New marriages/pairings repeat many things of the previous ones; those in shorter first unions & with higher neuroticism typically experienced decreases in functioning

(Eventual) Stability and Change Across Partnerships. Matthew D Johnson, Franz J Neyer. Journal of Family Psychology, February 2019, DOI: 10.1037/fam0000523

Abstract: Does a new partnership differ from its preceding one? This study investigates whether relationship dynamics change as people transition from one partnership to another and examines a number of predictors that might explain variation in change trajectories. We draw on data gathered from 554 focal participants in the German Family Panel (pairfam) study surveyed at four time points spanning two intimate unions to answer these questions. Latent change score modeling results showed eventual stability in five of seven constructs under investigation. When looking at overall change from Time 1 in partnership 1 to Time 2 of partnership 2, there were no mean-level changes in relationship and sexual satisfaction, perceptions of relational instability, and frequency of conflictual and intimate exchanges. Sexual frequency and partner admiration improved across partnerships. Further analyses showed much change unfolded in the interim; all constructs showed significant deterioration as the first partnership drew to a close, marked improvements as individuals moved from the end of the first partnership into their next union, and worsened across the first year of the second partnership. Neuroticism and relationship length were the most consistent predictors of change across partnerships: those in shorter first partnerships and with higher neuroticism typically experienced decreases in functioning across partnerships. These findings provide support for an eventual stability conceptualization of relationship development across partnerships.

The Role of Motivation, Attention and Design in the Spread of Moralized Content Online

Brady, William J., Molly Crockett, and Jay J. Van Bavel. 2019. “The MAD Model of Moral Contagion: The Role of Motivation, Attention and Design in the Spread of Moralized Content Online.” PsyArXiv. March 11. doi:10.31234/osf.io/pz9g6

Abstract: With over 2 billion active users, online social networks represent an important venue for moral and political discourse and have been used to organize political revolutions, sway elections, and raise awareness of social issues. These examples rely on a common process in order to be effective: the ability to engage users and spread moralized content through online networks. Here, we review evidence that expressions of moral emotion play a key role in the spread of moralized content (a phenomenon we call ‘moral contagion’). Next, we propose a psychological model to explain moral contagion. The ‘MAD’ model of moral contagion argues that people are motivated to share moral-emotional content; that such content is especially likely to capture attention; and that the design of social media platforms facilitates its spread. We review each component of the model and raise several novel, testable hypotheses that can spark progress on the scientific investigation of civic engagement and activism, political polarization, propaganda and disinformation, and moralized consumer behavior in the digital age.

Both girls and boys engage in gender-enforcing behavior (try to exclude others due to their gender); aggression and biased gender-related beliefs are associated with gender-enforcing behavior

Characteristics of Preschool Gender Enforcers and Peers Who Associate with Them. Sonya Xinyue Xiao et al. Sex Roles, Mar 2019, https://link.springer.com/article/10.1007/s11199-019-01026-y

Abstract: Children who try to exclude others due to their gender can be considered as “gender enforcers.” Using multiple methods (observations, interviews) and informants (children, teachers, teacher aides), we investigated the prevalence of gender enforcement, the characteristics of gender enforcers, and potential associations of exposure to gender enforcers. Participants were 98 (Mage = 49.47 months, SD = 11.40; 52% boys) preschoolers from a southwestern city in the United States. Results showed that both girls and boys engage in gender-enforcing behavior. Further, findings suggest that aggression and biased gender-related beliefs are associated with gender-enforcing behavior. Children who spent more time (over months) with enforcers were observed to play more with same-gender peers and to show more biased gender cognitions than were children who spent less time with enforcers. The study extends our understanding of how gender norms are enforced in early childhood, and it provides insights that may help to identify young gender enforcers. These findings have potential to inform future research and practice related to gender-based aggression in childhood.

Keywords: Gender beliefs Gender norms Peer pressure Peer relations

People condemn scientific procedures they perceive to involve playing God; judge a novel scientific practice to involve more playing God and to be more morally unacceptable; happens even to non-believers

Aversion to playing God and moral condemnation of technology and science. Adam Waytz and Liane Young. Phil. Trans. Roy. Soc. B, Volume 374, Issue 1771, March 11 2019. https://doi.org/10.1098/rstb.2018.0041

Abstract: This research provides, to our knowledge, the first systematic empirical investigation of people's aversion to playing God. Seven studies validate this construct and show its association with negative moral judgements of science and technology. Motivated by three nationally representative archival datasets that demonstrate this relationship, studies 1 and 2 demonstrate that people condemn scientific procedures they perceive to involve playing God. Studies 3–5 demonstrate that dispositional aversion to playing God corresponds to decreased willingness to fund the National Science Foundation and lower donations to organizations that support novel scientific procedures. Studies 6a and 6b demonstrate that people judge a novel (versus established) scientific practice to involve more playing God and to be more morally unacceptable. Finally, study 7 demonstrates that reminding people of an existing incident of playing God reduces concerns towards scientific practices. Together, these findings provide novel evidence for the impact of people's aversion to playing God on science and policy-related decision-making.

1. Introduction

In Mary Shelley's Frankenstein, the eponymous Victor Frankenstein animates a human-like creature through scientific experimentation, stating, ‘A new species would bless me as its creator and source; many happy and excellent natures would owe their being to me’ ([1], p. 101). Yet, by the story's end, the experiment has gone horribly wrong, and the creature, a monster, turns against Frankenstein. Many have read Frankenstein as a critique of humans' desire to play God, a romantic indictment of the Enlightenment's scientific advancements.
This critique of playing God pervades people's opposition toward science and technology. Sunstein ([2], p. 539) describes aversion to playing God as a heuristic [3] that guides¹ moral disapproval of human intervention in the domains of sex, reproduction and nature: ‘“Do not play God” is the general heuristic here, with different societies specifying what falls in that category and with significant changes over time’. Despite its apparent importance, though, behavioural science has largely ignored the principle ‘Do not play God’ as a topic of study, work emerging in the fields of genetic engineering [4], nature conservation [5] and medicine [6] instead. The current work addresses this gap within behavioural science.
Scholars have put forth several definitions of playing God, with varying specificity. Most broadly, playing God involves what science scholar, Philip Ball [7], refers to as, ‘Mankind assuming powers beyond our station or our ability to control’. The current work adapts this general definition and focuses on a single domain that typifies aversion to playing God—people's responses to human intervention in science and technology.
Aversion to playing God, and its basis in aversion to human interference in the natural order (supported empirically in studies 6a and 6b), resembles other related but conceptually distinct constructs. Moral foundations theory [8–11], for example, specifies one moral foundation related to aversion to playing God: purity/sanctity. This foundation has theoretical roots in the moral code of purity/divinity, detailed as follows ([12], p. 576, italics added): ‘A person disrespects the sacredness of God, or causes impurity or degradation to himself/herself, or to others. To decide if an action is wrong, you think about things like sin, the natural order of things, sanctity, and the protection of the soul or the world from degradation and spiritual defilement’. The link between impurity and violating the ‘natural order of things' is critical to the code of purity/divinity and yet has gone largely unexplored. We believe that understanding aversion to playing God can illuminate this link.
Closest to this topic is work on naturalness bias—people's preference for natural processes and products rather than those that originate from human-imposed agency on the natural order of things [13,14]. Rozin ([15], p. 31) notes that ‘Human intervention seems to be an amplifier in judgements on food riskiness and contamination’, and Sunstein ([2], p. 539) notes that secular societies endorse a version of the ‘Do not play God’ principle in the form of ‘Do not tamper with nature’. Extensive work reveals people's preference for foods and medicines produced naturally and without human intervention [16,17].
More recent work suggests that naturalness bias might be linked to moral aversion to taboo trade-offs, a social transaction that places a monetary price on a value that people perceive to be sacred [18,19]. Work examining people's aversion to genetically modified food suggests that it elicits dislike not only for its ‘unnaturalness’, but also elicits moral emotional responses (e.g. disgust) similar to canonical taboo trade-offs [20].
Despite the resemblance between people's naturalness bias (and related constructs) and people's aversion to playing God, these constructs are nevertheless distinct. A pilot study (electronic supplementary material) reveals several practices that people perceive to involve tampering with nature but not playing God (e.g. emitting carbon monoxide while driving) as well as practices perceived to involve playing God but not tampering with nature (e.g. airline Chief Executive Officers' conspiring to fix prices). In addition, study 2 presents one case largely unrelated to nature (drone warfare) and establishes a link between aversion to playing God and moral judgement.
The link between principles regarding God and principles regarding nature and the natural order also aligns with extensive work on intuitive theism—people's implicit belief that a supernatural deity has intelligently designed nature itself [21–23]. Importantly, we take aversion to playing God to be distinct from religious cognition in three ways. First, our pilot study (electronic supplementary material) distinguishes playing God from judgements of religious violations. Second, across studies we show that religiosity does not explain the relationship between aversion to playing God and moral judgement. Third, we demonstrate that aversion to playing God need not involve any consideration of God as the source of action per se and that aversion to playing God is distinct from religious conviction.
The present research characterizes the relationship between aversion to playing God and moral attitudes primarily in the domains of science and technology. Scientific procedures frequently involve human intervention in nature and sacred aspects of human experience [18]. Our overarching hypothesis is that aversion to playing God corresponds to negative attitudes toward science and technology across diverse contexts. Three archival nationally representative datasets provide initial support for this relationship (electronic supplementary material) and motivate the present empirical work.

2. Overview of studies

Studies 1 and 2 provide initial support that people morally condemn practices to the degree that they see them as involving playing God. Studies 3–5 extend these findings by showing that aversion to playing God corresponds to behavioural intentions and behaviours including willingness to fund the National Science Foundation (NSF), and real monetary donations to organizations supporting stem cell research and genetically modified rice. Given that this is, to our knowledge, the first systematic psychological examination of aversion to playing God, we also examine an important moderator—novelty. Studies 6a and 6b present a case in which the relationship between aversion to playing God and moral condemnation is modulated by the novel versus established nature of the act. Study 7 extends these findings by demonstrating that reminders of existing acts of playing God (i.e. reducing the perceived novelty of playing God) improve attitudes toward scientific practices.

[...]

3. Discussion

These studies establish, for the first time, to our knowledge, aversion to playing God as a valid psychological construct relevant to judgements of science and technology including robotics (drones), GMOs, vaccinations and stem cell research. Importantly, our findings provide critical evidence for the association between aversion to playing God and moral condemnation of novel scientific practices, even when these practices benefit human well-being [27].
Given that this research represents, to our knowledge, the first systematic examination of aversion to playing God, several key questions emerge. One is the degree to which aversion to playing God causally influences moral judgement towards science and technology. Although we acknowledge the plausibility of a bidirectional relationship between these constructs, study 7, in particular, supports a causal pathway from aversion to playing God to moral judgement. Future research can examine this pathway as well, for example, testing whether people condemn a chemical change in an organism that results from human intervention more than one that results from randomness, and whether perceptions of playing God drive any difference. Given that existing work shows that people view human-caused harm as worse than naturally arising harm and harm caused by acts worse than harm by omission [28], and that people prefer natural products and processes (that are chemically identical) to human-made ones [16], we believe these effects are likely.
Another key question is whether aversion to playing God simply reflects general moral condemnation. The present research suggests this is not the case. First, study 5 shows that aversion to playing God positively correlates with support for the Cure Violence charity, and study 6b shows aversion to playing God is unrelated to the moral acceptability of an established practice in the legal domain. In other words, the relationship between aversion to playing God and moral judgement is not consistent across contexts. Second, the inconsistent relationship between aversion to playing God and political ideology suggests that this construct does not merely reflect a particular political profile associated with a particular set of moral foundations [9,10]. Archival studies 1b and 1c (electronic supplementary material) also show little association between ideology and aversion to playing God. Thus, aversion to playing God reflects a specific moral concern that emerges among liberals and conservatives alike.
A related question is whether aversion to playing God simply reflects religious conviction. The present research suggests that aversion to playing God represents a distinct construct from religiosity or belief in God. First, across studies, measures of religiosity and belief in God do not account for the association between aversion to playing God and disapproval of science and technology. Aversion to playing God predicts moral condemnation above and beyond religious constructs. Second, the pilot study (electronic supplementary material) and study 2 showed no association between measures of religiosity or belief in God and aversion to playing God. The inconsistent relationship between religiosity and aversion to playing God across studies may stem from opposing influences of religious belief on perceptions of playing God. As documented here, when a relationship between religious belief and aversion to playing God emerges, it is typically positive. That is, believers deliver harsher moral judgements than non-believers. This pattern probably stems from an explicit code within many Judeo-Christian traditions that calls for respecting God's authority as a sole creator [29,30]; thus, intervening in matters such as reproduction is incompatible with respect for God as an ultimate agent. Yet, some Judeo-Christian sects, such as Lutheranism, teach adherents to carry out the will of God through their actions [31]. Therefore, followers may view certain interventions as essential to their religion. Because no comparisons among religions are offered here, future work is needed to assess whether aversion to playing God is attenuated for religions that explicitly instruct people to be secondary agents for God's plans.
As it stands, one of the current limitations of this work is its generalizability to adherents of non-Judeo-Christian religions, which as of now is an open question. For example, a strict interpretation of the Islamic idea of Tawhid (one should not worship other Gods nor take on Godhead for oneself) would prohibit acts of playing God, yet the Islamic spiritual tradition of Sufism also allows people to take divine traits so that God can act ‘through them’ ([32], p. 417). Other scholars suggest that playing God in the case of cloning is less of a concern for Hinduism and Buddhism because it fits with the idea of reincarnation [29], although these religions' views about the creation and destruction of life complicate this question [33]. Ultimately, future research can test the strength of aversion to playing God in other religions.
Given the prevalence of atheism [34], future research may also examine whether even atheists demonstrate an aversion to playing God at an implicit level. Although our work demonstrates a relationship between increased religiosity and aversion to playing God, aversion to playing God is present across the religious spectrum in all of the present studies. Atheists may therefore demonstrate their aversion at an implicit level, similar to other aspects of religious cognition that emerge even among those who explicitly disavow religious belief [22,35]; indeed, recent studies have shown that religious primes affect moral behaviour and public self-awareness even among atheists [36,37]. At an explicit level, atheists might express their aversion in non-religious terms, such as ‘Do not tamper with nature’, as noted by Sunstein ([2], p. 539).
Overall, our work suggests that most people believe (implicitly or explicitly) that, in the domains of science and technology, human intervention should be avoided and instead left to a more metaphysical source of action—for theists that source might be God, and for atheists or others that source might be fate [38], nature or some other agentic practice already in place. In other words, aversion to playing God may not necessarily reflect an aversion to humans' taking on the role of a religious spirit or creator, but rather an aversion to human agency in a domain in which another agent is thought to be responsible.
Given that playing God is not reducible to religiosity or belief in God, other related beliefs about secular pre-existing systems or agents governing science might similarly affect moral judgements of science and scientific progress. For example, belief in the infallible capacity of nature might impede views on scientific innovation as well. Take, for example, the hotly contested debate over GMOs. Spitznagel & Taleb [39] argue against genetically modified food by stating, ‘The statistical mechanism by which a tomato was built by nature is bottom-up, by tinkering in small steps…In nature, errors stay confined and, critically, isolated’. This belief in nature's near-perfect ability may stifle innovation in food production and farming [40], inspiring beliefs (akin to aversion to playing God) that humans should not interfere in these domains. Study 6b hints at the contribution of belief in a natural order to these attitudes.
In summary, aversion to playing God, which may result from ideas about deference to God or some higher organizing power as the ultimate agent, can increase inertia in moral and scientific domains. Given rapid advances in reproductive technology, pharmaceuticals and robotics and artificial intelligence, and the novelty of these advancements, we expect aversion to playing God to continue to influence public opposition towards these developments. Particularly in the domain of social robotics, as scientists and developers become increasingly Frankensteinian in engineering human-like agents, the present work suggests the importance of understanding where negative attitudes towards these agents originate and how to mollify them, in efforts to facilitate scientific progress.

