The current findings provide support for mild but robust cognitive dysfunction in first-degree relatives of late-onset Alzheimer's disease affected individuals

Cognitive Functioning of Unaffected First-degree Relatives of Individuals With Late-onset Alzheimer's Disease: A Systematic Literature Review and Meta-analysis. Ari Alex Ramos, Noelia Galiano-Castillo & Liana Machado. Neuropsychology Review, Sep 3 2022. https://rd.springer.com/article/10.1007/s11065-022-09555-2

Abstract: First-degree relatives of individuals with late-onset Alzheimer's disease (LOAD) are at increased risk for developing dementia, yet the associations between family history of LOAD and cognitive dysfunction remain unclear. In this quantitative review, we provide the first meta-analysis on the cognitive profile of unaffected first-degree blood relatives of LOAD-affected individuals compared to controls without a family history of LOAD. A systematic literature search was conducted in PsycINFO, PubMed /MEDLINE, and Scopus. We fitted a three-level structural equation modeling meta-analysis to control for non-independent effect sizes. Heterogeneity and risk of publication bias were also investigated. Thirty-four studies enabled us to estimate 218 effect sizes across several cognitive domains. Overall, first-degree relatives (n = 4,086, mean age = 57.40, SD = 4.71) showed significantly inferior cognitive performance (Hedges’ g = -0.16; 95% CI, -0.25 to -0.08; p < .001) compared to controls (n = 2,388, mean age = 58.43, SD = 5.69). Specifically, controls outperformed first-degree relatives in language, visuospatial and verbal long-term memory, executive functions, verbal short-term memory, and verbal IQ. Among the first-degree relatives, APOE ɛ4 carriership was associated with more significant dysfunction in cognition (g = -0.24; 95% CI, -0.38 to -0.11; p < .001) compared to non-carriers (g = -0.14; 95% CI, -0.28 to -0.01; p = .04). Cognitive test type was significantly associated with between-group differences, accounting for 65% (R23 = .6499) of the effect size heterogeneity in the fitted regression model. No evidence of publication bias was found. The current findings provide support for mild but robust cognitive dysfunction in first-degree relatives of LOAD-affected individuals that appears to be moderated by cognitive domain, cognitive test type, and APOE ɛ4.

Discussion

To our knowledge, this is the first meta-analysis to quantify the impact of family history of LOAD on cognition, summarizing 218 effect sizes from 34 empirical studies. The results provide compelling evidence that first-degree relatives show a mild but robust amount of overall cognitive dysfunction compared to controls without LOAD-affected relatives. Cognitive deficits in first-degree relatives were evident in executive functions, language, verbal IQ, verbal and visuospatial LTM, and verbal STM or IM. These outcomes indicate that, compared to controls without a family history of LOAD, first-degree relatives have higher chances of obtaining lower scores on neuropsychological measures across multiple cognitive domains. One plausible explanation for these findings relates to altered biomarkers in probands of LOAD-affected individuals. For instance, previous studies have indicated that unaffected offspring of individuals with LOAD show morphological and metabolic brain changes that resemble the preclinical manifestations of LOAD-related pathology (Dubois et al., 2016), including increased global brain atrophy rates (Debette et al., 2009), reduced medial temporal lobe activation (Donix et al., 2010; Johnson et al., 2006), higher levels of beta-amyloid deposition (Clark et al., 2016; Duarte-Abritta et al., 2018), and decreased gray matter volume (Berti et al., 2011; Honea et al., 2010). On the other hand, the lack of significant group differences in premorbid intelligence and visuospatial STM or IM, and especially the near null effects in performance IQ and visual perception, suggest that having a family history of LOAD does not seem to be associated with significant decline in these domains. Alternatively, first-degree relatives may exhibit distinct patterns of cognitive dysfunction related to phenotypic differences in LOAD (Carrasquillo et al., 2014; Ferreira et al., 2020; Snowden et al., 2007; Vogel et al., 2021). For example, recent research indicated that the limbic-predominant phenotype is strongly associated with the amnestic presentation of the disease (e.g., LTM dysfunction), whereas the posterior phenotype is characterized by visuospatial or perceptual abnormalities (Vogel et al., 2021).

