Sunday, April 7, 2019

The Downsides of Minimum Wage Increases

The Downsides of Minimum Wage Increases. Jonathan Meer. The Library of Economics and Liberty, Apr 6 2019.

Excerpts (full text and links in the post above):

The House of Representatives recently took up the Raise the Wage Act, which would more than double the federal minimum wage to $15 an hour over the next five years. [...]

When debate focuses on the total number of jobs lost or gained, it hides this potentially nasty distribution of the benefits: a recent college graduate with a barista job may get a few more dollars an hour, but the high school dropout finds it harder to get and keep a job. Those who have the least to offer employers, might need more training, or are the biggest risks to hire will face the toughest challenges.

Some opponents of higher minimum wages skip over the fact that some people absolutely would benefit. Those who get and keep these higher-paying jobs might be winners, seeing an increase in their take-home pay.

But those gains have offsetting losses that could either reduce that gain or even reverse it.

My research ( shows that when the minimum wage is raised, employers offset increased labor costs by reducing benefits like the generosity of health insurance. Other benefits, like free parking or flexibility in scheduling, are more difficult to measure but are also likely to be cut back. Employers will likely expect more work effort when they are forced to pay more, changing the nature of jobs. And in the longer run, economists have found that employers shift towards automation [...] and expecting customers to do more things themselves[...]– reducing job growth in ways that aren’t always obvious. This damage takes time to be seen, which is one reason minimum wage hikes, like rent control, often seem appealing.

And then there are those who would definitely lose because they can’t find work, and they aren’t likely to be picked at random. The minimum wage is a blunt instrument. It doesn’t distinguish between types of workers and the households they come from. The teenage children of well-off families, earning money to buy video games, are treated the same as single moms struggling to get by. When wages are set at an artificially high rate, why should an employer take a risk on the single mother who needs the occasional shift off to take her kids to the doctor? The kid from a disadvantaged background who needs some direction on how to treat customers appropriately? Or the recently released felon trying to work his way back into the community? Why should employers bother with them when there are plenty of lower-risk people who are willing to work at those artificially high wages?

Recent evidence ( from Seattle’s minimum wage increases show that it’s precisely the most inexperienced workers who struggle the most in the face of high minimum wages. And another line of my own research ( finds that minimum wage increases cause employers shift towards workers with more credentials.

It will get much worse in the next recession. The economic expansion over the last decade has allowed employers to absorb much of the cost of increased labor market regulations, but a downturn will quickly force them to cut jobs that are marginally productive only in good times. Those at the margins of the workforce will be left further behind. Low-wage jobs aren’t easy, don’t pay well, and are rarely fun. But not being able to find work at all is far worse.

Despite the lowest unemployment rates in decades, only 39% of adults without a high school degree had a full-time job in 2018 – and among young African-Americans dropouts, it’s a shocking 26%. It’s hard to believe that the best way to help them find work and start climbing the job ladder is to put the first rung out of reach [...].

A national minimum wage of $15 per hour ignores the wide variation in labor markets and the cost of living. Even in high-wage New York and California, half of hourly workers outside of metropolitan areas earn less than $15 an hour. Nineteen states, including Texas, have median hourly wages below $15; outside of large cities in Texas, 60 percent of hourly workers earn less than $15. A uniform minimum wage at such an unprecedented level, far beyond historical norms, would cause real damage to inexperienced and low-skill workers – especially in areas in desperate need of more opportunities.

Advocates are absolutely correct that we have to do something to help low-income families. Other policies, like the Earned Income Tax Credit, are better targeted and much more effective. [...]


Jonathan Meer is a professor of economics at Texas A&M University. His research focuses on charitable giving, the economics of low-wage labor markets, and the economics of education.

The Industry Anatomy of the Transatlantic Productivity Growth Slowdown

The Industry Anatomy of the Transatlantic Productivity Growth Slowdown. Robert J. Gordon, Hassan Sayed. NBER Working Paper No. 25703. March 2019.