Ethics

Informed consent was obtained from all participants and institutional review board approval was obtained for all studies we conducted. For the GSS, used in archival study 1a, informed consent was obtained from participants and this survey was approved by the institutional review board at NORC at the University of Chicago. The survey used in archival study 1b was approved by the institutional review board at Johns Hopkins University that granted exempt status for consent. For the polls used in archival study 1c, they were conducted within the CASRO standards for research and all participants received informed consent before participating.

Data accessibility

The data supporting this article are available in the Dryad Digital Repository: https://doi.org/10.5061/dryad.gv7qs12 [41].

Authors' contributions

Both authors designed the studies, analysed the data, drafted the paper and approved the final submission.

Competing interests

We have no competing interests.

Funding

We received no external funding for this study.

Acknowledgements

We thank the GPPC for archival study 1b data, and Kurt Gray, Adam Galinsky, Linda Skitka, Jonathan Baron, Josh Rottman, Ellen Winner, Fiery Cushman and Ryan Miller for helpful comments.

Footnotes

1 Although we assume the causal pathway from aversion to playing God to moral judgement and explicitly support this pathway empirically in study 7, we also acknowledge that people may use assessment of playing God to justify moral judgment post hoc.
2 Our data contained 11 people who made donations outside of 3 s.d. either for Cure Violence or for the National Stem Cell Foundation, and whose exclusion alters the significance of these findings. Given our a priori decision not to exclude outliers and given the bounded nature of this measure, we chose to include these participants in our analyses as they represent meaningful data points of people who feel strongly about donating to one charity or the other. Furthermore, regressing donations transformed by square root (such that they no longer represent values outside of 3 s.d.) on APG reveals the same significant results reported in the primary analyses.

Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9.figshare.c.4381796.
One contribution of 17 to a theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Have Humans Evolved to Be Cheaters? Is it something general? Have other monogamous species did the same?

Have Humans Evolved to Be Cheaters? Nadia Nooreyezdan. The Swaddle, Mar 10, 2019. https://theswaddle.com/have-humans-evolved-to-be-cheaters/

Excerpts (full text with lots of links at the reference above):

[...] Evolutionary psychology experts like Christopher Ryan and Cacilda Jethá, co-authors of the book Sex at Dawn, theorize men and women both have biological motivations for cheating while maintaining a monogamous relationship. Male animals, including humans, have an evolutionary drive to have as many offspring as they can with different mates, while females are motivated to seek out mates with superior genes in order to increase the genetic diversity (and chances of survival) of their offspring. But some animals, humans included, have also evolved, both socially and biologically, to want the security of monogamous relationships; we may still feel jealousy or betrayal if we’re cheated on, wanting our partners to be faithful, while also being sexually attracted to other people.

While this isn’t an excuse for people to cheat, it does suggest humans will always have these opposing biological motivations creating a tension within ourselves and our relationships.

Monogamy is uncommon among animals, with fewer than 5 percent of mammals staying with the same mate. Humans evolved towards monogamy mostly because babies are, in a word, helpless. [...]

Because they needed to be nursed, carried, protected, and fed for several months, if not years, pairs or groups of parents raising the infant became necessary, Ryan and Jethá write. But since male primates didn’t like to be responsible for offspring that wasn’t theirs (an understatement — this usually resulted in infanticide by angry males), pair-bonding became a necessity. Monogamous pairs seemed to be the solution for decreasing male-male competition while still ensuring enough resources for the offspring. But this didn’t mean that primitive man didn’t cheat.

Monogamy was a convenient way to ensure that, in the genetic competition of evolution, one’s offspring reached maturity. It meant staying together basically ‘for the sake of the kids,’ but not necessarily being sexually faithful to one another. Our motivations for reproduction, Ryan and Jethá argue, drive us to seek partners outside of our monogamous relationships.

Reproduction required little investment for males; females, on the other hand, have to choose between the security a male partner may provide, and superior genetic qualities — because as Daniel Kruger, an evolutionary psychologist at the University of Michigan, has pointed out, it’s rare for a man to provide both.

“One long-term strategy is to settle down and have a long-term relationship with a guy who’s a reliable, stable provider, but then have an affair on the side with a guy who has phenotypic qualities and can provide that high-quality genetic investment,” Kruger has said. And genetically, women are predisposed to have this kind of ‘back-up plan,’ which researchers at the University of Texas refer to as the “mate-switching hypothesis.” But in order to maintain the status quo of the monogamous relationship, both men and women have to resort to what Kruger refers to as “strategies and counter strategies.” In other words, humans just try to not get caught.

Not convinced? These same competing impulses are found throughout nature, even among the animal kingdom’s erstwhile paragons of monogamy: birds.

With an estimated 90 percent of feathered species staying monogamous, birds have also been found to be serial cheaters for the same evolutionary incentives humans have. For decades, scientists believed that birds’ social monogamy during breeding season meant that the bonded pairs were prone to loyalty. But further genetic and behavioral research has shown that up to 75 percent of the offspring in a population could be from “extra-pair copulations.” Adultery, jealousy, and cuckolded partners abound, from indigo buntings, to yellow warblers. Even wandering albatrosses, who return back to the same partner every year after months at sea, aren’t always sexually faithful, with 14 to 24 percent of chicks fathered by a male who is not the mother’s life partner. Clearly, social monogamy for birds is strictly separate from sexual monogamy, a far rarer occurrence.

Regardless of species, it seems that cheating while monogamous is possibly the most ideal situation for bonded pairs. The male gets to spread his genes as far and as wide as he can, with next to no repercussions, thus ensuring reproduction with at least one female; and the female gets to increase the genetic diversity among her offspring (in case some have genetic defects, others can survive) without risking the loss of resources provided by her mate. Ideally, males will help provide for all of the female’s offspring (unaware that they might not all be his), and females will be ignorant of their male partners’ mating with others.

It could be great, if we could all be open about this and okay with the idea of raising children regardless of whether they’re biologically ours. However, child rearing à la Plato’s idea of collective parenting feels like an unattainable dream. Like so many things about humans, we must manage conflicting impulses — genetic incentives to stay in monogamous relationships, and ones that lead us to cheat.

In her recent book, The State of Affairs: Rethinking Infidelity, couples’ therapist Esther Perel argues that being honest about our desires is [...].

Bad data, bad decisions... 9 years later after Obamacare, Centers for Medicare and Medicaid Services' actuaries find that CBO numbers were wildly off

9 Years After Obamacare Passed, Agency Finds Numbers Were Wildly Off. Jarrett Stepman. The Daily Signal, Feb 22, 2019. https://www.dailysignal.com/2019/02/22/9-years-after-obamacare-passed-agency-finds-numbers-were-wildly-off

Democrats defeated Republicans in the Obamacare repeal fight by warning that 22 million Americans would be thrown off their health insurance. They pointed to data leaked from the Congressional Budget Office.

Well, it turns out that data was completely wrong.

According to a report [a paywalled paper, https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2018.05499] by the Centers for Medicare and Medicaid Services released Wednesday, the Congressional Budget Office wildly overestimated the number of people who would lose their health insurance with the repeal of the individual mandate penalty.

Initial estimates from the Congressional Budget Office said 14 million would drop off their health insurance coverage due to the elimination of the individual mandate. Then, during the height of the 2017 debate over repeal, progressives touted a leaked number from the Congressional Budget Office claiming that 22 million people would “lose” their insurance if Congress repealed the law.

However, as health care analyst Avik Roy pointed out (https://www.forbes.com/sites/theapothecary/2017/07/22/cbo-three-fourths-of-coverage-difference-between-obamacare-gop-bills-driven-by-individual-mandate/#320be2c63627
), what made this number so high was the inflated number of people expected to lose their insurance due to repeal of the mandate—about 73 percent to be exact. So, it wouldn’t be 22 million Americans losing their insurance. Most of those in the projection would simply be choosing to opt out of insurance.

And it turns out even that wasn’t true. A far smaller number of Americans appear to be opting out of insurance since the individual mandate’s repeal. Only 2.5 million more people are expected to go without insurance in 2019 due to its repeal, according to the latest report, and that number is expected to decline in the years ahead.

[...]

The Congressional Budget Office is opaque, to say the least. It does not publish or share the way it comes up with numbers, and some have criticized the organization for its lack of transparency and outsized influence on policymaking.

Doug Badger, a visiting fellow in domestic policy studies at The Heritage Foundation, told The Daily Signal that Congressional Budget Office analysis has been a chronic problem.

“When it comes to the individual mandate, CBO has never let the facts affect their wildly inaccurate estimates. CBO continued to forecast that millions of insured Americans would suddenly become uninsured if the mandate were repealed,” Badger wrote in an email to The Daily Signal. “CBO’s faulty estimates misled the public into believing that repealing Obamacare would lead to a vast increase in the number of uninsured. Bad estimates produced bad policy.”

[...]


---
the paywalled paper:

National Health Expenditure Projections, 2018–27: Economic And Demographic Trends Drive Spending And Enrollment Growth. By Andrea M. Sisko, Sean P. Keehan, John A. Poisal, Gigi A. Cuckler, Sheila D. Smith, Andrew J. Madison, Kathryn E. Rennie, and James C. Hardesty


doi: 10.1377/hlthaff.2018.05499

ABSTRACT National health expenditures are projected to grow at an average annual rate of 5.5 percent for 2018–27 and represent 19.4 percent of gross domestic product in 2027. Following a ten-year period largely influenced by the Great Recession and major health reform, national health spending growth during 2018–27 is expected to be driven primarily by long-observed demographic and economic factors fundamental to the health sector. Prices for health care goods and services are projected to grow 2.5 percent per year, on average, for 2018–27—faster than the average price growth experienced over the last decade—and to account for nearly half of projected personal health care spending growth. Among the major payers, average annual spending growth in Medicare (7.4 percent) is expected to exceed that in Medicaid (5.5 percent) and private health insurance (4.8 percent) over the projection period, mostly as a result of comparatively higher projected enrollment growth. The insured share of the population is expected to remain stable at around 90 percent throughout the period, as net gains in health coverage from all sources are projected to keep pace with population growth.

