the literature and benefit from the empirical results when designing business strategies and making decisions. Air carriers’ large-scale operations need a notable level of use of resources, and every major decision has an implication for a company’s financial returns, which will be reflected in its share value. This is understandable given the limited level of available resources for a company and the need to respect efficiency when allocating these restricted resources. Further, these companies should also bear in mind their competitors’ profiles and offering to customers (Chih et al., 2021 ). In practice, however, there is sufficient evidence of the inefficiency of airlines’ business strategy. To put this in perspective, Nzuva ( 2020 ) discusses how the majority of airline firms are suffering from low levels of profit, which hampers the industry from expanding. To this effect, business model modification is vital to meet airlines’ long-term growth.

Taking the above into account, we suggest that major changes in airlines’ value are caused by certain leading global trends such as the green finance discussion and unexpected negative events. Notably, the theme has gained momentum since the proposal of the 17 United Nations Sustainable Development Goals (SDGs) in 2015, which require allocating a firm’s resources to acquiring eco-friendly equipment, reaching higher standards for products and prioritising safety measures collectively as a corporation framework to “shift the world onto a sustainable and resilient path”. This trend has been even more in the spotlight since the outbreak of the COVID-19 pandemic since it induced a crash in firm value, causing stakeholders and institutional investors to look for sustainable profit-making shares and protect their wealth at this time of crisis. Consequently, airline managers and industry decision-makers have acknowledged the recent preferred shift towards the importance of sustainable development strategies to communicate their obligation to maximise the wealth of their stakeholders. This campaign towards sustainability also provides an opportunity to launch a sustainable development agenda, which acts as an insurance link against unexpected negative events such as health crises (e.g., the current pandemic and SARS) and international political and economic instability (e.g., the 2008 global financial crisis, terror attacks such as 9/11, among others). In this regard, our findings provide insights for managers who are considering allocating available resources to sustainability activities by adopting more efficient and robust approaches, which by comparing stock returns for the airline industry with the whole market return (Maneenop and Kotcharin, 2020 ) consider the firm’s characteristics in terms of its business model and ownership structure. For instance, in terms of investment in renewable resources, recent developments in technology may throw up potential opportunities to reduce energy consumption by utilising more fuel-efficient aircraft technology and introducing direct flight pattern networks. We suggest that managers consider these factors to act proactively under economic turbulence rather than take a reactive approach to crash value at times of crisis. This could also apply to potential policymakers, requiring firms to invest more in such initiatives to be beneficial not only for the firm but also for society in the long term.

Limitations and future research

The selective, observational, and retrospective essence of this systematic review had several limitations. First, the search terms used cannot be assumed to be fully comprehensive and capture all the relevant academic articles. This is because a broad range of keywords has been used by researchers in the literature. We restricted the search to the definite and most probable keywords to capture the most relevant studies, making it almost impossible to cover the state of the field over time in a single study. To address this issue, future research could use a literature-exploration algorithm to find an almost overwhelming number of matching documents on a research topic. The second limitation is that we considered only articles published in the WoS Core Collection-Clarivate and Scopus databases. Future reviews should include articles published in other databases such as journal citation reports (JCR).

Third, within the papers found in the review, several studies were recognised as directly unrelated and removed from the study. Future studies may need to broaden the scope of the investigation in this regard. These studies could be improved by the investigation and assessment of advanced metamodels from other contexts and compliance with new techniques to be used (Binsuwadan et al., 2021 ). Additionally, due to the outbreak of COVID-19 and the questions it has raised about valuation, contributions to measure the effectiveness of preserving actions to survive the crisis, such as cutting capacity to reduce costs, relief on taxes and charges by governments, and to propose proactive strategies for policymakers to deal with fluctuating oil prices, seem necessary. In particular, a greater focus is needed on investigating the effectiveness of fiscal policies to prevent exposures in oil-related sectors such as the air transport industry. Furthermore, given that most studies in the literature concentrate on one or just a few airlines, more studies could be carried out using larger samples that cover a variety of firms. Last, the findings of the theme analysis may encourage more research on sustainable value drivers as a promising area of research. Additional contributions to provide significant information to understand the sustainable development agenda in recognition of firm value are needed for a sustainable future of the air transport industry.

Data availability

The data that supports the finding of this study are openly available in Scopus and Web of Science core-collection Clarivate databases. We used the articles published in these two databases through the right of access by the authors’ institution. These data are also publicly available from Google searches.

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This project has received funding from European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 713679 and from the Universitat Rovira i Virgili (URV).