Notably, subgroup analyses revealed that the APOE ɛ4 genotype moderates performance differences between first-degree relatives and controls without a family history of LOAD, which makes sense given that the APOE ɛ4 genotype is the most replicated risk factor for LOAD in genetics studies (Cacabelos, 2003; Yang et al., 2021). Specifically, relative groups documented as ɛ4 carriers exhibited more significant dysfunction in cognition (g = -0.24) compared to relative groups documented as non-ɛ4 carriers (g = -0.14). This finding is consistent with preliminary research (Debette et al., 2009; Tsai et al., 2021) demonstrating that first-degree relatives with both risk factors (APOE ɛ4 genotype and a family history of LOAD) are more likely to present with deficits in cognition (e.g., executive dysfunction and verbal and visuospatial LTM difficulties). Evidence also suggests that first-degree relatives with both risk factors exhibit greater beta-amyloid deposition (Yi et al., 2018), higher brain atrophy rates (Debette et al., 2009), and reduced gray matter volume (Ten Kate et al., 2016) compared to those with only one risk factor. Nevertheless, the current systematic synthesis revealed that few studies on the topic document separate scores for ɛ4 carriers verses non-carriers. Hence, the lack of control for APOE ɛ4 status might help account for the contradictory findings from empirical studies on cognition of first-degree relatives of LOAD-affected individuals previously noted in the introduction, and if factored in to analyses of cognitive domains, could potentially paint a different picture with regard to the domains that did not reach statistical significance. Moving forward from the current outcomes, a major challenge for future research on the topic is to determine the combined effects and parse out the unique contributions of APOE ɛ4 carriership and a family history of LOAD in profiling cognitive dysfunction in first-degree relatives. Importantly, the APOE ε4 effect on cognition reported here is based on a specific sample (first-degree relatives of LOAD-affected individuals) and hence our results do not apply to the general population of APOE ε4 carriers.

Although relative group mean age was not a significant moderator and the null hypothesis on the equality of effect sizes in the subgroup analysis on age category was not rejected, the dysfunction effect size for samples intermixing middle-aged (40–65 years) and older (> 65 years) first-degree relatives (g = -0.23, 95% CI [-0.37, -0.09], p = 0.002) was statistically significant and nearly twice the size of the dysfunction effect for samples including only middle-aged individuals (g = -0.12, 95% CI [-0.26, 0.02], p = 0.081). This suggests that the inclusion of a large percentage of middle-aged individuals in the studies analyzed here may have led to an overall smaller dysfunction effect size (g = -0.16, 95% CI [-0.25, -0.08], p < 0.001) than might be expected in older cohorts, thus calling into question the generalizability of the current findings. This conjecture seems in line with findings from a previous study noted in the introduction (Zeng et al., 2013), in which, compared to controls, family members of LOAD-affected individuals showed substantial differences on neuropsychological measures only quite late in life (70 or more years).

The effects of a family history of LOAD on cognition remain poorly understood. Cognitive dysfunction in first-degree relatives of AD-affected individuals has gained attention only in the last two decades. Figure 2 shows that out of 34 empirical works, only three studies (Green & Levey, 1999; La Rue et al., 1995, 1996) were published before the current century, and all of the studies were published within the past 30 years. As previously noted, LOAD-related neuropathological changes precede the clinical diagnosis of LOAD by many years, hence, an increasing number of studies has attempted to longitudinally follow cognitive changes and brain abnormalities in earlier first-degree relatives. In this meta-analytic review, some included studies were drawn from ongoing prospective studies, thus, follow-up research on these cohorts as they grow older is expected. This will allow investigation of cognitive dysfunction in older cohorts of first-degree relatives with a family history of LOAD.