-
During 2018–27 national health spendingisexpectedtobedriven primarily by long-observed demographic and economic factors fundamentaltothehealthsector, largely in contrast to the prior decade—which was affected by the notable impacts of a historic recessionand the implementation of wide-ranging health reform legislation.1 Overall, national health spending is projected to grow at 5.5 percentperyear,onaverage,for2018–27(exhibit1). This is faster than the average growth rate experiencedfollowingthelastrecession(3.9percent for 2008–13) and the more recent period inclusive of the Affordable Care Act’s major coverage expansions(5.3percentfor2014–16).However, it is slower than the rate throughout the nearly two decades preceding the Great Recession (7.3 percent for 1990–2007). Growth in gross domesticproduct(GDP)duringtheten-yearprojectionperiodisprojectedtoaverage4.7percent. Because national health spending growth is expectedtoincrease0.8percentagepointfaster, onaverage,thangrowthinGDPovertheprojection period, the health share of GDP is expected to rise from 17.9 percent in 2017 to 19.4 percent in 2027, with almost all of the increase in share expected after 2020. Projected average annual spending growth rates for the underlying major payers of health care are expected to vary substantially during 2018–27,mainlyasaresultofdifferingexpected trends in enrollment growth. Average Medicare spendinggrowthisprojectedtobethefastest,at
doi: 10.1377/hlthaff.2018.05499 HEALTH AFFAIRS 38, NO. 3 (2019): – ©2019 Project HOPE— The People-to-People Health Foundation, Inc.
Andrea M. Sisko (Andrea .Sisko@cms.hhs.gov) is an economist in the Office of the Actuary, Centers for Medicare and Medicaid Services (CMS), in Baltimore, Maryland.
Sean P. Keehan is an economist in the CMS Office of the Actuary.
John A. Poisal is a deputy director of the National Health Statistics Group, CMS Office of the Actuary.
Gigi A. Cuckler is an economist in the CMS Office of the Actuary.
Sheila D. Smith is an economist in the CMS Office of the Actuary.
Andrew J. Madison is an actuary in the CMS Office of the Actuary.
Kathryn E. Rennie is an actuary in the CMS Office of the Actuary.
James C. Hardesty is an actuary in the CMS Office of the Actuary.
March 2019 38:3 Health Affairs 1
Costs & Spending
Downloaded from HealthAffairs.org on February 20, 2019. Copyright Project HOPE—The People-to-People Health Foundation, Inc. For personal use only. All rights reserved. Reuse permissions at HealthAffairs.org.
Exhibit 1
National health expenditures(NHE),aggregateand percapitaamounts,shareofgross domesticproduct(GDP), andaverageannualgrowthfromprevious year shown, by source of funds, selected calendar years 2013–27
Source of funds 2013a 2016 2017 2018b 2019b 2027b Expenditure, billions NHE $2,881.8 $3,361.1 $3,492.1 $3,646.9 $3,823.1 $5,963.2 Health consumption expenditures 2,728.6 3,202.9 3,324.5 3,470.3 3,637.6 5,679.9 Out of pocket 325.9 356.1 365.5 378.6 396.9 585.8 Health insurance 2,088.1 2,504.5 2,604.2 2,720.9 2,850.6 4,545.8 Private health insurance 947.1 1,136.4 1,183.9 1,237.7 1,278.2 1,896.7 Medicare 589.9 677.1 705.9 747.4 800.1 1,436.8 Medicaid 445.2 565.6 581.9 594.8 623.4 992.1 Federal 256.9 358.3 361.2 369.5 386.5 611.1 State and local 188.4 207.3 220.6 225.3 237.0 380.9 Other health insurance programsc 105.9 125.3 132.6 141.0 148.8 220.2 Other third-party payers and programs and public health activity 314.7 342.4 354.8 370.8 390.0 548.4 Investment 153.2 158.2 167.6 176.5 185.5 283.3 Population (millions) 315.7 322.9 325.2 327.9 330.7 352.7 GDP, billions $16,784.9 $18,707.2 $19,485.4 $20,498.6 $21,503.1 $30,755.4 Disposable personal income, billions 12,505.3 14,170.9 14,796.3 15,563.2 16,297.3 23,453.9 NHE per capita 9,128.9 10,410.1 10,739.1 11,121.2 11,559.3 16,907.0 GDP per capita 53,170.5 57,941.2 59,922.8 62,511.0 65,015.9 87,198.3 Prices (2012=100.0) Personal Health Care Price Index 1.015 1.049 1.062 1.081 1.101 1.359 GDP Implicit Price Deflator, chain weighted 1.018 1.059 1.079 1.104 1.130 1.344 NHE as percent of GDP 17.2% 18.0% 17.9% 17.8% 17.8% 19.4% Annual growth NHE 3.9% 5.3% 3.9% 4.4% 4.8% 5.7% Health consumption expenditures 4.0 5.5 3.8 4.4 4.8 5.7 Out of pocket 2.0 3.0 2.6 3.6 4.8 5.0 Health insurance 4.4 6.2 4.0 4.5 4.8 6.0 Private health insurance 3.4 6.3 4.2 4.5 3.3 5.1 Medicare 5.3 4.7 4.2 5.9 7.1 7.6 Medicaid 5.3 8.3 2.9 2.2 4.8 6.0 Federal 5.6 11.7 0.8 2.3 4.6 5.9 State and local 5.0 3.2 6.4 2.1 5.2 6.1 Other health insurance programsc 6.0 5.8 5.8 6.4 5.5 5.0 Other third-party payers and programs and public health activity 3.4 2.9 3.6 4.5 5.2 4.4 Investment 1.7 1.1 6.0 5.3 5.1 5.4 Populationd 0.8 0.8 0.7 0.8 0.9 0.8 GDP 2.5 3.7 4.2 5.2 4.9 4.6 Disposable personal income 2.9 4.3 4.4 5.2 4.7 4.7 NHE per capita 3.0 4.5 3.2 3.6 3.9 4.9 GDP per capita 1.7 2.9 3.4 4.3 4.0 3.7 Prices (2012=100.0) Personal Health Care Price Index 2.2 1.1 1.3 1.7 1.9 2.7 GDP Implicit Price Deflator, chain weighted 1.6 1.3 1.9 2.3 2.3 2.2
SOURCES Centers for Medicare and Medicaid Services, Office of the Actuary, National Health Statistics Group; and Department of Commerce, Bureau of Economic Analysis and Bureau of the Census. NOTES For definitions, sources, and methods for NHE categories, see CMS.gov. National Health Expenditure Accounts: methodology paper, 2017: definitions, sources, and methods [Internet]. Baltimore (MD): Centers for Medicare and Medicaid Services; [cited 2019 Jan 25]. Available from: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/dsm-17.pdf. Numbers might not add to totals because of rounding. Percent changes are calculated from unrounded data. Tables with data for all years of the projection period can be found at CMS.gov. NHE projections 2018–27—tables [Internet]. Baltimore (MD): Centers for Medicare and Medicaid Services; 2019 [cited 2019 Feb 20]. Available from: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/Proj2018Tables.zip. aAnnual growth, 2008–13. bProjected. cIncludes health-related spending for Children’s Health Insurance Program (CHIP), Titles XIX and XXI; Department of Defense; and Department of Veterans Affairs. dEstimates reflect the Bureau of the Census’s definition of resident-based population (which includes all people who usually reside in the fifty states or the District of Columbia but excludes residents living in Puerto Rico and areas under US sovereignty, and US Armed Forces overseas and US citizens whose usual place of residence is outside of the United States). Estimates also include a small (typically less than 0.2 percent of population) adjustment to reflect census undercounts. Projected estimates reflect the area population growth assumptions found in the 2018 Medicare Trustees Report (see note 4 in text).
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7.4 percent per year, as the shift of the babyboom generation into the program continues toresultinrobustgrowthinenrollment(2.5percent per year, on average) (exhibit 2). This shift also contributes to comparatively slower projected private health insurance enrollment growth of just 0.2 percent per year in 2018–27 and underlies the expectation that growth in private health insurance spending will be the slowest among the payers, at just 4.8 percent peryear,onaverage.Medicaidspendinggrowth is expected to be 5.5 percent, on average, with projected enrollment growth of 1.3 percent per year during this period. Per enrollee, rates of growth in spending for Medicare, Medicaid, and private health insurance are expected to be somewhat similar over the ten-year projection period (4.7 percent,
4.1percent,and4.6percentperenrollee,respectively). However, these averages mask the unique year-to-year trends among the major payersthatareinfluencedbyregulation,legislation, and economic factors—each of which is discussed in more detail below. For 2018, national health spending is projected to have grown by 4.4 percent, following a rate of 3.9 percent in 2017 (exhibit 1).1 Faster projected spending growth of almost 2 percentage points in Medicare (5.9 percent) primarily contributes to the acceleration that reflectshigherexpectedgrowthforbothhospital services and prescription drugs. However, Medicaid spending growth is projected to have slowed by 0.7 percentage point in 2018 (to 2.2 percent), as enrollment growth is expected to have slowed for the fourth consecutive year.
Exhibit 2
National health expenditures (NHE) and health insurance enrollment, aggregate and per enrollee amounts, and average annual growth from previous year shown, by source of funds, selected calendar years 2013–27
Source of funds 2013a 2016 2017 2018b 2019b 2027b Expenditure, billions Private health insurance $947.1 $1,136.4 $1,183.9 $1,237.7 $1,278.2 $1,896.7 Medicare 589.9 677.1 705.9 747.4 800.1 1,436.8 Medicaid 445.2 565.6 581.9 594.8 623.4 992.1 Annual growth in expenditure Private health insurance 3.4% 6.3% 4.2% 4.5% 3.3% 5.1% Medicare 5.3 4.7 4.2 5.9 7.1 7.6 Medicaid 5.3 8.3 2.9 2.2 4.8 6.0 Per enrollee spending Private health insurance $ 5,052 $ 5,771 $ 6,001 $ 6,269 $ 6,511 $ 9,384 Medicare 11,503 12,144 12,347 12,726 13,240 19,546 Medicaid 7,553 7,944 8,013 8,099 8,289 12,029 Annual growth in per enrollee spending Private health insurance 4.3% 4.5% 4.0% 4.5% 3.9% 4.7% Medicare 2.4 1.8 1.7 3.1 4.0 5.0 Medicaid 0.9 1.7 0.9 1.1 2.4 4.8 Enrollment, millions Private health insurance 187.5 196.9 197.3 197.4 196.3 202.1 Medicare 51.3 55.8 57.2 58.7 60.4 73.5 Medicaid 58.9 71.2 72.6 73.4 75.2 82.5 Uninsured 44.1 28.7 29.7 29.9 31.2 36.2 Population 315.7 322.9 325.2 327.9 330.7 352.7 Insured share of total population 86.0% 91.1% 90.9% 90.9% 90.6% 89.7% Annual growth in enrollment Private health insurance −0.9% 1.7% 0.2% 0.1% −0.6% 0.4% Medicare 2.9 2.8 2.5 2.7 2.9 2.5 Medicaid 4.4 6.5 2.0 1.1 2.4 1.2 Uninsured 1.2 −13.4 3.7 0.7 4.3 1.9 Population 0.8 0.8 0.7 0.8 0.9 0.8
SOURCE Centers for Medicare and Medicaid Services, Office of the Actuary, National Health Statistics Group. NOTES For definitions, sources, and methods for NHE categories, see CMS.gov. National Health Expenditure Accounts: methodology paper, 2017 (see exhibit 1 notes). Numbers might not add to totals because of rounding. Percent changes are calculated from unrounded data. Tables with data for all years of the projection period can be found at CMS.gov. NHE projections 2018–27—tables (see exhibit 1 notes). aAnnual growth, 2008–13. bProjected.
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Fromtheperspectiveofoverallhealthinsurance enrollment,netgainsinhealthinsurancecoverage across all sources are expected to have kept pacewithoverallpopulationgrowth.Asaresult, the insured share of the population is projected to have remained stable at 90.9 percent. For 2019, growth in national health spending is expected to increase again to 4.8 percent (exhibit 1). Medicare spending growth is projected to continue accelerating (to 7.1 percent), partlyas aresultof fastergrowth inperenrollee spending attributable to higher fee-for-service payment updates. Growth in Medicaid expenditures is also expected to rise (to 4.8 percent), in partbecauseofexpansionsofMedicaidcoverage in Idaho, Maine, Nebraska, Utah, and Virginia. A somewhat mitigating influence on overall national health spending growth, however, is the expected impact of the repeal of the individual mandate. The repeal is expected to result in lowerprivatehealthinsuranceenrollment,since some people—particularly those with directpurchase insurance—may elect to forgo coverage.2,3 Combined,theseshiftsinenrollmentlead to a projected net increase in the number of uninsured of 1.3 million people, to 31.2 million in2019(exhibit 2). However,projectedgains in enrollment through other sources are expected to partially offset those declines, resulting in only a slight decrease in the insured share of the population (to 90.6 percent in 2019, from 90.9 percent in 2018). For2020–27,growthinnationalhealthspendingisexpectedtoaverage5.7percent.Thisrateis fasterthanprojectedfor2019,andfastergrowth is generally evident for the underlying major payers and health care services and goods (exhibits 1 and 3). The acceleration is in part duetofastergrowthinpersonalhealthcarepricesasmeasuredbythePersonalHealthCarePrice Index (exhibit 1). Also contributing is increasingly higher expected growth in utilization on thepartofMedicarebeneficiariesandthosewith privatehealthinsurance,thelatterinfluencedby a lagged response to comparatively higher income growth during 2020–22. With respect to insurance coverage over 2020–27, growth in employer-sponsored health insurance enrollmentis projected to bebelow thatof population growth and decline for those purchasing insurance directly, which contributes to a slight decline in the insured share of the population to 89.7 percent by 2027 (exhibit 2). The share of health care spending sponsored (orfinanced)byfederal,state,andlocalgovernments is expected to increase by 2 percentage points during 2018–27, reaching 47 percent by 2027 (exhibit 4). The increase is entirely accounted for by the federal government share,
which is expected to grow from 28 percent in 2017 to 31 percent in 2027, and largely reflects fastergrowth inMedicare spending as the babyboomgenerationcontinuestotransitionintothe program. The expected business and household share is expected to fall from 55 percent in 2017 to 53 percent in 2027.
Model And Assumptions The national health expenditure projections incorporate a combination of actuarial and econometricmodelingmethods,aswellasjudgments about future events and trends that are expectedtoinfluencehealthspending.3 Theyare largely based on economic and demographic assumptions in the 2018 Medicare Trustees Report,4updatedtoreflectmorerecentlyreleased macroeconomic data.3 The projections also reflect current law5 and do not reflect any policy proposals currently under consideration. Estimates of future health care spending and enrollment are inherently subject to substantial uncertainty that increases over the projection horizon. In addition to the potential effects of evolvinghealthcaremarketsandchangesinlaw over time, economic conditions can differ from the intended midrange assumptions used here. In the case of one economic variable, disposable personal income, analysis by the Office of theActuaryhasconsistentlyfoundarelationship between growth in that metric and growth in health spending, especially for private health insurance.3 That is, as income growth increases or decreases, health spending growth tends to follow in the same direction, but with a lag. Thisrelationshiphasbeenevidentoverthefull history of the National Health Expenditure Accounts and is reflected in these projections.3 As a result, with faster growth in income assumed for the coming decade relative to the recent past, it is expected that health spending growth will respond and be higher as well.3 The projections presented here reflect this relationship. Thus, to the extent that actual growth in income differs from what is assumed, actual growthinhealthspendingmaydifferfromwhat is projected.
Factors Accounting For Growth Inexhibit5averageannualpersonalhealthcare spending6growthisdecomposedtodemonstrate the relative contributions of underlying price growth (economywide and relative personal health care price inflation), use and intensity, population growth, and age-sex mix. During 2018–27 personal health care spending growth is expected to average 5.5 percent, with growth