By merging KLEMS data sets and aggregating over the ten largest Western European nations (EU-10), we are able to compare and contrast productivity growth up through 2015 starting from 1950 in the U.S. and from 1972 in the EU-10. Data are provided at the aggregate level, as well as for 16 industry groups within the total economy and 11 manufacturing sub-industries. The analysis focuses on outcomes over four time intervals: 1950-72, 1972-95, 1995-2005, and 2005-15. We interpret the EU-10 performance as catching up to the U.S. in stages, with its rapid growth of 1950-72 representing a delayed adoption of the inventions that propelled U.S. productivity growth in the first half of the 20th century, and the next EU-10 stage for 1972-95 as imitating the U.S. outcome for 1950-72. We show that both the pace of aggregate productivity growth during 1972-95 for the EU-10 as well as its industrial composition matched very closely the growth record of the U.S. in the previous 1950-72 time interval.

A striking finding is that for the total economy the “early-to-late” productivity growth slowdown from 1972-95 to 2005-15 in the EU-10 (-1.68 percentage points) was almost identical to the U.S. slowdown from 1950-72 to 2005-15 (-1.67 percentage points). There is a very high EU-U.S. correlation in the magnitude of the early-to-late slowdown across industries. This supports our overall theme that the productivity growth slowdown from the early postwar years to the most recent decade was due to a retardation in technical change that affected the same industries by roughly the same magnitudes on both sides of the Atlantic.

Empirical evidence that prospectively negative events exhibit a significant amount of shifted (i.e., positive) emotions in the corresponding social network messages, serving as antidote in negative events

KuĊĦen E., Strembeck M., Conti M. (2019) Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube. In: Kaya M., Alhajj R. (eds) Influence and Behavior Analysis in Social Networks and Social Media. ASONAM 2018. Lecture Notes in Social Networks. Springer, Cham. December 12 2018,

Abstract: In this paper, we present a study on 5.6 million messages that have been sent via Twitter, Facebook, and YouTube. The messages in our data set are related to 24 systematically chosen real-world events. For each of the 5.6 million messages, we first extracted emotion scores based on the eight basic emotions according to Plutchik’s wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the messaging behavior of social media users. In particular, we found empirical evidence that prospectively negative real-world events exhibit a significant amount of shifted (i.e., positive) emotions in the corresponding messages. To explain this finding, we use the theory of social connection and emotional contagion. To the best of our knowledge, this is the first study that provides empirical evidence for the undoing hypothesis in online social networks (OSNs). The undoing hypothesis assumes that positive emotions serve as an antidote during negative events.

Rolf Degen summarizing... They super-rich are different: They found a way to transmute wit into gold.

Wealth Generation as a Form of Expertise: An Examination from 2002-2016 of Elite Education, Cognitive Ability, and the Gender Gap Among Billionaires. Jonathan Wai & Tomoe Kanaya. Journal of Expertise 2019, Vol 2(1).

Abstract: The study of expertise has focused on areas such as chess, music, and sports. Here, we argue that wealth generation can also be considered a form of expertise. This study examines 14,246 global Forbes billionaires across 15 years (2002-2016) to examine historical trends of elite education and cognitive ability, looking at the world (and U.S. specifically) as a function of industry, country, sex, self-made status, and net worth. The results reveal that the elite education and cognitive ability level of billionaires has remained relatively stable over time, suggesting the billionaire filtering structure has remained relatively unchanged. Yet, at least within the U.S., the percentage of elite educated and cognitively talented billionaires entering the technology and especially the finance and investment sectors has increased over time. These results suggest that one factor to consider in increasing inequality in the U.S. may be the role of human talent in selecting areas of occupational expertise that have amplified their ability to generate wealth in more recent years. This paper broadens the definition of expertise to include wealth generation—the idea that the development of wealth expertise may have skills that transcend field—and suggests deliberate practice cannot be the full explanation of success for this area of expertise. A multidisciplinary perspective can help test the strength and generality of expertise theories, more comprehensive models ofe xpertise should account for abilities and education, and the investigation of expertise models should account for historical changes.