Implications

Findings from the current quantitative review may have important clinical and theoretical implications. LOAD is an age-dependent dementing disease with cognitive symptoms that appear after a lengthy period of evolving neuropathophysiological abnormalities, and thus the effect sizes for between-group differences in several cognitive domains reported here may assist in establishing sensitive cognitive markers for first-degree relatives. This assertion builds on previous empirical research indicating that impairments in cognitive abilities such as premorbid intelligence, memory, and language are deemed potential markers for future development of LOAD (Blacker et al., 2007; Chen et al., 2000; Rapp & Reischies, 2005; Yeo et al., 2011). Equally important, executive dysfunction can be detected in middle-aged offspring many years before the affected parent develops dementia (Debette et al., 2009; Eyigoz et al., 2020). Hence, developing cognitive-based interventions for first-degree relatives, especially APOE ɛ4 carriers, is a pressing need. In relation to this, recent randomized controlled trials have shown that cognitive training benefits individuals at the early stages of LOAD (Cavallo et al., 2016; Kang et al., 2019; Lee et al., 2013). To our knowledge, however, no study has addressed the potential benefit of such a therapeutic strategy in buffering against cognitive decline in unaffected first-degree relatives of LOAD-affected individuals.

Emerging work has found that imagining mildly harming an individual (stealing, pushing) increased the participants' perceived likelihood of harming

How Imagination and Memory Shape the Moral Mind. Brendan Bo O’Connor, Zoë Fowler. Personality and Social Psychology Review, September 3, 2022. https://doi.org/10.1177/10888683221114215

Abstract: Interdisciplinary research has proposed a multifaceted view of human cognition and morality, establishing that inputs from multiple cognitive and affective processes guide moral decisions. However, extant work on moral cognition has largely overlooked the contributions of episodic representation. The ability to remember or imagine a specific moment in time plays a broadly influential role in cognition and behavior. Yet, existing research has only begun exploring the influence of episodic representation on moral cognition. Here, we evaluate the theoretical connections between episodic representation and moral cognition, review emerging empirical work revealing how episodic representation affects moral decision-making, and conclude by highlighting gaps in the literature and open questions. We argue that a comprehensive model of moral cognition will require including the episodic memory system, further delineating its direct influence on moral thought, and better understanding its interactions with other mental processes to fundamentally shape our sense of right and wrong.

Keywords: episodic simulation, imagination, memory, moral cognition

U-shape around middle age: Happiness initially increases after the age of 50, but commonly stagnates afterwards and eventually reverts at high age; this pattern does not emerge for all countries, and is not always observed for women

Does Happiness Increase in Old Age? Longitudinal Evidence from 20 European Countries. Christoph K. Becker & Stefan T. Trautmann. Journal of Happiness Studies, Sep 2 2022. https://rd.springer.com/article/10.1007/s10902-022-00569-4

Abstract: Several studies indicate that happiness follows a U-shape over the life cycle: Happiness decreases after the teenage years until reaching its nadir in middle age. A similar number of studies views the U-shape critically, stating that it is the result of the wrong controls or the wrong model. In this paper, we study the upward-pointing branch of the U-shape, tracing the happiness of European citizens 50 and older over multiple waves. Consistent with a U-shape around middle age, we find that happiness initially increases after the age of 50, but commonly stagnates afterwards and eventually reverts at high age. This pattern is generally observed irrespective of the utilized happiness measure, control variables, estimation methods, and the consideration of selection effects due to mortality. However, the strength of this pattern depends on the utilized happiness measure, control variables, and on mortality effects. The general pattern does not emerge for all countries, and is not always observed for women.