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in personal health care prices expected to account for nearly half of that growth, on average. Growth in use and intensity is expected to account for just under one-third of the average annual personal health care spending growth, with population growth and the changing age-sex mix of the population accounting for
the remainder. Over specific years within the projection period, however, there are notable trends in prices and the volume and intensity of services, some of which are anticipated to contrast with recent experience. Inflationforhealthcaregoodsandservices,as measured by the Personal Health Care Price
Exhibit 3
National health expenditures (NHE) amounts and annual growth from previous year shown, by spending category, selected calendar years 2013–27
Spending category 2013a 2016 2017 2018b 2019b 2027b Expenditure, billions NHE $2,881.8 $3,361.1 $3,492.1 $3,646.9 $3,823.1 $5,963.2 Health consumption expenditures 2,728.6 3,202.9 3,324.5 3,470.3 3,637.6 5,679.9 Personal health care 2,438.0 2,851.9 2,961.0 3,085.3 3,242.5 5,058.4 Hospital care 937.6 1,092.8 1,142.6 1,193.4 1,254.7 1,961.6 Professional services 759.4 884.0 920.0 962.8 1,013.6 1,541.2 Physician and clinical services 569.6 666.5 694.3 728.0 767.6 1,172.0 Other professional services 78.7 92.4 96.6 100.8 106.1 165.3 Dental services 111.1 125.1 129.1 134.0 139.9 203.9 Other health, residential, and personal care 144.3 173.4 183.1 188.4 196.9 318.6 Home health care 81.4 93.1 97.0 101.8 108.8 186.8 Nursing care facilities and continuing care retirement communities 149.0 163.0 166.3 170.8 178.0 270.7 Retail outlet sales of medical products 366.3 445.6 451.9 468.1 490.5 779.4 Prescription drugs 265.2 332.0 333.4 344.5 360.3 576.7 Durable medical equipment 45.1 51.0 54.4 57.4 60.9 97.8 Other nondurable medical products 56.0 62.7 64.1 66.2 69.3 105.0 Government administration 37.4 44.7 45.0 46.7 49.4 81.0 Net cost of health insurance 174.2 220.7 229.5 247.2 252.0 417.3 Government public health activities 79.1 85.6 88.9 91.1 93.6 123.2 Investment 153.2 158.2 167.6 176.5 185.5 283.3 Noncommercial research 46.7 47.6 50.7 53.5 56.2 83.3 Structures and equipment 106.5 110.6 116.9 123.1 129.3 200.0 Annual growth NHE 3.9% 5.3% 3.9% 4.4% 4.8% 5.7% Health consumption expenditures 4.0 5.5 3.8 4.4 4.8 5.7 Personal health care 4.1 5.4 3.8 4.2 5.1 5.7 Hospital care 5.2 5.2 4.6 4.4 5.1 5.7 Professional services 3.6 5.2 4.1 4.7 5.3 5.4 Physician and clinical services 3.7 5.4 4.2 4.9 5.4 5.4 Other professional services 4.6 5.5 4.6 4.3 5.3 5.7 Dental services 2.2 4.0 3.2 3.8 4.4 4.8 Other health, residential, and personal care 4.9 6.3 5.6 2.9 4.5 6.2 Home health care 6.0 4.6 4.3 4.9 6.8 7.0 Nursing care facilities and continuing care retirement communities 3.0 3.0 2.0 2.7 4.2 5.4 Retail outlet sales of medical products 2.2 6.8 1.4 3.6 4.8 6.0 Prescription drugs 2.0 7.8 0.4 3.3 4.6 6.1 Durable medical equipment 3.3 4.2 6.8 5.5 6.1 6.1 Other nondurable medical products 2.7 3.8 2.2 3.3 4.7 5.3 Government administration 4.2 6.1 0.5 3.9 5.7 6.4 Net cost of health insurance 3.3 8.2 4.0 7.7 2.0 6.5 Government public health activities 3.1 2.7 3.9 2.4 2.8 3.5 Investment 1.7 1.1 6.0 5.3 5.1 5.4 Noncommercial research 1.5 0.7 6.5 5.4 5.1 5.0 Structures and equipment 1.8 1.3 5.7 5.3 5.1 5.6
SOURCE Centers for Medicare and Medicaid Services, Office of the Actuary, National Health Statistics Group. NOTES For definitions, sources, and methods for NHE categories, see CMS.gov. National Health Expenditure Accounts: methodology paper, 2017 (see exhibit 1 notes). Numbers might not add to totals because of rounding. Percent changes are calculated from unrounded data. Tables with data for all years of the projection period can be found at CMS.gov. NHE projections 2018–27—tables (see exhibit 1 notes). aAnnual growth, 2008–13. bProjected.
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Index and inclusive of both economywide and relative personal health care price inflation, is projected to play a larger role in the coming decade (averaging growth of 2.5 percent per year for 2018–27, compared to 1.1 percent for 2014–17)andaccountfornearlyhalfofpersonal health care spending growth. This expectation reflects accelerating growth in both economywide inflation and relative personal health care price inflation (or the difference between price growth for personal health care goods and servicesand economywideinflation).Theexpected acceleration in growth in economywide prices occurredprimarily in 2018. From 2019 forward, asteadyincreaseinrelativepersonalhealthcare priceinflationisprojected,ascertainfactorsthat
contributedtolowornegativegrowthinrelative personal health care price inflation since 2011 are anticipated to be less influential in restraining prices over the next decade. Such factors includerisingsensitivitytopricesbyconsumers and insurers, especially for services subject to cost sharing;7 selective contracting by insurers; and improvements in productivity through the useoflower-costprovidersinphysicianoffices.8 Similarly, input price growth, including healthsector wages, is expected to accelerate as downward pressure on provider prices lessens. The average growth rate for use and intensity of services is projected to be 1.7 percent over 2018–27 and to account for about 30 percent ofpersonalhealthcarespendinggrowth(exhib
Exhibit 4
National healthexpenditures(NHE) amounts,average annualgrowthfromprevious yearshown, and percentdistribution, by type of sponsor, selected calendar years 2013–27
Type of sponsor 2013a 2016 2017 2018b 2019b 2027b Expenditure, billions NHE $2,881.8 $3,361.1 $3,492.1 $3,646.9 $3,823.1 $5,963.2 Businesses, household, and other private revenues 1,620.6 1,836.7 1,914.1 2,002.9 2,095.2 3,136.4 Private businesses 580.4 669.1 696.5 730.9 765.1 1,123.2 Household 833.0 942.8 978.6 1,019.9 1,064.1 1,619.3 Other private revenues 207.2 224.7 239.0 252.0 266.0 393.9 Governments 1,261.2 1,524.4 1,577.9 1,644.0 1,727.9 2,826.8 Federal government 752.7 952.4 982.4 1,032.7 1,089.7 1,833.8 State and local governments 508.5 572.0 595.5 611.2 638.2 993.0 Annual growth NHE 3.9% 5.3% 3.9% 4.4% 4.8% 5.7% Businesses, household, and other private revenues 2.8 4.3 4.2 4.6 4.6 5.2 Private businesses 2.3 4.9 4.1 4.9 4.7 4.9 Household 3.1 4.2 3.8 4.2 4.3 5.4 Other private revenues 3.3 2.7 6.4 5.4 5.6 5.0 Governments 5.3 6.5 3.5 4.2 5.1 6.3 Federal government 6.1 8.2 3.2 5.1 5.5 6.7 State and local governments 4.2 4.0 4.1 2.6 4.4 5.7 Distribution NHE 100% 100% 100% 100% 100% 100% Businesses, household, and other private revenues 56 55 55 55 55 53 Private businesses 20 20 20 20 20 19 Household 29 28 28 28 28 27 Other private revenues 7 7 7 7 7 7 Governments 44 45 45 45 45 47 Federal government 26 28 28 28 29 31 State and local governments 18 17 17 17 17 17
SOURCE Centers for Medicare and Medicaid Services, Office of the Actuary, National Health Statistics Group. NOTES For definitions, sources, and methods for NHE categories, see CMS.gov. National Health Expenditure Accounts: methodology paper, 2017 (see exhibit 1 notes). Numbers might not add to totals because of rounding. Percent changes are calculated from unrounded data. Tables with data for all years of the projection period can be found at CMS.gov. NHE projections 2018–27—tables (see exhibit 1 notes). aAnnual growth, 2008–13. bProjected.
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it5).Thisresultcontrastswiththerateobserved during the years immediately following the implementation of the coverage expansions under theAffordableCareAct(2014–16),whenuseand intensity was the dominant driver of personal health care spending growth—representing 2.9 percentage points, or just over half, of the average spending growth rate of 5.4 percent. Initially, these increases were largely influenced by expanding enrollment, followed by faster per enrollee spending growth that likely reflected care provided to the newly insured. Unlike that unique time period, during 2018–27 growth in the use and intensity of medical care isprimarilyinfluencedbytheanticipatedeffects of macroeconomic growth consistent with the longer-run historical relationship.
Outlook For Spending And Enrollment By Payer Medicare Medicare spending growth is projected to have increased 5.9 percent in 2018, compared to 4.2 percent in 2017 (exhibit 1), mainly because of faster per enrollee spending growth(3.1percentin2018versus1.7percentin 2017) (exhibit 2). Increases in Medicare private health plan payments, as well as spending for fee-for-service hospital care and prescription
drugs, underlie the projected acceleration. In 2019 Medicare spending is projected to increase by 7.1 percent, a 1.2-percentage-point acceleration over growth in 2018. Increases in fee-for-servicepaymentratescomparedto2018, along with slightly faster growth in the use and intensity of physician and clinical services, contribute to faster expected growth in perenrollee spending, which is projected to rise to 4.0 percent. Additionally, projected Medicare enrollment growth reaches its peak at 2.9 percent in 2019, up from 2.7 percent in 2018. Over 2020–27 Medicare spending growth is expected to remain highest among the payers, averaging 7.6 percent. Compared to the 7.1 percent increase projected for 2019, this faster average growth is primarily driven by an expectation of a continued rebound in growth in the useandintensityofservicesusedthroughoutthe period that is more consistent with the program’s long-term experience, compared to that of the past decade. By the end of the projection period (2026–27) the expected growth rate decelerates to around 7.0 percent, down from a projection-period peak of 8.1 percent in 2022, as slower increases in input prices— including for hospitals—and anticipated faster multifactor productivity growth lead to smaller payment updates for many Part A services. En
Exhibit 5
Factors accounting for growth in personal health care (PHC) expenditures, selected calendar years 1990–2027
SOURCES Centers for Medicare and Medicaid Services, Office of the Actuary, National Health Statistics Group; and Department of Commerce, Bureau of Economic Analysis and Bureau of the Census. NOTES “Relative PHC inflation” represents the share of medical price growth that exceeds economywide inflation. “Economywide inflation” reflects the gross domestic product deflator index. “Use and intensity” includes quantity and mix of services. As a residual, this factor also includes any errors in measuring prices or total spending. “Age-sex mix” refers to that mix in the population. Growth in the total PHC Price Index is equal to the sum of economywide and relative PHC inflation and is a chain-weighted index of the price for all personal health care deflators. The height of the bars reflects the sum of factors that contribute positively to growth. In those cases where a factor may contribute growth of less than zero, the net total growth is reflected by the line and associated point estimate noted for each period.
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rollmentgrowthisalsoanticipatedtoslowgradually during these years, from 2.8 percent in 2020to2.1percentby2027—aratemoreconsistent with the pre-baby-boom period. By the end of the projection period the Medicare share of total health spending is projected to rise to 24.1 percentby 2027 from 20.2 percentin 2017. Medicaid Medicaid spending growth is expected to have been just 2.2 percent in 2018, down from 2.9 percent growth in 2017 (exhibit 1)—the fourth consecutive year of slowing growth following the ACA’s expansion of Medicaid coverage in 2014. The expected trend in2018,asinprioryears,isprincipallyexplained by slower growth in enrollment, which is projected to have slowed to 1.1 percent in 2018 from 2.0 percent the previous year (exhibit 2). While growth for nearly all Medicaid services is expected to have slowed in 2018, growth in the net cost for Medicaid managed care plans is expected to have rebounded, compared to a decline in growth in 2017. This pattern reflects the historical and projected timeline over which the federal government is recovering payments from managed care organizations as a result of favorable prior-period experience.1 Growth in Medicaid spending is expected to acceleratein2019to4.8percent.Fiveadditional states have approved and are expected to implement Medicaid expansion in 2019, a factor that contributes in part to the aggregate spending growthincrease.ProjectedMedicaidenrollment growth—2.4percentin2019comparedto1.1percentin2018—reflectsthisnewlyeligiblepopulation. Growth in per enrollee Medicaid spending is expected to accelerate, as well, by 1.3 percentage points to 2.4 percent in 2019, as a result of faster growth in price factors. Medicaid spending is expected to grow at an average rate of 6.0 percent over 2020–27. The patterninannualgrowth,however,isinfluenced byreductionstodisproportionatesharehospital payments for hospitals set in law.9 These payments are scheduled to be reduced in 2020 and are then further reduced in 2021. Consequently, Medicaid spending growth is expected to grow slowly at 5.0 percent in 2020 and 5.4 percent in 2021. For 2022 through 2025, when the disproportionate share hospital payment reductions are equivalent to 2021, overall Medicaid spending growth is expected to be higher at 6.1 percent. Beginning in 2026 there are no reductions in the disproportionate share hospital payments,whichleadstoanotableexpectedoneyear acceleration in 2026 for overall Medicaid spending growth to 7.0 percent. Otherwise, an enrollment mix more heavily influenced by spendingpatternsofcomparativelymoreexpensive aged and disabled beneficiaries is expected
to result in per enrollee spending growth that is at or above 5 percent in every year during 2022–27. Private Health Insurance And Out-OfPocket Spending For private health insurance spending, growth is expected to have increased slightlyfrom4.2percentin2017to4.5percentin 2018, near the overall growth rate for national health expenditures of 4.4 percent (exhibit 1). While spending for most services and goods is expected to have grown slightly faster in 2018,10 the acceleration was partially offset by slower projectedgrowthinthenetcostofprivatehealth insurance,11 as private insurers offering plans in the Marketplace had fared better financially in 2017 and thus reduced the difference between premium revenues and expected benefit payments.12 Out-of-pocket spending growth is expected to have accelerated to 3.6 percent in 2018 from 2.6 percent in 2017, a rate that is consistent with faster income growth as well as with the higher average deductibles for employer-based private health insurance enrollees in 2018 compared to 2017.13 The projected spending trends in 2019 in part reflect the estimated impact of the effective repeal of the individual mandate. As some people choose to forgo maintaining health insurance, private health insurance enrollment is expected to decline slightly, primarily in the directpurchaseinsurancemarket.Accordingly,private health insurance spending growth is expected to slow to 3.3 percent in 2019 from 4.5 percent in 2018. Conversely, out-of-pocket spending is expected to grow more rapidly,at 4.8 percentin 2019 compared to 3.6 percent in 2018, in part because fewer people have private insurance coverage. Private health insurance spending is expected to grow 5.1 percent per year, on average, for 2020–27. Growth in this spending is projected to peak at 5.4 percent in 2023–24, in lagged response to the high anticipated growth in disposable personal income a few years prior. Private health insurance spending growth is then expected to slow to 4.8 percent by 2027, as income growthgenerallydecelerates.As the payer with the slowest expected growth over the full projection period, the private health insurance shareofnationalhealthspendingisprojectedto fall from 33.9 percent in 2017 to 31.8 percent in 2027. Growth in out-of-pocket spending, which is also primarily influenced by economic factors, isexpectedtobesimilartothatofprivatehealth insurance spending in 2020–27, at 5.0 percent. However, the projection-period peak in growth is expected in 2022 (5.4 percent), the year in which the excise tax on high-cost insurance