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Abdi, Y., Li, X. & Càmara-Turull, X. Firm value in the airline industry: perspectives on the impact of sustainability and Covid-19. Humanit Soc Sci Commun 10 , 294 (2023). https://doi.org/10.1057/s41599-023-01644-8

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Yang, Zan. "Five essays in property valuation." Doctoral thesis, Stockholm : Dept. of Real Estate and Construction Management, Royal Institute of Technology [Avd. bygg- och fastighetsekonomi, Tekniska högsk.], 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3026.

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McParland, Clare. "European investment valuation practices and implications for the harmonisation of valuation standards." Thesis, University of Ulster, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342318.

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Amidu, Abdul-Rasheed. "Expertise development in commercial property valuation practice." Thesis, Birmingham City University, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719996.

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Armitage, Lynne Audrey. "The role of property market analysis in the valuation of investment grade property." Thesis, Queensland University of Technology, 1999. https://eprints.qut.edu.au/36086/13/Lynne_Armitage_Thesis.pdf.

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Nordlund, Bo. "Valuation and Performance Reporting in Property Companies Accouding to IFRS." Doctoral thesis, KTH, Bygg- och fastighetsekonomi, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-9243.

O'Roarty, Brenna Ann. "A critical assessment of the rental valuation of retail property." Thesis, University of Ulster, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243624.

Louey, Wing-hong, and 雷永康. "Analysis of the asset valuation methods of real estate properties in the People's Republic of China and Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31251389.

Fibbens, M. J. W. "The application of personal computers to direct comparison valuation : a residential mass appraisal investigation /." View thesis View thesis, 1993. http://library.uws.edu.au/adt-NUWS/public/adt-NUWS20030610.165133/index.html.

Teang, Kanha, and Yiran Lu. "Property Valuation by Machine Learning and Hedonic Pricing Models : A Case study on Swedish Residential Property." Thesis, KTH, Fastigheter och byggande, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298307.

Sri, Navarathne Sakalashilpathilaka Laksrilal Heli Prasad Neelawala. "Asymmetric information between buyers and sellers in the residential property market: A hedonic property valuation approach." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/76412/5/S.N.S.L.H.P%20Neelawala%20Thesis.pdf.

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Scott, Ian Park. "A knowledge-based approach to the computer-assisted mortgage valuation of residential property." Thesis, University of South Wales, 1998. https://pure.southwales.ac.uk/en/studentthesis/a-knowledgebased-approach-to-the-computerassisted-mortgage-valuation-of-residential-property(85b5791b-47d5-4a6d-83e0-7c8c94b2978f).html.

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Havard, Timothy M. "Valuer behaviour and the causes of excessive variance in commercial investment property valuation." Thesis, University of Manchester, 1999. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665990.

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Van, der Byl Calven. "A statistical model for valuation of residential property in the Nelson Mandela Metropolitan area." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1020045.

Borst, Richard A. "Discovering and applying location influence patterns in the mass valuation of domestic real property." Thesis, Ulster University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.438804.

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Suen, Fun-sing, and 孫奮生. "Decision support systems for real estate evaluation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31257021.

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Boman, Anna, and Jonas Larsson. "Patent Valuation in Theory and Practice." Thesis, Linköping University, Department of Management and Economics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1578.

Background: Today, an increased need to value patents is expressed in several different situations. For example, banks more frequently accept patents as collateral for loans and patents are being exchanged more often between companies. It is argued that a hindrance for the recognition of the value of patents, and other assets lacking physical form, is that the current methods of valuation are not developed for this type of assets.

Purpose: Our objective is to investigate the practical relevance of four theoretical valuation approaches in the context of patent valuation and to point out crucial factors affecting the choice of valuation approach.

Procedure: Interviews were conducted with professionals working in the field of corporate finance and with an expert in the field of patents and intellectual property rights.

Results: The respondents are not of the same opinion whether relevant approaches for patent valuation exist at all. Among the respondents who find it possible to value patents, the income approach is the dominating approach. The theoretical correctness of this approach, derived from the definition of value, is stressed as the primary argument for the use of it. Methods such as Decision Tree Analysis, within the income approach, and Relief from Royalty, a hybrid of the market- and income approach, are used as complements.

Rajapaksa, Dewage Darshana Peiris. "Floods and property values: A hedonic property and efficiency analysis." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/86975/1/Darshana%20Peiris_Rajapaksa_Thesis.pdf.

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Godwin, Wayne. "A critical review of the approaches and attitudes of South African property valuers towards the valuation of hotels under a contemporary management agreement." Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27058.