Discussion

Studies measuring happiness and well-being over the life cycle have found mixed results, and in particular the U-shape of happiness is a controversial finding. Consistent with a U-shape around middle age, we find that happiness increases after the age of 50, irrespective of the specification used. Furthermore, our results indicate that happiness tends to stagnate or even decrease at very high age. When conducting our analysis on country- or gender-specific subsamples, a more varied picture emerges. Where we find significant results in these subsamples, however, it is always consistent with a U-shape. These findings are also robust when accounting for differences due to mortality selection effects. While selection effects are indeed at work, with happier respondents being more likely to be alive at the time the next wave is elicited, CASP-12 is the only measure where the pattern is affected: selection makes the observed pattern more pronounced in this case. The result could potentially stem from the CASP-12 measuring control and agency, which decrease towards the end of one’s life (Oliver et al., 2021; Ribeiro et al., 2020; Rodríguez-Blázquez et al., 2020). This might also help to explain why we find lower turning points for CASP-12 and EURO-D in Table 4 in contrast to life satisfaction, when including additional controls. One reason why life satisfaction might continue to increase in high age is that older people might give up on aspiration and enjoy life more (Blanchflower & Oswald, 2004; Frey & Stutzer, 2010). CASP-12 and EURO-D, on the other hand, measure elements related to control and mental health, which might be more negatively affected by age. Different happiness measures might capture different aspects of life, highlighting the importance of looking at multiple measures at the same time.

Importantly, the observed age-happiness relation is consistently obtained using different approaches that have been used in both research that found and did not find the happiness dip in middle age. Additionally, the happiness-age relationship does not only hold for measures of subjective well-being (life satisfaction), but also for affective/eudemonic (CASP-12) and mental health measures (EURO-D). We are thus confident that our findings are meaningful for a substantial number of European countries.

Naturally, we can make no predictions about the trajectory of the happiness-age relation under the age of 50, as the SHARE data set only provides data for older Europeans. However, as other studies have indicated, there is support for the overall U-shape in various European countries (Blanchflower, 2021). We find that happiness indeed increases after middle age, compared to other studies finding a decrease after middle age (Easterlin, 2006; Mroczek & Spiro, 2005) or an overall decrease (Frijters & Beatton, 2012; Kassenboehmer & Haisken-DeNew, 2012). These differences could reflect regional differences, as Easterlin (2006) and Mroczek and Spiro (2005) use US data. Alternatively, methodological differences might drive these divergences. Kassenboehmer and Haisken-DeNew (2012) utilize respondents leaving the survey panel temporarily, to differentiate between age and years in the survey. Both should still be correlated, however. Frijters and Beatton’s (2012) main result is based on fixed effects regressions, which might ultimately not be reliable enough to deal with the age-period-cohort problem (Heckman & Robb Jr, 1985; Yang & Land, 2008). Mrozcek and Spiro’s (2005) use of a demeaned variable in their specification might similarly be problematic (McIntosh & Schlenker, 2006).

Our results are in line with previous studies indicating an increase of happiness after 50 (Morgan & O’Connor, 2017) or an upward profile for affective measures (Mroczek & Kolarz, 1998). However, similar to other studies, our results also provide evidence that happiness, depending on the measure used, stagnates or even decreases later in life (Blanchflower, 2021; Blanchflower & Graham, 2020; Gwozdz & Sousa-Poza, 2010). Our results support the view that people go through a period of relatively low happiness (relative to happiness at older age) around the midpoint of their life. For policy makers, it is important to further explore why this dip occurs and how it can be alleviated.

Going forward, it is important to highlight that proving or disproving the U-shape of happiness, or as in our case components of it, should not be a goal in itself. While knowing the average path happiness takes over the course of a human life is important, even more so is understanding which life events affect the emerging trajectory (Bjørnskov et al., 2008; Galambos et al., 2020, 2021; Lachman, 2015; Morgan & O’Connor, 2020). Past research has shown the happiness effects of marriage (Grover & Helliwell, 2019), parenthood (Nelson et al., 2013), social networks in general (Becker et al., 2019), income (Easterlin, 1974), social support (Siedlecki et al., 2014), permanent employment (Piper, 2021), the quality of formal institutions (Bjørnskov et al., 2010), giving up on aspirations (Schwandt, 2016), and health (Bussière et al., 2021; Gwozdz & Sousa-Poza, 2010; Oliver et al., 2021). Mapping the evolution of these events over the life course may help to better understand the emergence of the U-shape of happiness.