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plans is scheduled to go into effect.14 By 2027, becausetotalout-of-pocketspendingisexpected to grow more slowly, on average, than health insurance spending (exhibit 1), it is expected to account for a decreasing share of national health spending (9.8 percent in 2027, down from 10.5 percent in 2017).
Outlook For Major Medical Services And Goods Prescription Drugs Following growth of just 0.4 percent in 2017, prescription drug spending isexpectedtohavegrown3.3percentin2018but still be among the slowest-growing health care sectors (exhibit 3). Higher utilization growth is anticipated, compared to the relatively low growth in 2016 and 2017,1 partially driven by anincreaseinthenumberofnewdrugintroductions (fifty-nine in 2018, up from an average of thirty-four during 2016–17).15 In 2019 prescription drug spending growth is projectedtoacceleratefurther,to4.6percent,as a result of higher expected growth in drug utilization(includingfromnewdrugs)andamodest increase in drug price growth. Prescription drug spending is expected to increase, on average, by 6.1 percent per year for 2020–27 (exhibit 3). Contributing to the acceleration in growth during this period is the expectation that the use of prescription drugs will increaseoverthenextseveralyearsasaresultof increasingly robust efforts by employers and insurerstoreduceanybarriersregardingtheuseof maintenance drugs needed to keep their enrolleeswithchronicconditionshealthy.16 Twoother factors contributing to higher expected growth in the use of prescription drugs are the aging of thepopulationandchangestopharmacotherapy guidelines.16 These trends, coupled with faster expected spending increases in lagged response to faster growth in income, result in a peak projected growth rate for prescription drug spending of 6.4 percent in 2023–24. Finally, prescription drug spending growth is expected to rise becauseofashiftintheintensityandmixofdrug usageassociatedwiththemanyprojectscurrently in clinical development that could, over the nextfewyears,resultininnovative,yetmoreexpensive,newdrugsacrosssuchtherapeuticareas as cancer, diabetes, and Alzheimer’s disease.17 Hospitals Hospital spending is expected to have grown similarly in 2018 (4.4 percent) and 2017 (4.6 percent) (exhibit 3). By payer, somewhatslowergrowthinbothMedicaidandprivate healthinsurancehospitalspendingoffsetslightly faster growth in Medicare hospital spending. For2019hospitalspendinggrowthisexpectedto increase to 5.1 percent because of faster growth
in Medicare hospital payment updates and an increaseintheuseofhospitalservicesassociated withnewMedicaidexpansion–relatedenrollees. These increases are somewhat offset by slower expectedgrowthinprivatehealthinsurancehospital spending, which is partially attributable to the repeal of the individual mandate. Over2020–27hospitalspendinggrowthisexpected to average 5.7 percent per year, up from 5.1 percent in 2019. Consistent with overall spending, Medicare is expected to experience the fastest growth in spending for hospital care during this period. The peak growth for overall hospital spending is projected to occur in 2026 (6.1 percent) and is strongly influenced by substantially faster Medicaid spending growth in 2026 that reflects the expiration of Medicaid disproportionate share hospital payment reductions scheduled in current law for September 30, 2025. Private health insurance spending growth for hospital care is expected toreachitsprojection-periodpeakin2024,consistent with the lagged relationship to income. Hospital price growth is also expected to rise by2027.Theaccelerationinthisgrowthoverthe projection period primarily reflects continued wage increases for hospital employees that are anticipated from the low rates of growth experienced following the Great Recession, as well as tighter labor markets for hospital employees, including nurses.18 Growth is partially offset, however, by Medicare payment updates that are reduced by growth in economywide productivity,whichisprojectedtoaccelerateduringthe projection period.4 Physician And Clinical Services Spending in2018forphysicianandclinicalservicesisprojected to have grown 4.9 percent, rising from 4.2 percent in 2017 (exhibit 3). Price growth for physician and clinical services is expected to have increased 0.3 percentage point but to have remained at near historically low rates at 0.7percent.Thiscontinuedlowpricegrowthwas likely influenced, in part, by physician practices using more nonphysicians to provide care, a practicethatwasrelatedtoincreasedproductivity and profits even in the presence of slow price growth.8 The acceleration in overall projected spending growth also reflects faster growth in use that is partly related to a lagged response to growth in income over the recent history and alsofromincreasesinthenumberofofficevisits due to the severe 2017–18 flu season.19 In2019,growthinspendingforphysicianand clinical services is projected to accelerate once more,to5.4percentfrom4.9percentin2018.An accelerationinMedicaidspendinggrowthisthe primary factor contributing to the trend, which is in part associated with program’s expansion
March 2019 38:3 Health Affairs 9
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by additional states. Over the remainder of the projection period, 2020–27, average annual growth in physician and clinical services spending is projected to be 5.4 percent. The growth rate for Medicare spending is expected to be substantially faster than that projected for physician and clinical services spending in private health insurance. Thatprojecteddifferentialislargelyduetofaster enrollment associated with the continued shift ofthebaby-boomgenerationfromprivatehealth insurance to Medicare. Another factor contributing to the growth in overall physician and clinical services spending over 2020–27 is an anticipated acceleration in physicianprice growth.Underlyingthisacceleration are projected rising costs related to the provision of care. In particular, wages are expected to increase as a result of the supply of physicians not being able to meet expected increases in demand for care connected with the aging population.20 Furthermore, some of the productivity gains that have been achieved throughtheuseoflower-costprovidersasasubstitute for physician care within physician practices may be less pronounced in the future, becauseoflimitationssuchaslicensingrestrictions on the scope of care that may be provided by nonphysician providers.21
Conclusion Duringthepasttenyearsthelingeringeffectsof the Great Recession, coupled with the coverage
and payment provisions of the Affordable Care Act, have significantly influenced the trends in healthcarespendingandenrollmentintheUnited States. Over the next decade, however, the outlook for health spending and insurance coverage isexpected to beprimarily drivenby longobserved demographic and economic factors fundamental to the health sector. While the national health spending growth rate is projected to average 5.5 percent per year for 2018–27 (exhibit 1), annual growth is expected to generally accelerate over much of the projection period. Medicare spending growth is expected to accelerate and be the fastest among the major payers, reflecting not only the continued enrollment shift of the baby-boom generationintotheprogrambutalsothegrowthratefor useandintensity,whichisprojectedtogradually increase toward the rates observed during Medicare’s long-term history. Growth in health care prices,reflectingbotheconomywideandrelative personal health care price inflation, is also expected to rebound somewhat toward rates more consistent with the period before the Great Recession and to return to a state in which personal health care price growth exceeds that of economywidepriceinflation.Finally,recentand anticipatedfastergrowthindisposablepersonal income is expected to lead to an increased demand for services, albeit with a lag, and put upwardpressureonthepatternofprivatehealth insurance and out-of-pocket spending growth over the projection period. ▪
The opinions expressed here are the authors’ and not necessarily those of the Centers for Medicare and Medicaid
Services. The authors thank Paul Spitalnic, Stephen Heffler, Aaron Catlin, Micah Hartman, Greg Savord, Cathy
Curtis, and anonymous peer reviewers for their helpful comments. [Published online February 20, 2019. ]
NOTES
1 Martin AB, Hartman M,Washington B, Catlin A, National Health Expenditure Accounts Team. National healthcarespendingin2017:growth slows to post–Great Recession rates; share of GDP stabilizes. Health Aff (Millwood). 2019;38(1):96–106. 2 By 2019 the individual mandate repeal is anticipated to result in about 1.5 million fewer direct-purchasemarket enrollees, who are expected to be somewhat younger and healthier than those who retain coverage, aswellasabout1.0 million fewer employer-sponsoredinsurance-market enrollees, than otherwise would have been projected. After 2019 the enrollment effects are expected to be smaller. Medicaid enrollment is assumed to be unaffected. See Centers for Medicare and Medicaid Services.
Projections of national health expenditures (note 3). 3 Centers for Medicare and Medicaid Services. Projections of national health expenditures: methodology and model specification [Internet]. Baltimore (MD): CMS; 2018 Feb 14 [cited 2019 Feb 4]. Available from: https://www.cms.gov/ResearchStatistics-Data-and-Systems/ Statistics-Trends-and-Reports/ NationalHealthExpendData/ Downloads/Projections Methodology.pdf 4 Boards of Trustees. 2018 annual report of the Boards of Trustees of the Federal Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds [Internet]. Baltimore (MD): Centers for Medicare and Medicaid Services; 2018 [cited 2019 Jan 25]. Available from:
https://www.cms.gov/ResearchStatistics-Data-and-Systems/ Statistics-Trends-and-Reports/ ReportsTrustFunds/Downloads/ TR2018.pdf 5 Consistent with the methods employed in the Medicare Trustees Report (see note 4), these projections assume that payments would continue to be made even after the projected depletion of the Medicare Hospital Insurance trust fund, currently projected to occur in 2026. 6 Personal health care expenditures (PHC) measures the total amount spent to treat people with specific medical conditions. It represents about 85 percent of total national health expenditures over the projection period. 7 Brot-Goldberg ZC, Chandra A, Handel BR, Kolstad JT.What does a
Costs & Spending
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deductible do? The impact of costsharing on health care prices, quantities, and spending dynamics [Internet]. Cambridge (MA): National Bureau of Economic Research; 2015 Oct [cited 2019 Jan 25]. (NBER Working Paper No. 21632).Availablefrom:https://www .nber.org/papers/w21632.pdf 8 Medical Group Management Association [Internet]. Englewood (CO): MGMA; 2018. Press release, New MGMA data shows medical practices utilizing more non-physician providers are more profitable, productive; 2018 Jul [cited 2019 Jan 25]. Available from: https://www.mgma .com/news-insights/press/newmgma-data-shows-medicalpractices-utilizing-mo 9 The current schedule of reductions to Medicaid disproportionate share hospital payments was most recently modified by the Bipartisan Budget Act of 2018. 10 Mercer. Mercer annual survey finds health benefit cost growth will hold at4.1%in2019[Internet].NewYork (NY): Mercer; 2018 Sep 12 [cited 2019 Jan 25]. Available from: https://www.mercer.com/ newsroom/mercer-annual-surveyfinds-health-benefit-cost-growthwill-hold-at-41-in-2019.html 11 The net cost of insurance is the difference between total private health insurance spending and benefits incurred. It includes administrative costs, taxes, net gains or losses to reserves, and profits.
12 Fehr R, Cox C, Levitt L. Individual insurance market performance in mid-2018 [Internet]. San Francisco (CA): Henry J. Kaiser Family Foundation; 2018 Oct 5 [cited 2019 Jan 25]. Available from: https://www .kff.org/health-reform/issue-brief/ individual-insurance-marketperformance-in-mid-2018/ 13 Claxton G, Rae M, Long M, Damico A, Whitmore H. Health benefits in 2018: modest growth in premiums, higher workercontributions at firms with more low-wage workers. Health Aff (Millwood). 2018;37(11): 1892–900. 14 Inresponsetotheexcisetaxonhighcost insurance plans, some employers are expected to reduce the value of their health insurance benefits to remain below tax thresholds, which would result in higher cost sharing for employees. 15 Food and Drug Administration, Center for Drug Evaluation and Research. Advancing health through innovation: 2018 new drug therapy approvals [Internet]. Silver Spring (MD):FDA;2019Jan[cited2019Jan 25]. Available from: https://www .fda.gov/downloads/Drugs/ DevelopmentApprovalProcess/ DrugInnovation/UCM629290.pdf 16 IQVIA. 2018 and beyond: outlook andturningpoints.Parsippany(NJ): IQVIA; 2018 Mar 13. 17 EvaluatePharma. World preview 2018, outlook to 2024 [Internet]. London: EvaluatePharma; 2018 Jun [cited 2019 Jan 25]. Available from:
http://info.evaluategroup.com/rs/ 607-YGS-364/images/WP2018.pdf 18 Evans M. U.S. hospital profits fall as labor costs grow and patient mix shifts.Wall Street Journal [serial on the Internet]. 2018 Apr 23 [cited 2019 Jan 25]. Available from: https://www.wsj.com/articles/u-shospital-profits-fall-as-labor-costsgrow-and-patient-mix-shifts1524495601 19 Garten R, Blanton L, Elal AIA, Alabi N, Barnes J, Biggerstaff M, et al. Update: influenza activity in the United States during the 2017–18 season and composition of the 2018–19 influenza vaccine. MMWR Morb Mortal Wkly Rep. 2018; 67(22):634–42. 20 Dall T, West T, Chakrabati R, Reynolds R, Iacobucci W. 2018 update: the complexities of physician supply and demand: projections from 2016 to 2030: final report [Internet].Washington (DC): IHS Markit; 2018 Mar [cited 2019 Jan 25]. Available from: https://aamcblack.global.ssl.fastly.net/ production/media/filer_public/85/ d7/85d7b689-f417-4ef0-97fbecc129836829/aamc_2018_ workforce_projections_update_ april_11_2018.pdf 21 Hoffman M. Can nurse practitioners fill the void in primary care? MD [serial on the Internet]. 2018 Apr 19 [cited 2019 Jan 25]. Available from: https://www.mdmag.com/medicalnews/can-nurse-practitioners-fillthe-void-in-primary-care
March 2019 38:3 Health A