Pienaar, Petrus Terblanche. "The use of the Discounted Cash Flow (DCF) method as a method of valuation within the South African property industry: A critical review." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/14125.

Daud, Muhammad Nasir. "Public sector information management and analysis using GIS in support of property valuation in Malaysia." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313266.

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Evaluation Research: Definition, Methods and Examples

Content Index

• What is evaluation research
• Why do evaluation research

Quantitative methods

Qualitative methods.

• Process evaluation research question examples
• Outcome evaluation research question examples

What is evaluation research?

Evaluation research, also known as program evaluation, refers to research purpose instead of a specific method. Evaluation research is the systematic assessment of the worth or merit of time, money, effort and resources spent in order to achieve a goal.

Evaluation research is closely related to but slightly different from more conventional social research . It uses many of the same methods used in traditional social research, but because it takes place within an organizational context, it requires team skills, interpersonal skills, management skills, political smartness, and other research skills that social research does not need much. Evaluation research also requires one to keep in mind the interests of the stakeholders.

Evaluation research is a type of applied research, and so it is intended to have some real-world effect.  Many methods like surveys and experiments can be used to do evaluation research. The process of evaluation research consisting of data analysis and reporting is a rigorous, systematic process that involves collecting data about organizations, processes, projects, services, and/or resources. Evaluation research enhances knowledge and decision-making, and leads to practical applications.

LEARN ABOUT: Action Research

Why do evaluation research?

The common goal of most evaluations is to extract meaningful information from the audience and provide valuable insights to evaluators such as sponsors, donors, client-groups, administrators, staff, and other relevant constituencies. Most often, feedback is perceived value as useful if it helps in decision-making. However, evaluation research does not always create an impact that can be applied anywhere else, sometimes they fail to influence short-term decisions. It is also equally true that initially, it might seem to not have any influence, but can have a delayed impact when the situation is more favorable. In spite of this, there is a general agreement that the major goal of evaluation research should be to improve decision-making through the systematic utilization of measurable feedback.

Below are some of the benefits of evaluation research

• Gain insights about a project or program and its operations

Evaluation Research lets you understand what works and what doesn’t, where we were, where we are and where we are headed towards. You can find out the areas of improvement and identify strengths. So, it will help you to figure out what do you need to focus more on and if there are any threats to your business. You can also find out if there are currently hidden sectors in the market that are yet untapped.

• Improve practice

It is essential to gauge your past performance and understand what went wrong in order to deliver better services to your customers. Unless it is a two-way communication, there is no way to improve on what you have to offer. Evaluation research gives an opportunity to your employees and customers to express how they feel and if there’s anything they would like to change. It also lets you modify or adopt a practice such that it increases the chances of success.

• Assess the effects

After evaluating the efforts, you can see how well you are meeting objectives and targets. Evaluations let you measure if the intended benefits are really reaching the targeted audience and if yes, then how effectively.

• Build capacity

Evaluations help you to analyze the demand pattern and predict if you will need more funds, upgrade skills and improve the efficiency of operations. It lets you find the gaps in the production to delivery chain and possible ways to fill them.

Methods of evaluation research

All market research methods involve collecting and analyzing the data, making decisions about the validity of the information and deriving relevant inferences from it. Evaluation research comprises of planning, conducting and analyzing the results which include the use of data collection techniques and applying statistical methods.

Some of the evaluation methods which are quite popular are input measurement, output or performance measurement, impact or outcomes assessment, quality assessment, process evaluation, benchmarking, standards, cost analysis, organizational effectiveness, program evaluation methods, and LIS-centered methods. There are also a few types of evaluations that do not always result in a meaningful assessment such as descriptive studies, formative evaluations, and implementation analysis. Evaluation research is more about information-processing and feedback functions of evaluation.

These methods can be broadly classified as quantitative and qualitative methods.

The outcome of the quantitative research methods is an answer to the questions below and is used to measure anything tangible.

• Who was involved?
• What were the outcomes?
• What was the price?

The best way to collect quantitative data is through surveys , questionnaires , and polls . You can also create pre-tests and post-tests, review existing documents and databases or gather clinical data.

Surveys are used to gather opinions, feedback or ideas of your employees or customers and consist of various question types . They can be conducted by a person face-to-face or by telephone, by mail, or online. Online surveys do not require the intervention of any human and are far more efficient and practical. You can see the survey results on dashboard of research tools and dig deeper using filter criteria based on various factors such as age, gender, location, etc. You can also keep survey logic such as branching, quotas, chain survey, looping, etc in the survey questions and reduce the time to both create and respond to the donor survey . You can also generate a number of reports that involve statistical formulae and present data that can be readily absorbed in the meetings. To learn more about how research tool works and whether it is suitable for you, sign up for a free account now.