China after half a century: Individuals whose grandparents belonged to the pre-revolution elite earn 16 pct more income and have completed more than 11 pct additional years of schooling than those from non-elite households

Persistence Despite Revolutions. Alberto F. Alesina, Marlon Seror, David Y. Yang, Yang You & Weihong Zeng. NBER Working Paper 27053. Mar 2021. DOI 10.3386/w27053

Abstract: Can efforts to eradicate inequality in wealth and education eliminate intergenerational persistence of socioeconomic status? The Chinese Communist Revolution and Cultural Revolution aimed to do exactly that. Using newly digitized archival records and contemporary census and household survey data, we show that the revolutions were effective in homogenizing the population economically in the short run. However, the pattern of inequality that characterized the pre-revolution generation re-emerges today. Almost half a century after the revolutions, individuals whose grandparents belonged to the pre-revolution elite earn 16 percent more income and have completed more than 11 percent additional years of schooling than those from non-elite households. We find evidence that human capital (such as knowledge, skills, and values) has been transmitted within the families, and the social capital embodied in kinship networks has survived the revolutions. These channels allow the pre-revolution elite to rebound after the revolutions, and their socioeconomic status persists despite one of the most aggressive attempts to eliminate differences in the population.

The Economist The grandchildren of China’s pre-revolutionary elite are unusually rich:

Selection through violence targeting the pre-revolution elite

One may speculate that the pattern of persistence among the pre-revolution elite is driven by selective violence against the elite during the Communist and Cultural Revolutions. If killing and violence were more intense in historically less unequal places and more successful among individuals with fewer resources and a lower capacity to resist, or among those unable to ensure that their descendants perform well, then such a selection could generate a pattern of persistence and upwardly bias the estimates on intergenerational persistence.

We examine the relationship between pre-revolution local inequality (such as the landlord share of the population or land ownership Gini coefficients) and the intensity of violence (both cases of killings and cases of persecutions) reported in the corresponding counties.26 We find that violence was not associated with regional inequality prior to the revolutions: this is the case for the violence both during the Communist Revolution (see Appendix Table A.11), and during the Cultural Revolution (see Appendix Table A.12). More importantly, the systematic killing of landlords and rich peasants was limited in scale as most of the pre-revolution elite survived the revolutions. The observed overall level of violence, albeit not zero, was too low to drive the persistence pattern that we document.

6 Conclusion

This paper investigates the extent to which efforts to eradicate inequality in wealth and education can shut off intergenerational persistence of socioeconomic status. We find that the Communist and Cultural Revolutions in China — among the most radical social transformations in recent human history — prevented the elite from transmitting to their children physical capital and human capital acquired from formal schooling. Nonetheless, the grandchildren of the pre-revolution elite, growing up after the revolution ended, systematically bounce back and earn substantially higher income than their peers. We show that two channels — the transmission of human capital through families, and the survival of social capital manifested in kinship-based networks — contribute to the pre-revolution elite’s persistence despite the revolutions. These channels, both centered around families, have been extraordinarily resilient despite such broad and deep institutional and political changes as the Chinese revolutions brought about. Thus, these channels may be largely and generally immune to policy interventions that aim to level the playing field, making them powerful sources of persistence across generations. One may only speculate that had the Chinese revolutions involved mass killing of the elites themselves, lasted for more than one generation, or directly targeted transmission within the family sphere, the younger generation would be prevented from co-residing or exchanging with those who grew up prior to the revolutions. As a result, human capital transmission within families as well as family-based social capital among the elite may become severely undermined. Since policies targeting intergenerational mobility as extreme as the Chinese revolutions — let alone those more extreme — are exceptionally rare, intergenerational persistence would likely endure.

The % of blacks who believe “racial discrimination is the main reason why many blacks can’t get ahead today” more than doubled from 30% in 2012 to 68% in 2021 while the % who believe “blacks who can’t get ahead are responsible for their own condition” dropped from 54% to 25%

Black Americans Have a Clear Vision for Reducing Racism but Little Hope It Will Happen. Many say key U.S. institutions should be rebuilt to ensure fair treatment. Pew Research Center, Aug 30, 2022. https://www.pewresearch.org/race-ethnicity/2022/08/30/black-americans-have-a-clear-vision-for-reducing-racism-but-little-hope-it-will-happen/

According to the new survey, Black Democrats (73%) are far more likely than Black Republicans (44%) to say racial discrimination is the main reason Black people in the U.S. can’t get ahead. Notably, Black Republicans (45%) are more likely than most other demographic subgroups to say Black people who can’t get ahead in the U.S. are mostly responsible for their own condition. Just 21% of Black Democrats hold the same view.