Concept of a Love for Cash is introduced: Physical money worship & strong emotional attachment to it; next to fear of technology, is one of the most important reasons for the resistance to cashless banking

Banking, Unbanking, and New Banking. Dominika Maison. The Psychology of Financial Consumer Behavior pp 185-208, https://link.springer.com/chapter/10.1007/978-3-030-10570-9_6

Abstract: This chapter is dedicated to analysing the psychological factors that support or hinder different levels of banking (having an account, use of a payment card, and use of mobile banking). There are still many people in countries across the world who do not have a bank account or do not use payment cards, which leads to financial exclusion. Apart from the obvious reason for being unbanked resulting from infrastructure limitations (e.g., limited access to bank branches or payment terminals), there are also psychological factors that can affect the level of banking service use. A new model of the levels of banking service use and the results of quantitative and qualitative research are presented in this chapter, revealing the specific nature of functioning on each of the identified levels of banking, along with the psychological barriers relating to moving up banking levels. The barriers to having a bank account and to acceptance of cashless financial behaviours are discussed. A new concept of a Love for Cash is introduced that refers to physical money worship and strong emotional attachment to physical money. Love for Cash, next to fear of technology, was found to be one of the most important reasons for the resistance to cashless banking.

Keywords: Financial exclusion Model of levels of banking service use Love for Cash scale Money worship Cashless financial behaviour

Los Angeles county: Streetcars' influence, visible in current urban density, has not dissipated in the 60 years since the streetcar's removal; mutually reinforcing pathways of regulation & agglomerative clustering

Vestiges of Transit: Urban Persistence at a Micro Scale. Leah Brooks and Byron Lutz. Review of Economics and Statistics, March 04, 2019. https://doi.org/10.1162/rest_a_00817

Abstract: We document intra-city spatial persistence and its causes. Streetcars dominated urban transit in Los Angeles County from the 1890s to the early 1910s, and were off the road entirely by 1963. However, we find that streetcars' influence remains readily visible in the current pattern of urban density and that this influence has not dissipated in the 60 years since the streetcar's removal. We examine land use regulation both as a consequence of streetcars and as a mechanism for streetcars' persistent effect. Our evidence suggests that the streetcar influences modern behavior through the mutually reinforcing pathways of regulation and agglomerative clustering.

EconLit codes: R3, R4, R5, N9

 We document intra-city spatial persistence and its causes.

Bias Blind Spot: They rated themselves lower than others in both susceptibility to biases & personal shortcomings; belief in free will was positively associated with the susceptibility to better than average effect

Agency and self-other asymmetries in perceived bias and shortcomings: Replications of the Bias Blind Spot and extensions linking to free will beliefs. Prasad  Chandrashekar et al. March 2019. DOI: 10.13140/RG.2.2.19878.16961

Description: Bias Blind Spot is the phenomenon that people tend to perceive themselves to be less susceptible to biases than others. In three pre-registered experiments with samples from Hong Kong and the United States (overall N = 969), we replicated two experiments (Study 1-Survey 2 and Study 2) from Pronin, Lin, and Ross (2002), the first published demonstration of the effect. Participants rated themselves lower than others in both susceptibility to biases (mini meta-analysis: dz = - 1.00) and personal shortcoming (mini meta-analysis: dz = - 0.34). The self-other asymmetry of susceptibility for biases was larger than that of personal shortcomings (dz = - 0.43). Thus, the replication findings provide strong empirical support for the bias blind spot phenomenon. Extending the replications, belief in free will was positively associated with the susceptibility to better than average effect, and also with a stronger self-other asymmetry in rating personal shortcomings.