Keywords: wealth expertise, elite education, wealth inequality, historical examination, talent


Expertise development has traditionally been studied in domains such as sports, music, and games like chess (e.g., Ericsson, 2014; Ericsson, Krampe, & Tesch-Romer, 1993). However, there has been a recent push for more comprehensive theoretical models of expertise (e.g., for a review, see Hambrick, Macnamara, Campitelli, Ullen, & Mosing, 2016) and a broader multidisciplinary approach to studying expertise (Gobet, 2016). Indeed, expertise research has started to move into domains such business, law, politics, and even journalism (e.g., Volden, Wiseman, & Wai, 2016; Wai, 2013; Wai & Perina, 2018; Wai & Rindermann, 2017), though there has been less research to date focused on wealth accumulation (e.g., Wai & Lincoln, 2016) and the idea that this might also be a form of expertise.
A cottage industry exists around wealth creation (e.g., multiple organizations track the characteristics and habits of the wealthy), in large part because the idea that making money is a skill or form of expertise is attractive. For example, money managers often will demonstrate their “talent” at growing wealth to attract future clients by pointing to their rate of returns in the past. But throughout history, there has been much contention over whether wealth generation is really a form of expertise where talent has a role to play, and how much of becoming rich is simply due to “luck” or things like nepotism (Galbraith, 1994; Pluchino, Biondo, & Rapisarda, 2018). Many models of expertise suggest that general cognitive ability likely plays a role in expertise development (for a review, see Subotnik, Olszewski-Kubilius, & Worrell, 2011). Though certainly not the only important predictor, this perspective would align with the idea that cognitive talent differences may, at least in part, be important in explaining wealth inequality. Additionally, Ericsson’s deliberate practice model focuses on the idea that practice largely can account for individual differences in domain performance (Ericsson, 2014; Ericsson et al., 1993). In contrast to this, Macnamara, Hambrick, and Oswald (2014) showed that deliberate practice accounted for less than 1% of the performance variance in occupations. The review by Hambrick et al. (2016) strongly suggests that deliberate practice cannot be the full explanation of individual differences in performance across expertise domains studied. Another way to approach the estimation of the role of talent and practice in success is to examine the role of general cognitive ability first, and then consider that as an important source of variance to account for (e.g., Lubinski, 2004) prior to assessing the impact of other factors, such as practice or even luck, which are also important.
Broadening definitions and domains of expertise research to incorporate wealth generation and maintenance, therefore, may be important to test the generality of theoretical models of expertise and expert performance. Though wealth generation is on a continuum, an extreme level of this type of expertise, at least at present, is attaining billionaire status. Due to the many pathways of attaining billionaire status, it may seem that wealth generation is too broad to be considered as a form of expertise. There are, after all, so many different paths to attain billionaire status, with the paths themselves through particular industries that are arguably quantitatively and qualitatively different. The study of expertise has traditionally focused on carefully examining one particular domain as the venue for research (e.g., chess, running).
However, anotherway of studying expertise is to start with an outcome such as extreme wealth and then consider the domains through which such extreme wealth was generated such as different industries (e.g., real estate, technology). In fact, the study of comprehensive theoretical models of expertise (e.g., Hambrick et al., 2016) examines expertise development across multiple domains purposefully to shed light on what elements do or do not generalize across domains. Thus, looking at a large sample of billionaires across time and across industry or domain might shed light in a new way on different ways wealth expertise manifests itself.
Another way of thinking about wealth generation as a form of expertise is to consider that the skills and interests in wealth generation likely can and do transcend a specific domain or field. For example, a strong interest and desire to make money would lead an astute entrepreneur or investor to choose an industry based on wealth generation potential as a primary factor. The industry chosen would be selected not on interest in developing expertise within that industry, but rather because it is a good match to the individual’s particular skill sets, interests, connections, and know-how at the time. This would maximize the leveraging of that domain to increase the likelihood of wealth generation. For example, generating an idea, recognizing and seizing an opportunity, a willingness to take extreme risks, convincing others to invest in the idea, and sticking with the idea through lows and highs (or failing and generating a new idea) until eventual fruition are all general skills that likely transcend field in developing wealth expertise. Thus, the educational selectivity and cognitive ability required for a certain pathway to wealth expertise may be important to consider. That is, understanding the role that certain elite schools may play in different domains of wealth expertise can shed light on the role of educational filtering mechanisms and corresponding cognitive ability that different domains of wealth expertise may require.