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Quantitative data measure the depth and breadth of an initiative, for instance, the number of people who participated in the non-profit event, the number of people who enrolled for a new course at the university. Quantitative data collected before and after a program can show its results and impact.

The accuracy of quantitative data to be used for evaluation research depends on how well the sample represents the population, the ease of analysis, and their consistency. Quantitative methods can fail if the questions are not framed correctly and not distributed to the right audience. Also, quantitative data do not provide an understanding of the context and may not be apt for complex issues.

Learn more: Quantitative Market Research: The Complete Guide

Qualitative research methods are used where quantitative methods cannot solve the research problem , i.e. they are used to measure intangible values. They answer questions such as

• What is the value added?
• How satisfied are you with our service?
• How likely are you to recommend us to your friends?
• What will improve your experience?

LEARN ABOUT: Qualitative Interview

Qualitative data is collected through observation, interviews, case studies, and focus groups. The steps for creating a qualitative study involve examining, comparing and contrasting, and understanding patterns. Analysts conclude after identification of themes, clustering similar data, and finally reducing to points that make sense.

Observations may help explain behaviors as well as the social context that is generally not discovered by quantitative methods. Observations of behavior and body language can be done by watching a participant, recording audio or video. Structured interviews can be conducted with people alone or in a group under controlled conditions, or they may be asked open-ended qualitative research questions . Qualitative research methods are also used to understand a person’s perceptions and motivations.

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The strength of this method is that group discussion can provide ideas and stimulate memories with topics cascading as discussion occurs. The accuracy of qualitative data depends on how well contextual data explains complex issues and complements quantitative data. It helps get the answer of “why” and “how”, after getting an answer to “what”. The limitations of qualitative data for evaluation research are that they are subjective, time-consuming, costly and difficult to analyze and interpret.

Learn more: Qualitative Market Research: The Complete Guide

Survey software can be used for both the evaluation research methods. You can use above sample questions for evaluation research and send a survey in minutes using research software. Using a tool for research simplifies the process right from creating a survey, importing contacts, distributing the survey and generating reports that aid in research.

Examples of evaluation research

Evaluation research questions lay the foundation of a successful evaluation. They define the topics that will be evaluated. Keeping evaluation questions ready not only saves time and money, but also makes it easier to decide what data to collect, how to analyze it, and how to report it.

Evaluation research questions must be developed and agreed on in the planning stage, however, ready-made research templates can also be used.

Process evaluation research question examples:

• How often do you use our product in a day?
• Were approvals taken from all stakeholders?
• Can you report the issue from the system?
• Can you submit the feedback from the system?
• Was each task done as per the standard operating procedure?
• What were the barriers to the implementation of each task?
• Were any improvement areas discovered?

Outcome evaluation research question examples:

• How satisfied are you with our product?
• Did the program produce intended outcomes?
• What were the unintended outcomes?
• Has the program increased the knowledge of participants?
• Were the participants of the program employable before the course started?
• Do participants of the program have the skills to find a job after the course ended?
• Is the knowledge of participants better compared to those who did not participate in the program?

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Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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How impact valuation helps companies meet the latest sustainability reporting requirements

Impact valuation helps companies address sustainability both internally and externally. Image:  Getty Images/iStockphoto

.chakra .wef-1c7l3mo{-webkit-transition:all 0.15s ease-out;transition:all 0.15s ease-out;cursor:pointer;-webkit-text-decoration:none;text-decoration:none;outline:none;color:inherit;}.chakra .wef-1c7l3mo:hover,.chakra .wef-1c7l3mo[data-hover]{-webkit-text-decoration:underline;text-decoration:underline;}.chakra .wef-1c7l3mo:focus,.chakra .wef-1c7l3mo[data-focus]{box-shadow:0 0 0 3px rgba(168,203,251,0.5);} Jochen A Berner

Beate stuis.

.chakra .wef-1nk5u5d{margin-top:16px;margin-bottom:16px;line-height:1.388;color:#2846F8;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-1nk5u5d{font-size:1.125rem;}} Get involved .chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} with our crowdsourced digital platform to deliver impact at scale

• Impact valuation provides objective and quantitative insights and helps to inform the new demands of European sustainability reporting requirements.
• Translation of impacts into monetary terms enables a common understanding of the business relevance of sustainability.
• Deeper strategic insights and discussions within organizations are additional benefits of impact valuation that help transform business models.