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The bias blind spot is the phenomenon that people tend to perceive themselves as less
biased than others in their judgements and behaviors (Pronin, 2007; Pronin et al., 2002) and
further tend perceive their peers as more subject to the bias blind spot than they are. Broadly,
people seem to be able to detect or infer biases in others but fail to do so about themselves
(Pronin, Gilovich, & Ross, 2004). 
We had two main goals for the present investigation: (1) to conduct replications of the
bias blind spot effect, and (2) to examine extensions about the link between agency beliefs and
the bias blind spot. The proposed extension was meant to answer further calls for future research
to investigate possible “causes of people’s unwarranted faith in their own introspections?”
(Pronin, 2007, p. 41). We begin by introducing the literature on the bias blind spot and the chosen
target article for replication - Pronin et al. (2002). We then introduce agency beliefs and
hypothesize about the relationship between free will beliefs and the bias blind spot effect.
Bias blind spot and free will beliefs                  5
Bias blind spot
People have access to their own private mental lives but not to that of others, and so to
overcome this information asymmetry people continuously aim to detect and infer other people's
internal psyche. This information asymmetry often results in unintended and unaware self-other
attributional asymmetries (Nisbett & Ross, 1980). 
Many such attribution asymmetries have been documented over the years, such as the
widely discussed actor-observer bias (Jones & Nisbett, 1972), in that divergent responses of
others are perceived to reflect others’ stable personality dispositions (Jones, 1990). Individuals
also tend to believe and perceive their perceptions of the world are objective and accurate, coined
as "naïve realism" (Ross & Ward, 1995), easily detecting flaws in this assumption in others but
failing to detect such flaws in themselves. They may further assume that they self-reflect more
than others do, since when people evaluate their own behaviors and judgments, they base their
evaluations on introspection, whereas when evaluating others their assessments must rely on
others' behavior (Pronin, 2007; Pronin et al., 2004; Ross & Ward, 1995; Wilson & Dunn, 2004).
Finally, people tend to view themselves in a positive light or construe reality in a way that would
make for a more positive self-evaluations. Together, naïve realism, introspection illusion, and
self-enhancement motives, result in a bias blind spot, a self-other asymmetry in perceptions bias.
People further reject and persist in their biased perceptions even if being made aware of its
existence (Ehrlinger, Gilovich, & Ross, 2005; Pronin, 2007; Pronin & Kugler, 2007).
The chosen bias blind spot experiments
Pronin et al. (2002)’s work was the first demonstration of the bias blind spot. The article
has been influential with over 765 citations according to Google Scholar at the time of writing,
with theoretical developments and applications across several domains, such as judgment and
Bias blind spot and free will beliefs                  6
decision making, behavioral economics, assessment, and interpersonal and intergroup conflict
(e.g., Pronin et al., 2004). Bias blind sport is argued to be a distinct meta-bias with clear
implications on judgment and behavior (Scopelliti et al., 2015). For example, subsequent
empirical research showed relevance of the effect to law in viewing and remembering criminal
events (Jones, Crozier, & Strange, 2018), with observations of the effect in children as young as
six years old (Hagá, Olson, & Garcia-Marques, 2018), and evidence for the persistence of bias
despite being shown how it affected previous decisions (Hansen, Gerbasi, Todorov, Kruse, &
Pronin, 2014). To the best of our knowledge, there have been no previous attempts for a direct
replication of the experiments reported in the paper. 
The target article consisted of three experiments, and the current replications focused on
Survey 2 of Study 1 and Study 2. Findings in Pronin et al. (2002) and summarized in Table 1.
In their Study 1, participants separately rated their own and average Americans'
susceptibility to eight cognitive and motivational biases and three personal shortcoming biases.
Pronin et al. (2002) proposed that participants would demonstrate clear asymmetry in
susceptibility to biases but not to shortcoming items because people are likely to be aware of their
visible personal shortcomings than their invisible cognitive biases. In view of this, they proposed
and tested three hypotheses. The first hypothesis suggested that participant would rate themselves
as less susceptible than others on biases. The second hypothesis suggested that there should be no
difference between the rating of self-susceptibility and other’s susceptibility towards personal
shortcomings (null hypothesis). The third hypothesis combining the first two suggested that
asymmetries effects would, therefore, be larger for biases than for personal shortcoming items.
Bias blind spot and free will beliefs                  7
In their Study 2, participants compared themselves to others on six personality
dimensions, three positive and three negative. Findings indicated that participants rated
themselves as higher on positive personality dimensions and lower on negative ones, compared to
others. Furthermore, a large majority (76 %) of participants who claimed better-than-average
status insisted on this status even when alerted to the possible bias.
Adjustments to original design
We attempted a close replication of the original study by Pronin et al. (2002) yet made
several needed adjustment. First, we administered all surveys via an online Qualtrics survey.
Second, the two studies from Pronin et al. (2002) chosen for replication included Stanford
University undergraduate students and were not paid for completion of the study. The current
replication effort of three studies included one undergraduate sample from a university in HK,
and two paid samples using Amazon Mechanical Turk (MTurk). Thirdly, Study 2 and Study 3
replications combined the original article' Studies 1 and 2 into an integrated design. Forth, we
went beyond the replication and added extensions to the original design to examine the link
between agency beliefs and self-other bias asymmetries.
Extensions - belief in free will as a predictor of the bias blind spot
We aimed to extend the replication study by considering individuals’ perceived agency as
a predictor of the bias blind spot - whether perceiver’s beliefs in free will predict self-other
asymmetries regarding biases and personal shortcomings. Belief in free will is the general belief
that human behavior is free from internal and external constraints across situations (Feldman,
2017; Monroe & Malle, 2014). Studies on folk understanding of free will found that people
normally associate free will with having choice and understand free will as the absence of
Bias blind spot and free will beliefs                  8
internal and external constraints (Baumeister, 2008; Feldman, Baumeister,  & Wong, 2014;
Monroe, Dillon,  & Malle, 2014; Monroe & Malle, 2010; Vonasch, Baumeister, & Mele, 2018).
Belief in free will has been associated with a range of adaptive behavioral and
psychological outcomes such as academic and job performance (Feldman, Chandrashekar, &
Wong, 2016; Stillman et al., 2010), perseverance for long-term goals (Li, Zhao, Lin, Chen, &
Wang, 2018), self-control (Goto, Ishibashi, Kajimura, Oka,  & Kusumi, 2018), expressions of
love (Boudesseul, Lantian, Cova, & Bègue, 2016), job satisfaction (Feldman, Farh, & Wong,
2018), cooperation (Protzko, Ouimette, & Schooler, 2016), and well-being and meaning in life
(Crescioni, Baumeister, Ainsworth, Ent, & Lambert, 2016; Moynihan, Igou, & van Tilburg,
2017).
Although the research outlined above largely emphasize the effects of believing in free
will on individuals’ self-regulatory behavior, some recent work suggests that free-will beliefs
affect fundamental social-cognitive processes that are implicated in the judgements of self and
others. For example, Genschow, Rigoni, and Brass (2017) found a positive relationship between
the strength of the belief in free will and the correspondence bias, i.e., tendency to endorse
dispositional explanations than situational explanations. People with a strong belief in free will
tend to view the actions of wrongdoers as the result of choices that are freely made and therefore
endorse harsher punishments (Clark et al., 2014; Martin, Rigoni, & Vohs, 2017). Similarly, free
will beliefs influence judgements about the extent to which individuals’ choices determine their
economic outcomes (Mercier et al., 2018), and more broadly form the basis of a capacity for
change, not only for others but also for the self (Feldman, Wong, & Baumeister, 2016).
Bias blind spot and free will beliefs                  9
As discussed above, several findings are suggestive of the possible relationship between
free will beliefs and the bias blind spot. Personal shortcomings can be viewed as a form of
internal constraints of free will (e.g., fear of public speaking, planning fallacy, and
procrastination). Those with stronger free will beliefs are likely to perceive own behaviors as the
outcome of their own choices (Feldman et al., 2014) and have more control over decisions in life
(Rigoni, Kühn, Gaudino, Sartori, & Brass, 2012; Stillman, Baumeister, & Mele, 2011). In
summary, free will beliefs reflect a view of the self as an active agent with freedom to choose
actions and pursue goals, and therefore, should encourage the view of the self as devoid of
internal constraints that may impose limitations on self’s ability to make choices. Supporting this
view, people with a history of addiction to alcohol, tobacco, and other drugs rate themselves
lower on belief in free will (Vonasch, Clark, Lau, Vohs, & Baumeister, 2017). This is suggestive
of a negative relationship between free will beliefs and perceived personal shortcomings.
Furthermore, the work on the association between free will beliefs and correspondence bias
suggest that free-will beliefs would be associated with perceiving others as more affected by their
personal shortcomings.  Combined, the two are suggestive of a positive relationship between free
will beliefs and bias blind spot regarding personal shortcoming. We initially made no pre
registered predictions regarding perceived biases although similar arguments can be made.
Exploratory Hypotheses
We did not make priory predictions regarding associations between free will beliefs and
susceptibility to biases of the self and others. Belief in free will at its core is experienced as an
increased sense of agency, therefore believers perceive their own behaviors as generated by
themselves, rather than external forces (Rigoni et al., 2012). They therefore view their own
judgements and behaviors as lacking in biases, and by extension, may exhibit larger self-other
Bias blind spot and free will beliefs                  10
asymmetry in perceived bias. Extending on this argument toward susceptibility to better than
average effect, the belief in free will likely to be negatively associated with ratings of negative
personality dimensions of self in comparison to others, and positively associated with positive
personality dimensions of self in comparison to others.
Overview of empirical studies
There was a two weeks gap between the two data collections of Studies 1 and 2. In each of
the replication studies, we first pre-registered the experiment on the Open Science Framework
(OSF) and data collection was launched later that week. Pre-registrations, power analyses, and all
materials used in these experiments are available in the supplementary materials. OSF pre
registration review links: Study 1a -
https://osf.io/fwthk/?view_only=744526890b674a9fbec72acc37a79c86 ; Study 1b -
https://osf.io/qmcrn/?view_only=4820ad08078b4b5a860b08c0234c7229 ; Study 2 -
https://osf.io/fm48b/?view_only=60e6cf6df39147e0af1b28f4e7da0d4c.
In light of findings from the first two studies, Study 3 was designed to extend the findings.
Importantly, we wanted to replicate the proposed extensions in the Study 2 with a larger sample
to be able to detect smaller effect sizes. We preregistered our hypotheses and analysis plan on the
OSF, review link: https://osf.io/u3vds/?view_only=42450fc3d6b74866a1c022e7bfd299a9. 
Data and R/RMarkdown code for all studies is available on the OSF, review link:
https://osf.io/3df5s/?view_only=b29f8571eb874448907ce45c7379e371 . Full open-science
details and disclosures are provided in the supplementary. All measures, manipulations,
exclusions conducted for this investigation are reported, all studies were pre-registered with
Bias blind spot and free will beliefs                  11
power analyses reported in the supplementary, and data collection was completed before
analyses. 
Studies 1a and 1b
Studies 1a and 1b were meant as a pre-test of the effects in an undergraduate class.
Students worked in teams of 3-6 to design and run a series of replications, two of those were
Pronin et al.'s Study 1 Survey 2 and Study 2 corresponding to our Study 1a and 1b. The students
then served as the target sample for the experiments designed by their classmates, experiments
they were not involved in designing and had no prior knowledge of. The course materials covered
judgement and decision-making biases, which meant that the students were made aware a wide
array of other biases, and the experiments are, therefore, very conservative tests of the effect in a
non-naive sample.
Students were randomly assigned into groups and to the study for replication. Student
groups designed the survey, conducted effect size and confidence intervals calculations,
conducted power analyses, and wrote the pre-registrations for Studies 1a and 1b. The course
instructor completed the pre-registration on OSF and data collection. All the students registered
in the course were invited to take part as respondents in the study. To ensure anonymity, students
were only asked to indicate which replication group they belonged to and those were later
excluded from the data analysis of the study they designed. 
Participants and procedures
A total of 49 undergraduate students took part in the online course survey, and of those we
excluded the four students who designed Study 1a and six students who designed Study 1b,
Bias blind spot and free will beliefs                  12
resulting in a sample of 45 for Study 1a (Mage = 20.20, SD = 0.99; 31 females) and 43 for Study
1b. 
Study 1a
Measures
Biases and Personal shortcomings. 
Participants were presented with descriptions of eight biases and three personal
shortcomings: self-serving attributions for success or failures, dissonance reduction after free
choice, positive halo effect, biased assimilation of new information, reactive devaluation of
proposal one’s negotiation counterparts, perceptions of hostile media bias toward one’s group or
cause, fundamental attribution error (FAE) in “blaming the victim,” and judgments about the
“greater good” influences personal self-interest, procrastination, fear of public speaking, and
planning fallacy. The supplementary includes detailed descriptions of the biases and personal
shortcomings.
For each of the descriptions participants rated on their own susceptibility and
susceptibility of the average student at the university. Ratings were on a nine-point scale (1 = not
at all; 9 = strongly). 
Results and discussion
Descriptive statistics of the ratings on the susceptibility to bases biases and personal
shortcomings are presented in Table 2 (see supplementary for the descriptive statistics and plots
for each of the biases and personal shortcomings). We conducted the paired sample t-test to test
the hypothesis, summarized in Table 3.
Bias blind spot and free will beliefs                  13
Results of paired t-tests (one-tailed) indicated that participants, consistent with the original
study, reported themselves as less susceptible to biases (M = 5.60, SD = 0.86), than the average
students in the university (M = 6.35, SD = 0.91), Md  = -0.75, t (44) = -4.54, p <.001, dz = -0.68,
95% CI [-1.01, -0.35] (Plotted in Figure 1). Self-others asymmetry was found for all individual
biases except for cognitive dissonance (Table S3 in the supplementary).
In the original study, the authors made no prediction regarding self-other personal
shortcomings asymmetry. We conducted a two-tail dependent t-test but failed to find support for
any differences with a weak effect for high ratings of self (self: M = 6.20, SD = 1.78; others: M  =
6.49 , SD = 1.23; Md  = -0.29; t (44) = -1.13, p = .265; dz = -0.17, 95% CI [-0.47, 0.13]; see Figure
2 and Table S3 in the supplementary for details per each shortcoming). Quite possibly, as in the
original article, the small sample failed to detect a weak effect.
Finally, self-other bias asymmetry (M = -0.75, SD = 1.11) was stronger than self-other
personal shortcomings asymmetry (M = -0.29, SD = 1.72; Md = -0.46, t (44) = -1.97, p = .055, dz
= -0.29, 95% CI [-0.60, 0.01]; see Figure 3).
Study 1b 
Measures
Assessed Personality dimensions. 
Participants were presented with three positive and three negative personality dimensions
in randomized order. The positive personalities assessed were dependability, objectivity, and
consideration. The negative personalities assessed were snobbery, deceptiveness, and selfishness.
The ratings were made on a 9-points (1 = much lower than the average student; 5 = same as the
average student; 9 = much higher than the average student).
Bias blind spot and free will beliefs                  14
Bias recognition. 
After rating their personalities, participants were briefed of the better-than-average effect
and asked whether they were influenced by the bias when assessing their personalities (1  -
Objective measures would rate me lower on positive characteristics and higher on negative
characteristics than I rated myself; 2 - Objective measures would rate me neither more positively
nor more negatively than I rated myself; 3 - Objective measures would rate me higher on positive
characteristics and lower on negative characteristics than I rated myself). 
Results and discussion
Table 4 details descriptive statistics and Table 5 summarizes statistical tests (Table S4 in
the supplementary details ratings for each personality dimension).
We conducted one-sample one-tail t-tests and found that participants rated themselves as
having more positive personality dimensions (M = 5.74, t (42) = 5.09, p < .001, dz = 0.78, 95% CI
[0.43, 1.11]) and less negative personality dimensions than others (M = 4.16, t (42) = -4.55, p <
.001, dz = -0.69, 95% CI [-1.02, -0.36]). 
We then conducted a chi-squared test to test the hypothesis that the majority of
participants deny having the better-than-average effect, comparing to a 50%-50% random split.
Despite being made aware of the potential bias, only 9 of the 43 participants (21%)
acknowledged their potential bias leaving 79% of participants still claiming to be better than their
average peers (χ2 (1, N = 43) = 14.53, p < .001, dz = 1.43, 95% CI [0.69, 2.16]). 
Findings supported the better than average effect and denial of their own bias. Effect size
(dz = 1.43) of the replication was almost two times greater than the effect size of the original
Bias blind spot and free will beliefs                  15
study (dz  = 0.70) and the replication’s confidence intervals ([0.69, 2.16]) includes the original
effect size point estimate. We conclude the replications as successful.
Study 2
Method
Participants and procedures
A total of 303 American Amazon Mechanical Turk (MTurk) participants completed the
study using TurkPrime.com (Mage = 38.45, SD = 11.58; 166 females). First, participants rated
their free will beliefs on two scales and then rated their and others susceptibility to the
descriptions of eight biases and three personal shortcomings. The design was a 2 (self and other
ratings) by 2 (biases and personal shortcomings) within-subject design and display of conditions
was counterbalanced (see supplementary for more details and full measures). Participants then
answered a funneling section and provided demographic information. 
Measures
Belief in Free will. 
Free will beliefs (BFW) were measured using two free-will belief subscales: 5 items
measure of general BFW (Nadelhoffer, Shepard, Nahmias, Sripada, & Ross, 2014) (1 = Strongly