Roughly three-quarters (76%) of Black liberals say racial discrimination is the main reason Black people can’t get ahead, compared with 69% of Black moderates and 56% of Black conservatives. While nearly four-in-ten Black conservatives (39%) say Black people who can’t get ahead are mostly responsible for their own condition, smaller shares of Black moderates (25%) and Black liberals (17%) say the same.

About seven-in-ten Black registered voters (71%) say discrimination is the primary obstacle for Black people in the U.S., while roughly two-in-ten (23%) say Black people who can’t get ahead are mostly responsible for their own condition. Black adults who are not registered to vote are similarly divided on this measure, with roughly six-in-ten (62%) saying discrimination is the main  reason Black people can’t get ahead and about three-in-ten (29%) saying those who can’t get ahead are responsible for their own condition.

Black women are more likely (72%) than Black men (63%) to cite racial discrimination as the primary obstacle to getting ahead. Meanwhile, Black men (29%) are more likely than Black women (22%) to say Black people who can’t get ahead are mostly responsible for their own condition.

When it comes to education, about six-in-ten (62%) Black adults with a high school education or less say racial discrimination is the main reason many Black people can’t get ahead. By comparison, 71% of those with some college but no bachelor’s degree and 74% of those with at least a bachelor’s degree say the same. Likewise, Black adults with middle and upper incomes (71% and 74%, respectively) are more likely than Black adults with lower incomes (66%) to point to racial discrimination as the main reason many Black people can’t get ahead these days.

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From The Missing Data Depot: https://twitter.com/data_depot/status/1564984221654953984: The % of blacks who believe “racial discrimination is the main reason why many blacks can’t get ahead today” more than doubled from 30% in 2012 to 68% in 2021 while the % who believe “blacks who can’t get ahead are responsible for their own condition” dropped from 54% to 25%.

Adolescents in Nine European Countries: The Role of Individual Factors and Social Characteristics in Feelings after Exposure to Sexually Explicit Materials — Exposure may not be as distressing to youth as prevalent risk-focused narratives have suggested

Exposure to Sexually Explicit Materials and Feelings after Exposure among Adolescents in Nine European Countries: The Role of Individual Factors and Social Characteristics. Michaela Lebedíková, Vojtěch Mýlek, Kaveri Subrahmanyam & David Šmahel. Archives of Sexual Behavior, Aug 29 2022. https://rd.springer.com/article/10.1007/s10508-022-02401-9

Abstract: Research on adolescents’ sexual exposure has mostly focused on negcative outcomes using a risk-based lens, and there is little work on the factors that may predict exposure, as well as youths’ emotional responses to sexual content. Using a cross-national sample, the present study examined the associations of individual (sensation seeking and emotional problems) and social characteristics (the quality of family environment, including active and restrictive parental mediation) with adolescents’ exposure to sexually explicit materials and their feelings after exposure. The survey included 8,820 11- to 16-year-olds (Mage = 13.36 years, SD = 1.62, 48.0% male) from nine European countries (Czech Republic, Finland, Malta, Poland, Portugal, Romania, Serbia, Spain, Switzerland). The results revealed that although there were differences in the prevalence of youths’ sexual exposure by country, there were also similarities in the characteristics underlying exposure and subsequent feelings across different country contexts. No significant relationship was found between active parental mediation and exposure in most countries, and the findings regarding restrictive parental mediation were mixed. Although the majority of the participants reported neutral feelings, there were gender differences in feeling happy and upset after exposure. Overall, the results suggest that exposure may not be as distressing to youth as prevalent risk-focused narratives have suggested.