The study of occupational leaders or elites (e.g., Hacker, 1961; Khan, 2012) has attracted much public discussion and academic interest across multiple disciplines, especially in the U.S. due to a focus on income inequalityand what factors might explain why a tiny fraction of the population holds an enormous fraction of wealth (Piketty & Saez, 2003; Solow, 2014; Stiglitz, 2011). The path to becoming a billionaire is often linked to many personal and contextual factors such as family wealth and connections, attending highly selective schools and accessing networks, cognitive ability, and luck (Wai, 2013, 2014; Wai & Lincoln, 2016). Prior research examined the education and ability levels of Fortune 500 CEOs across the last two decades (Wai & Rindermann, 2015), and uncovered that such levels remained relatively stable across time. This suggests that the occupational filtering or selection structure for Fortune 500 CEOs has been unchanged for at least the last two decades. It remains to be explored whether this holds in other domains. Murray (2003) investigated human accomplishment across the full span of history, from 800 B. C. to 1950, so studying samples after 1950 is important for contemporary understanding, but at the same time the historical trends in the last 50 years are likely but a blip in comparison to accomplishment going back in time.
Scholars that span disciplines have commented on the role that technology may have played in amplifying the impact of highly talented individuals. For example, the economist Krueger (2012, p. 5) noted things have “favored people with the analytical skills to get the most out of technology.” The economist Mankiw (2013, p. 23) stated “changes in technology have allowed a small number of highly educated and exceptionally talented individuals to command superstar incomes in ways that were not possible a generation ago.” Indeed, prior research uncovered that, in more recent years, the billionaires around the world who accumulated their wealth from the technology and finance and investment sectors tended to have very high levels of elite education and corresponding cognitive ability (Wai, 2013, 2014). This also appeared true for 30-millionaires (Wai & Lincoln, 2016). Psychologists Aguinis and O’Boyle (2013) argued that changes in work have handsomely rewarded a handful of star performers who contribute the vast majority of value in innovation.
Given these comments, it is surprising that it has not been as widely considered that one partial explanation for increases in wealth and other forms of inequality, especially within the U.S., could be that academically gifted or intellectually talented and exceptionally productive individuals may be choosing to pursue opportunities with increasing frequency that lead to the accumulation of wealth. In essence, they may be choosing to develop expertise in attaining wealth. Industries that rely on technology or the ability to use money to make money may have become very rewarding for people with exceptional analytical skills. These ongoing discussions highlight that it is unclear whether elite education and ability selectivity for billionaires or for individual sectors have changed or remained the same over time and should be investigated.
An historical analysis of the education and cognitive abilitylevel of billionaires within these sectors could informthe idea that highly educated and cognitively advancedpeople may be increasingly developing expertise in technology, finance, and investments.Therefore, in this paper, we examine the role of general cognitive ability among billionaires across a number of years through the proxy of elite education. Further, in order to assess the idea that highly educated and cognitively advanced people may be increasingly developing expertise in technology, finance, and investments, we conduct an historical analysis of the education and cognitive level of billionaires within these sectors. More broadly, to examine the extent to which expertise in wealth generation is driven by expertise in specific domains, we examine billionaires across the many different industries or sectors in which they made their money. Broadly,we test the generality of expertise models by moving into a relatively unexplored domain of expertise, that of wealth generation.