disagree, 7 = Strongly agree; 𝛼 = 0.91) and BFW personal agency subscale (Rakos, Laurene,
Skala, & Slane, 2008) (4 items; 1 = Not true at all, 5 = Almost always true; 𝛼 = 0.92). Details of
all measures are provided in the supplementary. 
Bias blind spot and free will beliefs                  16
Biases and Personal shortcomings. 
Similarly to Study 1a, participants rated their own and other average Americans
susceptibility to biases and personal shortcomings (1 = not at all; 9 = strongly).
Results
Descriptive statistics are provided in Table 6 and statistical tests summary in Table 7 (see
Table S6 and Table S7 and Figures S5 to S8 in the supplementary for each of the biases and
personal shortcomings separately).We conducted a dependent sample t-test and found that
participants' perceived susceptibility to biases for self (N = 303; M = 4.64, SD = 1.35) was lower
than of others (M = 5.78, SD = 1.16 ; Md = -1.15; t (302) = -16.16, p < .001; dz = -0.93, 95% CI [
1.06, -0.79]; see Figure 4), and the self-other asymmetry effects were similar across all eight
biases (p < .001; see Table S8 in supplementary). In comparison, the original study found support
for only four of the eight biases and with weaker effects, possibly due to lacking power.
The original study found no support for self-other asymmetry in perceived personal
shortcomings, and the hypothesis was for a null (or weaker) effect. We conducted a dependent
sample t-test and found perceived personal shortcomings (M = 5.35, SD = 1.88) were lower than
perceived susceptibility to biases of other MTurk workers (M = 5.87, SD = 1.35; Md  = -0.52; t
(302) = -5.22, p <.001, dz = -0.30, 95% CI [-0.42, -0.18]; see Figure 5). The original study
reported self as lower than others for all three perceived personal shortcomings, yet it was not
reported if any of the results reached statistical significance. Our dependent sample t-tests found
support for an asymmetry for two out of three personal shortcomings (procrastination and
planning fallacy; see Table S8 in supplementary for details on each of the personal
shortcomings). These findings deviate from the findings of the original study.
Bias blind spot and free will beliefs                  17
Based on the findings in the original study, we expected a significant difference between
the biases and personal shortcomings asymmetries. We conducted a dependent sample t-test and
indeed found that self-other biases asymmetry (M = -1.15, SD = 1.24) was larger than the self
other personal shortcomings asymmetries (M = -0.52, SD = 1.75; N = 303 ; Md = -0.62, t (302) =
6.39, p <.001, dz = -0.37, 95% CI [-0.48, -0.25]; see Figure 6).
Finally, we examined the link between free will beliefs and perceived personal
shortcomings of self and others. Pearson correlations are detailed in Table 8. Both free will
beliefs scales were negatively correlated with perceived self personal shortcomings (general free
will: r = -0.22, p < .001, 95% CI [-0.32, -0.11]; personal agency: r = -0.17, p = .003, 95% CI [
0.28, -0.06]). However, we found no support for a link between free will beliefs measures and
perceived shortcomings in others (general free will: r = 0.00, p = .941, 95% CI [-0.11, 0.12];
personal agency: r = 0.05, p = .357, 95% CI [-0.06, 0.16]). Free will beliefs negatively correlated
with personal shortcomings self-other asymmetry (general free will: r = -0.24, p < .001, 95% CI
[-0.34, -0.13]; personal agency: r = -0.22, p < .001, 95% CI [-0.33, -0.11]). 
Probing the link between free will beliefs and susceptibility to biases we only found
support for personal agency subscale as negatively correlated with self-other asymmetry for
susceptibility to bias (r = -0.17, p  = .003, 95% CI [-0.28, -0.06]). We did not find support for
correlations between free-will beliefs and any of the measures associated susceptibility to biases:
self-bias (general free will: r  = -0.01, p = .811, 95% CI [-0.13, 0.10]; personal agency: r = -0.11, 
p = .055, 95% CI [-0.22, 0.00]), and others' bias (general free will: r = 0.02, p = .737, 95% CI [
0.09, 0.13]; personal agency: r  = 0.05, p = .366,  95% CI [-0.06, 0.16]).
Bias blind spot and free will beliefs                  18
Study 3
Method
Participants and procedures
A total of 621 American MTurk participants completed the study using TurkPrime.com
(Mage = 39.15, SDage = 11.88; 346 females). The rationale of the present study was as follows:
First, to test the robustness and reliability of the replication results found in Study 1a, Study 1b,
and Study 2 with a larger sample. Second, more importantly, replicate the proposed extension
hypotheses between free will beliefs and perceived personal shortcomings of self and others. 
Study 3 combined Study 1a and Study 1b as one single study. Procedures were modeled to
remain as close as possible to the original studies. The study included three parts. Participants
first completed the measures of free will beliefs. Then, in randomized order, participants rated
their and others susceptibility to given descriptions of eight biases and three personal
shortcomings, and compared themselves to others on three positives and three negative
personality dimensions with a test for recognition of their bias.
Measures
The measures for biases and personal shortcomings followed the design of Study 2. The
measures of personality dimensions and bias recognition followed were the same as Study 1b.
Free will beliefs were measured by three of the most common scales: eight items of the free-will
and determinism personal will sub-scales (Rakos et al., 2008) (0 = Not true at all, 4 = Almost
always true; 𝛼  = 0.74), five items of the general free will beliefs scale (Nadelhoffer et al., 2014)
(1 = Strongly disagree, 7 = Strongly agree; 𝛼 = 0.89) and the seven items from free will and
Bias blind spot and free will beliefs                  19
determinism plus scale (Paulhus & Carey, 2011) (1 = Not at all true, 5 = Always true; 𝛼  = 0.85;
recoded from a scale of 0 to 4 to match the original scale range).
Results
Table 9 details descriptive statistics and Table 10 summarizes statistical tests (see Table
S11 and Table S12 and Figures S9 to S12 in supplementary for each of the biases and personal
shortcomings separately).
Perceived susceptibility to bias and personal shortcomings
We conducted a series of dependent sample t-test mirroring Studies 1a and 2 (N = 621).
Perceived susceptibility to biases was lower for self (M = 4.69, SD = 1.30) than for others (M =
6.48, SD = 1.04; Md  = -1.80; t (620) = -32.04, p <.001, dz = -1.29, 95% CI [-1.39, -1.18]; see
Figure 7), in all biases (p < .001; see Table S13 in supplementary). Perceived shortcomings were
lower for self (M = 5.52, SD = 1.71) than for others (M = 6.25, SD = 1.16; Md = -0.73; t (620) =
10.54, p <.001, dz = -0.42, 95% CI [-0.51, -0.34]; see Figure 8), especially in procrastination and
planning fallacy (see Table S13 in supplementary). Self-other asymmetry was larger for biases
(M = -1.80, SD = 1.40) than for personal shortcomings (M = -0.73, SD = 1.73; Md  = -1.06; t (620)
= -13.01, p <.001, dz = -0.52, 95% CI [-0.61, -0.44]; see Figure 9). The results align with the
findings in Study 2.
Denying Personal Susceptibility to the Better Than Average Effect
We conducted a series of one-sample t-tests mirroring Study 1b. Participants rated
themselves as possessing more positive personality dimensions (M = 6.42; t (620) = 31.74, p
<.001; dz = 1.27, 95% CI [1.17, 1.38]) and less negative personality dimensions (M = 3.21; t
(620) = -30.38, p <.001; dz = -1.22, 95% CI [-1.32, -1.11]), compared to others. 
Bias blind spot and free will beliefs                  20
To assess denial of the bias, we conducted a chi-square comparing to a 50%-50% split.
Only 109 of the 621 participants (18%) admitted bias, leaving 82% denying the bias (χ2 (1, N =
621) = 261.53, p < .001; dz = 1.71, 95% CI [1.50, 1.91]).
Free-will beliefs and biases
Finally, we examined the link between free will beliefs and perceived personal
shortcomings of self and others. Pearson correlations are detailed in Table 11.
Belief in free will and personal shortcomings. 
Personal shortcomings for self was negatively associated with free-will beliefs (general: r
= -0.16, p < .001, 95% CI [-0.23, -0.08]; personal agency: r = -0.15, p < .001, 95% CI [-0.22,
0.07]; personal will: r = -0.09, p = .022, 95% CI [-0.17, -0.01]). However, we found no consistent
support for a link between free will beliefs measures and perceived shortcomings in others
(general: r = 0.02, p = .551, 95% CI [-0.05, 0.10]; personal agency: r = 0.05, p = .231, 95% CI [
0.03, 0.13]; personal will: r = 0.11, p = .007, 95% CI [0.03, 0.18]). 
Free will beliefs negatively correlated with personal shortcomings self-other asymmetry
(general free will: r = -0.17, p < .001, 95% CI [-0.25, -0.09]; personal agency: r = -0.18, p < .001,
95% CI [-0.25, -0.10]; personal will: r = -0.16, p < .001, 95% CI [-0.24, -0.09]).
Probing the link between free will belief and susceptibility to biases we found support for
personal will as negatively correlated with susceptibility to bias of the self (r = -0.14, p < .001,
95% CI [-0.22, -0.06]) and positively correlated with the susceptibility to bias of others (r = 0.12,
p = .003, 95% CI [0.04, 0.20]). Overall personal will was negatively correlated with self-other
asymmetry for susceptibility to bias (r = -0.22, p < .001, 95% CI [-0.29, -0.14]). We found no
Bias blind spot and free will beliefs                  21
support for a correlation with the two other measures of free will beliefs (correlations ranged
between 0.01 CI [-0.07, 0.09] and -0.03 CI [-0.11, 0.05]).
Belief in free will and better than average effect 
We found support for an exploratory negative relationship between free-will beliefs and
negative personality dimensions (general free-will: r = -0.09, p = .033, 95% CI [-0.16, -0.01];
personal agency: r = -0.16, p < .001, 95% CI [-0.23, -0.08]; personal will: r = -0.12, p = .003,
95% CI [-0.20, -0.04]). Positive personality dimensions were positively correlated with personal
will (r = 0.15, p < .001, 95% CI [0.07, 0.23]), but no support for a positive correlation with the
two other measures (General free will: r = 0.04, p = .341, 95% CI [-0.04, 0.12]; Personal agency:
r = 0.05, p = .222, 95% CI [-0.03, 0.13]).
Denial of bias correlated with general free-will (r = 0.11, p = .007, 95% CI [0.03, 0.19])
and personal agency (r = 0.14, p < .001, 95% CI [0.06, 0.22]), with no support for personal will
(r = 0.03, p = .531, 95% CI [-0.05, 0.10]). 
Overall, the results of the free will related findings are consistent with the results of Study
2.
General Results: Mini Meta-Analysis
We summarized the findings of the three studies together with the findings from original
article using a mini meta-analysis to assess the overall effect size (Goh, Hall, & Rosenthal, 2016;
Lakens & Etz, 2017). The overall effects for Study 1 Survey 2 of the original study were as
follows: bias asymmetry = -0.98 (95% CI = [-1.25, -0.72], p < .001) (see Figure  10), personal
Bias blind spot and free will beliefs                  22
shortcomings asymmetry = -0.19 (95% CI = [-0.47, 0.08], p = .158) (see Figure 11), bias versus
shortcomings difference = -0.44 (95% CI = [-0.56, -0.32], p < .001) (see Figure 12).
Similarly, overall effects for Study 2 of the original study were as follows: better than
average effect for positive personality dimensions = 1.22 (95% CI = [0.78, 1.66], p < .001) (see
Fig. 13), better than average effect for negative personality dimensions = -1.07 (95% CI = [-1.39,
-0.75], p < .001) (see Fig. 14), and denial to better than average effect = 1.32 (95% CI = [0.72,
1.91], p < .001) (see Fig. 15).
General Discussion
Summary and evaluation of replications
We conducted three replication studies of two studies from Pronin et al. (2002), testing the
bias blind spot effect. We summarized the findings of the three replication studies in Table 12.
Overall, we found that: (1) participants' perceived their susceptibility to biases as lower than that
of others, (2) participants perceived their own personal shortcomings as lower than that of others,
(3) bias asymmetry was larger than personal shortcomings asymmetry, (4) participants rated
themselves as higher on positive personality dimensions and lower on negative personality
dimensions, and (5) denied exhibiting the bias. 
The first aim of the current replication effort is to evaluate—in a confirmatory manner—
the size of an effect observed in the original study. To interpret the replication results we
followed the framework by LeBel, McCarthy, Earp, Elson, and Vanpaemel (2018) that take into
account three distinct statistical aspects of the results: (a) whether a signal was detected in the
replication (i.e., the confidence interval for the replication Effect size (ES) excludes zero), (b)
Bias blind spot and free will beliefs                  23
consistency of the replication ES with the original study’s ES, and (c) precision of the
replication’s ES estimate. The replication ES for asymmetry in bias in three individual studies
ranged between dz = -0.68 [-1.01, -0.35] and dz = -1.29 [-1.39, -1.18]. When pooled across all
studies with a mini-meta analysis, the overall estimate of the ES was: dz = -1.00 [-1.33, -0.67].
The results indicate signal was detected and that the replication ES is consistent with the original
study, i.e., the replication’s confidence interval includes the original ES point estimate of 0.86.
The replication results testing the asymmetry in personal shortcomings across three studies
ranged between dz = -0.17 [-0.47, 0.13] and dz = -0.42 [-0.51, -0.34]. Comparing the meta
analytic estimate (dz = -0.34 [-0.46, -0.23]) with original study suggest that, although, a signal
was detected, the replication ES is inconsistent and opposite in direction with the original ES
point estimate of 0.28. Therefore, a less favorable replication outcome suggesting small sample
size in the original study may have contributed to the observed effect. Finally, the hypothesis
testing the asymmetry between bias and personal shortcomings in current replication studies, ES
ranged between dz = -0.29 [-0.60, 0.01] and dz = -0.52 [-0.61, -0.44]. Meta-analytic estimate of
the ES (dz = -0.43 [-0.56, -0.29]) is inconsistent with the original ES point estimate of -0.61, i.e.,
similar in direction but smaller than the ES of the original study.
We followed a similar approach to summarize the replication of Study 2 of Pronin et al.
(2002). The replication ES’s for better than average effect for positive personality dimensions
were dz = 0.78 [0.43, 1.11] (Study 1b) and dz = 1.27 [1.17, 1.38] (Study 3). Meta-analytic
estimate of the ES (dz = 1.05 [0.57, 1.54]) is inconsistent with the original study’s ES point
estimate of 1.61, i.e., similar in direction but smaller than the ES of the original study. The
replication ES’s for better than average effect for negative personality dimensions were dz = -0.69
[-1.02, -0.36] (Study 1b) and dz = -1.22 [-1.32, -1.11] (Study 3). Meta-analytic estimate of the ES
Bias blind spot and free will beliefs                  24
(dz = -0.98 [-1.49, -0.47]) is consistent with the original study’s ES point estimate of -1.24.
Similarly, the replication ES for denial of bias were dz = 1.43 [0.69, 2.16] (Study 1b) and dz =
1.71 [1.50, 1.91] (Study 3). Meta-analytic estimate of the ES (dz = 1.69 [1.49, 1.88]) is
inconsistent with the original study’s ES point estimate of 0.76, i.e., similar in direction but larger
than the ES of the original study. 
In summary, the replication results show that ESs are similar in direction with the original
study for all the hypothesis tested and indicated signal (i.e., ES excludes zero) except for the
prediction of asymmetry in perceived shortcomings. As noted above effect size estimates in some
cases were inconsistent with the original study. However, we note that the sample size employed
in the original study was small. Overall, the replication results provide reasonable support for the
findings of the original study.
Agency beliefs extension
In Studies 2 and 3 we ran extensions examining the link between free will beliefs and the
bias blind spot effects, and the findings are summarized in Table 13. 
To this end, we pre-registered the theoretical relationship between the strength of belief in
free will on individuals’ tendency toward bias blindness and better than average effect. Overall,
our findings provide support for the hypothesis that belief in free will is linked asymmetry in
perceived personal shortcomings of self and of others. This particular asymmetry is mainly
driven by the negative correlation between BFW and perceived personal shortcomings. The
findings are in line with the recent findings that indicate that people’s view on the free will
question can affect fundamental cognitive processes. Most importantly, belief in free will is
associated with an increased sense of agency (Lynn, Muhle-Karbe, Aarts, & Brass, 2014) and
Bias blind spot and free will beliefs                  25
self-efficacy (Baumeister & Brewer, 2012). In the similar vein, current findings support the view
that more people believe in free-will the less they perceive the personal shortcomings of the self
because of the agentic view that their own behavior is generated by themselves (e.g., desires,
goals), rather than by constraints. Across two studies, results confirm the hypothesis.
The exploratory hypothesis that tested for the relationship between free will beliefs and
magnitude of blind spot related to biases did not indicate conclusive support. However, we did
not find any effects to the opposite direction, but rather effects indistinguishable from zero. The
findings suggest that free will may not have a meaningful influence on the invidious distinctions
people make between their own and others’ susceptibility to bias. Previous work by Genschow et
al. (2017) finds that free will beliefs are positively correlated with correspondence bias. The
current finding suggests free will beliefs not have the same nature of the relationship with
individuals’ susceptibility towards other kinds of biases.
Findings of study 3 are in support of the pre-registered exploratory hypothesis that belief
in free was negatively associated with negative personality dimensions (snobbery, deceptiveness,
and selfishness). Findings are consistent with the theoretical view that belief in free will is
associated with moral responsibility. For example, Vohs and Schooler (2008) found that inducing
disbelief in free will increased participants’ cheating behavior. Similarly, Baumeister,
Masicampo, and DeWall (2009) found that an attenuated belief in free will reduce participants’
pro-social inclinations. Martin et al. (2017) find that free will beliefs positively related to harsher
punishments of unethical behavior. Negative personality dimensions included in the current study
do correspond to the moral responsibility in a person. However, we found no support for the
prediction that free will beliefs are positively correlated with positive personality dimensions.
Results suggest that belief in free will may not be associated with better than average effect in
Bias blind spot and free will beliefs                  26
regards to positive personality dimensions. The lack of support for this hypothesis is consistent
with the theoretical argument that free will underlies laypersons’ sense-making for accountability
and choice more so under negative circumstances (Feldman, Wong, & Baumeister, 2016).
However, the results of the correlation between free will beliefs and the extent of denial of bias is
positive and significant. 
In summary, results from the pre-registered extension hypotheses indicated that direction
of correlation holds in almost all cases: with a couple of exceptions (noted above). When the
exceptions occur to the pre-registered hypothesis, we did not find effects to the opposite
direction, but rather effects indistinguishable from zero. 
Conclusion
We aimed to replicate and extend previous findings of bias blind spot effect that refer to
the tendency to see bias in others while being blind to it in ourselves. For the most part, we
replicated the results reported by Pronin et al. (2002). The study contributes to the recent call for
systematic, large-scale, and preregistered replication and validation studies. Additionally, the
present investigation explored the relationship between free will beliefs and the tendency to
impute bias more to others than to the self is rooted. We extended the literature on bias blind spot
exploring the sources of bias blind spot (e.g., Pronin & Kugler, 2007).