As companies navigate the implementation of laws such as the Corporate Sustainability Reporting Directive (CSRD) and the EU Taxonomy, many are questioning the higher costs of these new sustainability reporting requirements.

Despite these concerns, it remains important that we keep the original intention of these regulations in mind. Namely, requiring companies to report sustainability information with the aim of providing investors and stakeholders access to robust and comprehensive information to make more informed decisions; and establishing greater transparency about a company’s impact on planet and people.

Have you read?

Impact valuation: how to challenge and elevate traditional decision-making, is impact valuation the silver bullet for sustainability, what is impact valuation and how can it measure the business value of social and environmental efforts.

However, do the increased levels of reporting requirements meet these original expectations and support the transformation of companies towards sustainability? And if so, how can impact valuation contribute to this effort?

Double materiality under the CSRD

The CSRD specifies a double materiality analysis (DMA) that specifies that companies must assess both the financial effect of sustainability topics on their business, and the external impact of their business activities on the environment and society.

This includes a range of sustainability topics across the entire value chain of the environmental, social and governance pillars. From an impact perspective, the company must assess the severity of actual and potential impacts of their business on both the environment and people, based on scale, scope and irremediability, as well the likelihood that these impacts might take place.

Understanding impact valuation

Existing methodologies, such as that used by Frankfurt’s Value Balancing Alliance (VBA), employ widely accepted valuation techniques to translate the positive and negative impacts of companies’ activities into financial terms. VBA’s approach covers a broad range of environmental, social and economic impacts that results in impact values (e.g. for climate change, pollution or human rights) informing the DMA.

Impact valuation also offers additional insights, providing companies with a better understanding of their actual and potential impacts, and the resulting financial risks and opportunities they need to report information about under the CSRD:

• Understanding causality . Available methodologies provide a comprehensive description of environmental and social topics as well as the underlying cause and effect relationships of the described impact pathways. The resulting information is a good starting point for companies to drive better discussions with stakeholders during the DMA process.
• Insights into value chain stages . DMA requires the assessment of impacts across the value chain. Companies might have a good overview of their own operations, but insights from upstream and downstream activities are often limited. Impact valuation can help with this by using established techniques, such as input-output models and life-cycle analysis, to provide data points for all the value chain stages.
• Assessment of severity of impacts . The underlying analysis performed during impact valuation provides insights into the scale of the impact (e.g. local information about water scarcity or air pollution) as well as its scope (e.g. based on a geographical analysis).
• Comparability of different topics . Impact valuation translates sustainability topics into monetary units, which enables the direct comparison of previously difficult-to-compare impact topics, e.g. water consumption versus waste.

Using impact valuation in double materiality analysis

Though most first-time adopters of the CSRD are using a qualitative and often descriptive approach for the DMA consisting of external and/or internal stakeholder assessments, a few companies are currently working towards integrating impact valuation into their DMA, which brings advantages such as additional value chain insights, neutral and external data, and objectivity and comparability to the process.

There are different approaches used for the assessment of impact materiality using impact valuation:

Pre-assessment. Some companies use impact valuation to assist with qualitative assessments. This approach might prove advantageous for some organizations, for example, financial institutions with a lack of detailed data and a broad range of investments, in order to focus early on material CSRD topics per sector or investment class. No matter the circumstances, the double materiality requirements of CSRD must be fulfilled and in most cases, impact valuation only addresses current impacts, and not the full range of CSRD topics and sub-topics. In order to be fully CSRD-compliant, missing topics and future impacts also need to be assessed in the pre-assessment approach.

Fully integrated . The limitations seen in the pre-assessment approach can be overcome through a multi-pillar approach that combines impact valuation with external stakeholder surveys and internal expert workshops – the approach that was taken in our project.

We took an analytical approach for the integration of the three pillars, but qualitative combination could have also been used. If a qualitative approach is applied, the integration criteria must be clearly stated and applied consistently across all of the topics.

Our analytical approach included weighting the results of the different pillars and combining the results to reach a final assessment score. Any missing CSRD (sub-) topics and the consideration of future impacts were included through the integration of stakeholder and expert assessments. A final validation ensured overall consistency.

Final validation : A third option would be using impact valuation as a control mechanism for a final validation, ensuring that no major impacts are mis-stated.