Present Study
The present study draws from the Forbesglobal billionaires database from across a recent span of 15 years (2002-2016) to examine historical trends of elite education and cognitive ability of billionaires looking at the world overall and U.S. as a function of industry, country, sex, self-made status, and net worth. Additionally, we examined whether elite educated and talented people have been entering the technology and finance and investment sectors at higher rates over time. To examine the role that domain pathways matter in expertise development in wealth generation we examine elite education and ability as a function of industry or domain in which the billionaires obtained their wealth. Wealth expertise is on a wide continuum, and examining those who have made it to the top is important in its own right, just as is examining extraordinary performers in other types of domains. There are many paths to develop this type of expertise. What are those paths? What role does education and ability play?



Conceptualizing and Thinking about Wealth Generation as a Form of Expertise
This investigation leveraged billionaire status as a way to empirically conceptualize wealth generation as a form of expertise and to examine how specific occupations required elite education and cognitive abilities to achieve wealth expertise. There was great variation in the industries in which the billionaires earned their wealth, with general intelligence and elite education having much less of a premium in real estate, food and beverage, and fashion and retail and much more of a premium in technology and finance and investments. Overall, elite education and cognitive ability still mattered, just to different degrees for each subdomain. In particular, within the U.S., billionaires making their money in the technology and finance and investment sectors tended to be much more elite educated and cognitively able, perhaps choosing to develop expertise in a technology and/or finance andinvestment sector as a stepping stone towards the broader development of wealth expertise. This aligns with an analysis by industry within a group of 30-millionaires (Wai & Lincoln, 2016).The development of wealth expertise may have underlying skills that in fact also transcend any particular field. First, seeking to use one’s cognitive ability towards certain occupations with potential for wealth expertise requires a value placed on generating and accumulating wealth (this would be in sharp contrast to academics who are also often cognitively advanced, but value academic and intellectual freedom over wealth). Generating an idea that can result in wealth generation likely requires utilizing one’s individual profile of abilities, personality, and other traits but also leveraging one’s cultural resources, context, and connections in historical place and time. Some examples include, but are not limited to, the ability to recognize an opportunityacross many areas in which one might develop highly specific occupational expertise, the ability to persuade or pitch one’s ideas to funders and potential donors, and the ability to tolerate extreme risks. Additionally, based on the data across industriespresented in this paper, having a high cognitive ability, attending an elite school, and leveraging those resources towards turning an idea into a successful money generating enterprise are also important components. Industriousness or being willing to work long hours or put in 10,000 or more hours of practice, even in the face of a certain number of failures, is also likely important in developing wealth expertise as it is for other forms of expertise, as is luck. Broadly, high cognitive ability, attending a highly selective institution, and choosing to pursue wealth through the technology or finance and investments sectors appears to be more important in recent years for the development of wealth expertise.

Findings Across Time: 2002-2016
Prior work demonstrated a significant link between education/intellectual capacity and net worth, even within this highly select sample with restricted variability (Wai, 2013, 2014b), however, that was only for more recent years. A full analysis across a recent span of 15years suggests that broadly, the link between education/cognitive ability and net worth is not strong. This matches with findings on 30-millioniares, which indicated that after controlling for many confounders, the link between education/cognitive abilityand net worth became quite small (Wai & Lincoln, 2016). However, this does not mean that education/cognitive ability is not important for attaining great wealth because people in the top 1% of ability are likelyoverrepresented among billionaires. For example, given top 1% cognitive ability people should be represented at the base rate of 1%, this means globally top 1% people are overrepresented among billionaires by a factor of about 32 to 38, and within the U.S. top 1% people are overrepresented by a factor of about 41 to 47 (see Figure 1, total trends across time). This means that within this highly select sample, other factors may play a larger role in differentiating the person with only 1 billion from multiple billions. Future research might focus on investigating these differentiating factors that contribute to or take away from the development of wealth expertise even within this highly select sample across time.