Economics of Ideas, Science and Innovation Syllabus (PhD course) — Readings

Economics of Ideas, Science and Innovation Syllabus (PhD course). Aug 2022. https://progress.institute/economics-of-ideas/syllabus/

Readings:

Class 1 Course Overview and Macroeconomic Foundations

Arrow, Kenneth. 1962. “Economic Welfare and the Allocation of Resources for Invention.” In The Rate and Direction of Inventive Activity: Economic and Social Factors, pp. 609-625. Princeton, NJ: Princeton University Press.

Jones, Charles I. 2001. Chapter 4 and 5, pp. 78-86 and 96-122 in Introduction to Economic Growth. New York: W. W. Norton & Company.

Jones, Benjamin F. and Lawrence H. Summers. 2021. “A Calculation of the Social Returns to Innovation.” In Innovation and Public Policy, University of Chicago Press.

Bloom, Nicholas, Mark Schankerman, and John Van Reenen. 2013. “Identifying Technology Spillovers and Product Market Rivalry.” Econometrica 81(4): 1347-1393.

Jones, Benjamin F. 2009. “The Burden of Knowledge and the ‛Death of the Renaissance Man’: Is Innovation Getting Harder?” Review of Economic Studies 76(1): 283-317.

Class 2 Open Science as an Economic Institution

Aghion, Philippe, Mathias Dewatripont, and Jeremy C. Stein. 2008. “Academic Freedom, Private Sector Focus, and the Process of Innovation.” RAND Journal of Economics 39(3): 617-635.

Ahmadpoor, Mohammad, and Benjamin F. Jones. 2017. “The Dual Frontier: Patented Inventions and Prior Scientific Advance.” Science 357(6531): 583-587.

Azoulay, Pierre, Christian Fons-Rosen, and Joshua S. Graff Zivin. 2019. “Does Science Advance One Funeral at a Time?” American Economic Review 109(8): 2889-2920.

Dasgupta, Partha, and Paul David. 1994. “Towards a New Economics of Science.” Research Policy 23(5): 487-521.

Myers, Kyle. 2020. “The Elasticity of Science.” American Economic Journal: Applied Economics 12(4): 103-134.

Class 3 Innovation Policies, including the US Patent System

Bloom, Nicholas, John Van Reenen and Heidi Williams. 2019. “A Toolkit of Policies to promote Innovation” Journal of Economic Perspectives 33(3) 163–184

Budish, Eric, Benjamin Roin, and Heidi Williams. 2015. “Do firms underinvest in long-term research? Evidence from cancer clinical trials,” American Economic Review 105(7): 2044-2085.

Galasso, Alberto and Mark Schankerman. 2015. “Patents and Cumulative Innovation: Causal Evidence from the Courts,” Quarterly Journal of Economics 130(1): 317–69.

Gallini, Nancy and Suzanne Scotchmer. 2001. “Intellectual Property: When is it the Best Incentive System?” Innovation Policy and the Economy Volume 2, Adam Jaffe, Josh Lerner and Scott Stern, (editors), Cambridge Massachusetts: MIT Press.

Lisa L. Ouellette. 2012. “Do Patents Disclose Useful Information?” Harvard Journal of Law & Technology 25(2): 531-593.

Class 4 Contracting and Control Rights for Innovation

Aghion, Philippe, and Jean Tirole. 1994. “The Management of Innovation.” Quarterly Journal of Economics 109(4): 1185-1209.

Azoulay, Pierre, Joshua Graff Zivin, and Gustavo Manso. 2011. “Incentives and Creativity: Evidence from the Academic Life Sciences.” RAND Journal of Economics 42(3): 527-554.

Lerner, Joshua, and Ulrike Malmendier. 2010. “Contractibility and the Design of Research Agreements.” American Economic Review 100(1): 214-246.

Manso, Gustavo. 2011. “Motivating Innovation.” Journal of Finance 66(5): 1823-1860

Class 5 Labor Markets and the Supply of Innovators

Bell, Alexander M., Raj Chetty, Xavier Jaravel, Neviana Petkova, and John Van Reenen. 2019. “Who Becomes an Inventor in America? The Importance of Exposure to Innovation.” Quarterly Journal of Economics 134(2): 647-713.