Whether or not impact valuation can be used for the financial materiality analysis – which concentrates purely on the financial implications within an organization – remains an ongoing debate. The CSRD clearly states that impact and financial materiality are two different perspectives, and therefore two separate analyses should be undertaken. A better understanding of the impacts and their severity, as well as the related dependencies on natural, human and social resources, is helpful to identity financial risks or opportunities. Impact valuation can be used as a starting point for the financial materiality assessment. But even when a low impact is determined across their own value chain, companies might be highly affected by the negative impacts produced by third parties, with tangible effects on their own financial performance.

Our experience from our joint project shows that impact valuation not only provided an objective and quantitative additional tool for the DMA, but also enriched the qualitative discussions that took place during internal stakeholder and expert workshops. It provided a general overview of the magnitude of impacts for very different topics such as greenhouse gas emissions, pollution and social topics, helping us to remain objective. Additional valuable insights were gained on the upstream value stage analysis on regional, purchased material and pollutant levels.

Even more important was how using impact valuation opened the discussion to those colleagues with a stronger controller and finance background. The translation of impacts into monetary terms made it easier to have a common understanding about the business relevance of sustainability. Establishing a common narrative, one that directly fed into the DMA requirements, was a catalyst for deeper strategic insights and discussions within the organization that is important to the transformation of the business model.

The Corporate Sustainability Reporting Directive (CSRD) is the European Union's legislative framework aimed at fostering greater standardization in the disclosure of Environmental, Social, and Governance (ESG) impacts by companies. The European Sustainability Reporting Standards (ESRS) under the CSRD provide specific guidelines and standards for ESG reporting, ensuring comparability across companies and sectors.

From 2024 onwards, approximately 50,000 companies worldwide will progressively be required to disclose their ESG performance.

To equip companies with the necessary skills for comprehensive ESG reporting on the ESRS and other global standards, such as those from the International Sustainability Standards Board, the Forum has established a Community of ESG Practitioners. This community is part of the Forum's Stakeholder Metrics initiative.

Contact us for more information on how to get involved.

The collective involvement of the entire organization from operations to finance can guide and enable the sustainable transformation of business models. For us, the DMA project supported positive and ongoing engagement across the organization, and impact valuation served as a powerful tool in providing an objective and common language.

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World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Research: Boards Still Have an ESG Expertise Gap — But They’re Improving

• Tensie Whelan

Over the last five years, the percentage of Fortune 100 board members possessing relevant credentials rose from 29% to 43%.

The role of U.S. public boards in managing environmental, social, and governance (ESG) issues has significantly evolved over the past five years. Initially, boards were largely unprepared to handle materially financial ESG topics, lacking the necessary background and credentials. However, recent developments show a positive shift, with the percentage of Fortune 100 board members possessing relevant ESG credentials rising from 29% to 43%. This increase is primarily in environmental and governance credentials, while social credentials have seen less growth. Despite this progress, major gaps remain, particularly in climate change and worker welfare expertise. Notably, the creation of dedicated ESG/sustainability committees has surged, promoting better oversight of sustainability issues. This shift is crucial as companies increasingly face both regulatory pressures and strategic opportunities in transitioning to a low carbon economy.

Knowing the right questions to ask management on material environmental, social, and governance issues has become an important part of a board’s role. Five years ago, our research at NYU Stern Center for Sustainable Business found U.S. public boards were not fit for this purpose — very few had the background and credentials necessary to provide oversight of  ESG topics such as climate, employee welfare, financial hygiene, and cybersecurity. Today, we find that while boards are still woefully underprepared in certain areas, there has been some important progress .

• TW Tensie Whelan is a clinical professor of business and society and the director of the NYU Stern Center for Sustainable Business, and she sits on the advisory boards of Arabesque and Inherent Group.

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C3TI will enable internal and external parties to access information on clinical trial innovation efforts more easily, engage in collaborations, identify resources that can further support the use of innovative modalities, and find development programs where a concerted approach to the use of clinical trial innovations would be impactful. The goals of these efforts are to help those involved in clinical research in staying current with clinical trial innovations, improve the efficiency and effectiveness of clinical trials, help increase the participation of diverse populations in clinical trials, and, in turn, accelerate the development of safe and effective new drugs.

C3TI is governed by leadership from the following CDER offices:

• Office of the Center Director
• Office of Communications
• Office of Compliance
• Office of Medical Policy
• Office of New Drugs
• Office of Translational Sciences

Connect with us

[email protected]

Featured C3TI Demonstrations & Activities

Video by CDER Center Director and Office of New Drugs leadership

Watch Dr. Patrizia Cavazzoni and Dr. Kevin Bugin share more about the vision and mission of C3TI

Demonstration Program

Learn about C3TI demonstration projects and how they provide the opportunity to test, implement, and scale the integration of innovation into clinical trials

Discover the core activities of C3TI established to enable and amplify innovative approaches to clinical trials

Frequently Asked Questions

See how C3TI impacts drug development, regulatory review, and sponsors’ interactions with CDER staff

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• Pensions and ageing society
• Public service pensions

2020 Valuation - Judicial Pension Schemes

The Government Actuary's Department has completed a valuation of the Judicial Pension Schemes as at 31 March 2020.