The Gender Gap
Across the period 2002-2016,sex differences did drop in the initial 3 to 5 years but have been relatively stable in the last decade at about 9 to 1. Overall, sex differences are larger in the world than in the U.S. Broadly this suggests that for whatever reason, more men tend to endup as billionaires and that increasing the number of women billionaires may take some time. Given that among 30-millionaires the male-female ratio was 9.27 to 1 (Wai & Lincoln, 2016), and because newer billionaires may often come from multi-millionaires who increase their wealth, sex differences may shift slowly. These findings should be taken into account when considering ways to increase the numbers of women among billionaires and in the boardroom, among other sectors.

Are Elite Educated and Talented People Increasingly Choosing to Pursue Occupational Expertise That Leads to Wealth?
For the world, most of the elite educated and cognitively advanced people have tended towards the technology sector, and secondarily the finance and investments sector. For the U.S., a similar percentage of elite educated and cognitively advanced people have tended towards both the finance and investments and technology sectors and this percentage has grown over time. Since 2012, the elite education of the finance and investments sector has slightly increased, whereas the technology sector has appeared to level off. The overall pattern in the U.S. may indicate that elite educated people are increasingly choosing to pursue occupational expertise that leads to wealth, namely finance and investments and technology, in recent years. This trend further suggests that some of the increase in income or wealth inequality within the U.S. may be that cognitively advanced people are entering these highly lucrative occupations. Billionaire Mark Cuban, for example has declared that “the world’s first trillionaires are going to come from somebody who masters AI [artificial intelligence] and all its derivatives and applies it in ways we never thought of” (Clifford, 2017). It also means that if you want to develop expertise as a billionaire, and especially if you want to enter the technology, investments, or finance sectors, an elite education and corresponding cognitive ability needed for admission may be important in your path (see Rivera, 2015,for sociological mechanisms through which elite students get elite jobs in these industries). Tracking the role of education and ability in each of these sectors, especially technology, finance, and investments, will be of interest to uncoverthe role that education and ability plays in making enormous sums of money in the future.

Limitations and Future Directions
Consistent with previous research (e.g., Wai, 2014; Wai & Perina, 2018), attendance at American higher education institutions with average SAT (math + verbal) scores (or the ACT equivalent) of 1400 or higher according to U.S. News & World Report (America’s Best Colleges, 2013) as well attendance at a top college or university worldwide according to QS World University Rankings (2012) were used as an approximation for ability level. Because individual test scores were not publicly available, attending these institutions were reasonable approximations for individuals within the top 1% of ability (e.g., Frey & Detterman, 2004; Koenig et al. 2008; Wai, 2014). At an international level, admission to the very top schools was considered representative of at least a good portion of the top cognitive potential within each country. While this method cannot separate education from cognitive ability, it may give an underestimate in some cases as extremely cognitively advanced people may not have attended a highly ranked school for multiple reasons (e.g., financial limitations). It may give an overestimation in some cases due to individuals who gained entry with significantly sub-average test scores (e.g., legacy admissions; Espenshade & Radford, 2009; Golden, 2006; Sander, 2004). Specifically for billionaires, the fluctuations of the percentages of elite education and ability, through the proxy of typical test scores, may be influenced more from the wealth of parents and other corresponding advantages granted such as access to elite institutions and other networks, rather than individual ability. It
is reasonable, however, that both of these Type I and Type II errors counterbalance one another, while they may lower the reliability of the method.One limitation of the billionaires sample over 2002-2016 is that although there was some change, many of the people at the top of the list have remained on this list across time. Therefore, the shift in composition in education over time is largely driven by the nature of who loses and who gains wealth in a way that moves them across this billionaire cut point. As the samples have increased in recent years globally, it washarder to find background educational information on new people in different countries due to a lack of systematic public profiles (hence the high NR/NC percentage for the most recent years). This appeared to be less of a problem within the U.S. Future research might be directed at determining the qualities not only of those who join, but those who leave, the billionaire ranks over time. And because a degree from an elite institution likely opens doors and provides opportunities that would not otherwise be available, future research might investigate the extent to which high ability students who attend state institutions or less selective institutions may fare in attaining billionaire or wealth expertise status.
Another important limitation of using billionaire status as an indicator of wealth expertise is that there may not necessarily be a set of unifying factors underlying this extreme wealth indicator. We fully recognize this as a possibility, but believe it is worthy to explore the role of abilities and educational selectivity across multiple paths within the billionaire sample to examine whether there are commonalities. Additionally, there are likely elements in wealth generation that do transcend field, such as generating an idea, recognizing an opportunity, or convincing others to invest in that idea, among others.