Biasi, Barbara, David J. Deming, and Petra Moser. 2021. “Education and Innovation.” NBER Working Paper #28544.

Doran, K., Gelber, A. and Isen, A., 2022. The effects of high-skilled immigration policy on firms: Evidence from visa lotteries. Journal of Political Economy.

Marx, Matt, Deborah Strumsky, and Lee Fleming. 2009. “Mobility, Skills, and the Michigan Non-Compete Experiment.” Management Science 55 (6): 875–889.

Waldinger, Fabian, 2016. “Bombs, Brains, and Science: The Role of Human and Physical Capital for the Production of Scientific Knowledge,” The Review of Economics and Statistics, vol. 98, no. 5, pp. 811-831, 2016.

Decades of research suggest a correlation between belief in a dangerous world and political conservatism, but new research thinks this is wrong

Belief in a Dangerous World Does Not Explain Substantial Variance in Political Attitudes, But Other World Beliefs Do. Jeremy D. W. Clifton, Nicholas Kerry. Social Psychological and Personality Science, September 1, 2022. https://doi.org/10.1177/19485506221119324

Abstract: Decades of research suggest a correlation between belief in a dangerous world and political conservatism. However, research relied on a scale that may overemphasize certain types of dangers. Furthermore, few other world beliefs have been investigated, such that fundamental worldview differences between liberals and conservatives remain largely unknown. A preregistered study of nine samples (N = 5,461; mostly US Americans) found a negligible association between a newly improved measure of generalized dangerous world belief and conservatism, and that the original scale emphasized certain dangers more salient to conservatives (e.g., societal decline) over others most salient for liberals (e.g., injustice). Across many measures of political attitudes, other world beliefs—such as beliefs that the world is Hierarchical, Intentional, Just, and Worth Exploring—each explained several times more variance than dangerous world belief. This suggests the relevance of dangerous world belief to political attitudes has been overstated, and examining other world beliefs may yield insights.

Keywords: political psychology, conservatism, primal world beliefs, belief in a dangerous world, political attitudes

No social media for six hours? Facebook/Meta outage stressed users but they felt better once they realized the others users were also locked out of the network

No social media for six hours? The emotional experience of Meta's global outage according to FoMO, JoMO and internet intensity. Tal Eitan, Tali Gazit. Computers in Human Behavior, September 1 2022, 107474. https://doi.org/10.1016/j.chb.2022.107474

Highlights

• This study tested stress caused by the social networks' October 4 2021 outage.

• We used both quantitative and qualitative methods to explore the emotional experience.

• Content analysis revealed 4 types of reactions, including joy of missing out (JoMO).

• FoMO, Internet intensity, age, and marital status were found as predictors of stress.

• A significant interaction was found between gender and employment regarding stress.

Abstract: On October 4, 2021, a severe technical service failure of Meta (previously Facebook) caused a worldwide “outage” for six hours. Billions of people, not able to access their social media accounts, experienced different levels of stress. This study took advantage of these unique circumstances to test the stress caused by sudden lack of online access using three main factors: the fear of missing out (FoMO) effect, social media intensity, and demographic factors. In the two days immediately following this event, we conducted an online survey, with 571 adults responding. Using both quantitative and qualitative analyses, data were collected to explore the emotional experiences and predictors of the stress adults underwent during the social media outage. The content analysis revealed four types of reactions: (1) feeling anxious at first, but then feeling better after realizing the outage was global; (2) having only negative feelings; (3) having only positive feelings and even experiencing a version of the joy of missing out (JoMO); and (4) feeling indifferent. A hierarchical regression indicated that stress can be significantly predicted by FoMO, social media intensity, emotional experience, age, and marital status. In addition, FoMO and intensity were found to be mediators between age and stress. Finally, we found associations between stress and gender and employment, with self-employed women experiencing less stress than men and not self-employed women experiencing more stress than men. The findings are discussed in light of the FoMO vs. JoMO effects, the social comparison theory, and the role of demographic factors in reducing or increasing stress when social media is not available.

Keywords: Social media outageStressInternet intensityFoMO