Actuarial valuation report as at 31 March 2020

PDF , 4.3 MB , 84 pages

Advice on assumptions

PDF , 1.73 MB , 83 pages

Summary of assumptions

PDF , 431 KB , 3 pages

Report on membership data

PDF , 1.86 MB , 54 pages

At the request of the Lord Chancellor, the Government Actuary’s Department carried out an actuarial valuation of the Judicial Pension Schemes as at 31 March 2020. The valuation has been undertaken in accordance with the Public Service Pensions (Valuations and Employer Cost Cap) Directions 2023 , which specify certain assumptions and require other assumptions to be the Lord Chancellor’s best estimates.

See also: Public Service pensions: 2016 actuarial valuation reports .

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The polarization in today’s Congress has roots that go back decades

It’s become commonplace among observers of U.S. politics to decry partisan polarization in Congress . Indeed, a Pew Research Center analysis finds that, on average, Democrats and Republicans are farther apart ideologically today than at any time in the past 50 years.

But the dynamics behind today’s congressional polarization have been long in the making. The analysis of members’ ideological scores finds that the current standoff between Democrats and Republicans is the result of several overlapping trends that have been playing themselves out – and sometimes reinforcing each other – for decades.

• Both parties have grown more ideologically cohesive. There are now only about two dozen moderate Democrats and Republicans left on Capitol Hill, versus more than 160 in 1971-72.
• Both parties have moved further away from the ideological center since the early 1970s. Democrats on average have become somewhat more liberal, while Republicans on average have become much more conservative.
• The geographic and demographic makeup of both congressional parties has changed dramatically. Nearly half of House Republicans now come from Southern states, while nearly half of House Democrats are Black, Hispanic or Asian/Pacific Islander.

The Center’s analysis is based on DW-NOMINATE , a method that uses lawmakers’ roll-call votes to place them in a two-dimensional ideological space. It is designed to produce scores that are comparable across time. This analysis focuses on the first dimension, which is essentially the economic and governmental aspects of the familiar left-right spectrum and ranges from 1 (most conservative) to -1 (most liberal). (For more details on DW-NOMINATE and this analysis’ geographical definitions, read “How we did this.”)

This analysis is based on DW-NOMINATE, a method of scaling lawmakers’ ideological positions based on their roll-call votes. It is the latest iteration of a procedure first developed by political scientists Keith T. Poole and Howard Rosenthal in the early 1980s.

DW-NOMINATE places each lawmaker on a two-dimensional scale, much like a standard x-y graph. The first (“horizontal”) dimension is essentially the same as the economic and governmental aspects of the familiar left-liberal/right-conservative political spectrum. The second (“vertical”) dimension typically picks up crosscutting issues that have divided the major parties at various times in American history, such as slavery, currency policy, immigration, civil rights and abortion. But as Poole noted in 2017 , since about 2000 that second dimension has faded in significance, to the point where congressional activity has “collapse[d] into a one-dimensional, near-parliamentary voting structure … almost every issue is voted along ‘liberal-conservative’ … lines.”

Accordingly, like most political science work that employs DW-NOMINATE scores, this analysis focuses on the primary liberal/conservative scale. That scale runs from -1 (most liberal) to 1 (most conservative). Each lawmaker is assigned a value between those endpoints based on their voting record; the scores are designed to be comparable between Congresses and across time.

In mid-February 2022, we downloaded DW-NOMINATE data for all senators and representatives from the 92nd Congress (1971-72) to the current 117th Congress. We excluded nonvoting delegates from the analysis, as well as lawmakers who officially served but (due to health issues, resignation or other factors) didn’t have a voting record that could be analyzed and scored for a given Congress. We did include all other lawmakers who served at any time during a given Congress, including those who died mid-term; those appointed to temporarily fill Senate seats who only served for part of a term; and those who left Congress early to fill some other office, such as a Cabinet position. (We also included all House speakers, even if they didn’t have an analyzable voting record. For many years, the tradition in the House has been for speakers to vote only on very significant matters or if their vote will be decisive.)