Across a recent span of the last 15 years, the elite education and cognitive ability of billionaires has remained relatively stable, suggesting the billionaire filtering structure has remained relatively unchanged. Additionally, at least within the U.S., the percentage of elite educated and cognitively talented billionaires entering the technology and especially the finance and investment sectors has increased over time. This suggests that one factor to consider in increasing inequality in the U.S. may be the role of human capital or talent in selecting areas of occupational expertise that have amplified an individual’s ability to generate wealth.These findings add to the expertise literature by broadening the definition of expertise to include wealth generation and historically exploring the role of elite education and cognitive ability in this area of expertise, in part through the industries or pathways through which such expertise is developed. Even across time (from 2002-2016), elite education and cognitive ability appear to be an important factor in developing wealth expertise, suggesting that deliberate practice cannot be the full explanation of performance and that expertise in wealth generation is likely influenced by many interlocking factors, especially elite education and ability, in addition to factors such as luck. Of course, the importance of elite education and ability within billionaires varies, and billionaires as a whole also differ in the importance of elite education and ability in relation to other areas of expertise. For example, House members tend to have a lower level, whereas the most powerful men and women tend to have a higher level of elite education and ability. This suggests a multidisciplinary perspective is important to test thestrength and generality of expertise theories, that more comprehensive models of expertise should account for abilities and education, and that investigation of expertise models should account for historical changes.

Decision making under risk for others vs. the self: Decisions for others are a little more risky than decisions for the self; but the little is very little

Decision Making for Others Involving Risk: A Review and Meta-Analysis. Evan Polman, Kaiyang Wu. Journal of Economic Psychology, Apr 6 2019.

•    We meta-analyzed research on decision making under risk for others vs. the self.
•    We found decisions for others are a little more risky than decisions for the self.
•    We identify moderators, test their validity, and quantify their effects.
•    We suggest researchers focus on when (not whether) decisions for others are riskier.
•    We highlight what is unique about decision making for others vs. the self.

Abstract: Are choices for others riskier than choices people make for themselves? This question has been asked by economists, psychologists, and other researchers in the social sciences – which has generated a diversity of research accounts and results. For example, a number of studies have found strong instances of a risky shift in choices for others, while other studies have found no such effect or have found that choices for others instead generate a cautious shift. In a meta-analysis of 128 effects from 71 published and unpublished papers (totaling 14,443 observations), we found a significant though small effect size (d = 0.105) in favor of a risky shift when people choose for others. Moreover, we found considerable variance between studies (Q = 1106.25), suggesting that the net effect is susceptible to moderating factors or study characteristics, which we identify and discuss as well (viz. choice recipient, decision frame, decision reciprocity, theoretical perspective, study design). Thus, we document not only whether decisions for others are riskier, but when (and when such decisions are less risky). We further discuss what is distinctly unique about decision making for others – how such choices are not just different in degree from personal choices but different in kind.