Lawmakers who changed parties in mid-Congress were classified by whichever label they wore for the longest time. Independents were analyzed as part of whichever major party they caucused with, with the exception of Rep. Justin Amash of Michigan during the 116th Congress. (Amash left the Republican Party in mid-2019 , and for most of his final term did not caucus with either major party.)

In our discussion of “Southern Democrats” and “Southern Republicans,” we defined “the South” as the 11 states that comprised the Confederacy during the Civil War, most of which were dominated politically by Democrats for generations after Reconstruction ended. Southern Democrats, however, were ideologically and demographically quite distinct from Democrats in the rest of the country, so they merited separate study (and we wanted to see if today’s Southern Republicans are similarly distinctive). We chose to use the former Confederate states as our definition of “the South,” as the states that made up the so-called “Solid South” varied somewhat over time and we wanted a consistent, relatively objective definition.

Our analysis of the changing racial and ethnic composition of lawmakers was based on data from the U.S. House of Representatives’ archives .

Between the 92nd Congress of 1971-72 and the current 117th Congress, both parties in both the House and the Senate have shifted further away from the center, but Republicans more so. House Democrats, for example, moved from about -0.31 to -0.38, meaning that over time they’ve become modestly more liberal on average. House Republicans, by contrast, moved from 0.25 to nearly 0.51, a much bigger increase in the conservative direction.

As Democrats have grown more liberal over time and Republicans much more conservative, the “middle” – where moderate-to-liberal Republicans could sometimes find common ground with moderate-to-conservative Democrats on contentious issues – has vanished.

Five decades ago, 144 House Republicans were less conservative than the most conservative Democrat, and 52 House Democrats were less liberal than the most liberal Republican, according to the analysis. But that zone of ideological overlap began to shrink, as conservative Democrats and liberal Republicans – increasingly out of step with their caucuses and their constituents – either retired, lost reelection bids or, in a few cases, switched parties.

Since 2002, when Republican Rep. Constance Morella of Maryland was defeated for reelection and GOP Rep. Benjamin Gilman of New York retired, there’s been no overlap at all between the least liberal Democrats and the least conservative Republicans in the House. In the Senate, the end of overlap came in 2004, when Democrat Zell Miller of Georgia retired.

Ever since, the gaps between the least conservative Republicans and least liberal Democrats in both the House and Senate have widened – making it ever less likely that there’s any common ground to find.

The ideological shifts in the congressional parties have occurred alongside – and, perhaps to some extent, because of – geographic and demographic shifts in their composition.

In 1971-72, representatives from the 11 former Confederate states made up nearly a third (31.4%) of all the House Democrats who served in that Congress. Those Southern representatives were notably less liberal than Democrats from elsewhere in the country: Their average DW-NOMINATE score was -0.144, versus -0.388 for non-Southern House Democrats.

Over time, though, Southern Democrats became both fewer in number and more liberal – to the point where today, they account for only 22% of the House Democratic caucus, but ideologically are almost indistinguishable from their non-Southern colleagues (average scores of -0.383 and -0.381, respectively).

On the Republican side of the aisle, almost the exact opposite trend has occurred. Southerners made up less than 15% of the House GOP caucus 50 years ago but comprise about 42% of it today. And while Republicans in general have become more conservative, that’s been especially true of Southern Republicans in the House: Their DW-NOMINATE score has moved from about 0.29 (only slightly to the right of non-Southern Republicans) in 1971-72 to 0.57 in the current Congress, versus about 0.46 today for non-Southern House Republicans. (These trends are similar in the Senate, although only four of the 22 senators from former Confederate states are currently Democrats.)

The racial and ethnic makeup of both parties’ Southern lawmakers has changed considerably. In 1971-72, according to House records , only 12 African Americans served in the House and one in the Senate, and none were from the South. Of the five Hispanics in the House, two were from Texas (the lone Hispanic senator was from New Mexico). And the only Asian Americans or Pacific Islanders in Congress were Hawaii’s two senators (one Democrat, one Republican) and two representatives (both Democrats).

In the current Congress, 24 of the 50 House Democrats from the South are African American; seven are Hispanic; and two are Asian Americans or Pacific Islanders. (Rep. Bobby Scott of Virginia is of both African American and Filipino descent.) One of the four Democratic senators from the South (Raphael Warnock of Georgia) is African American. In contrast, only one of the 91 Southern House Republicans is Black (Byron Donalds of Florida); four others are Hispanic. One of the GOP’s 18 Southern senators is Black (Tim Scott of South Carolina) and two are Hispanic (Ted Cruz of Texas and Marco Rubio of Florida).

Note: This is an updated version of a post originally published June 12, 2014.

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