Monday, April 15, 2019

Responses of two species of dolphins to novel video footage: No differences observed for the percentage of time spent watching; males displayed a higher rate of aggressive behaviors than females

Behavioral responses of two species of dolphins to novel video footage: An exploration of sex differences. Kelley A. Winship, Holli C. Eskelinen. Zoo Biology, Vol 37, Issue 6, Nov/Dec 2018, Pages 399-407. https://doi.org/10.1002/zoo.21444

Abstract: This study assessed the interest toward novel video clips as enrichment stimuli in two species of captive dolphins (Tursiops: n = 11; Steno: n = 5). Videos were played at underwater viewing windows while the animals were housed with conspecifics, and responses were subsequently analyzed based on general content of each novel video. Interest levels (i.e., percentage of time watching and behavioral rate) were compared between species and within species across video categories. While the varied video contexts did not produce significant differences among the time spent watching or behaviors observed, species differences and sex differences were noted. Rough‐toothed dolphins displayed significantly more behaviors, particularly interest and bubble behaviors, than bottlenose dolphins, with no differences observed between the species for the percentage of time spent watching. Among bottlenose dolphins, males watched the television longer, and responded behaviorally significantly more, displaying a higher rate of bubble and aggressive behaviors than females. Male rough‐toothed dolphins displayed significantly more aggressive behaviors than females, with no other sex differences noted. Overall, these data suggest that television may serve as a useful enrichment device for certain individuals and species of cetaceans, as well as a cognitive experimental tool, as long as sex, species, and individual differences are taken into consideration when interpreting results.

From 2018: Single men feel competitive and hungry for high-calorie food after exposure to sexualized female models

Single men feel competitive and hungry for high-calorie food after exposure to sexualized female models. Sylvie Borau, Jean-François Bonnefon. Human Behavior and Evolution Society, 30th Annual Meeting, July 2018. http://www.hbes.com/conference/hbes2018/

Abstract: Many fast food companies use sex to target males, and they do so to sell high-energy foods, products that are not directly related to sex. The purpose of this research is to better understand why and how sex can sell high-energy foods to male consumers. We conducted four online experimental studies among heterosexual men in the US. In Study 1 (N=311), exposure to sexualized stimuli (vs. landscapes) increases single men’s hunger (but not partnered men’s hunger). Study 2 (N=330) replicates and extends the findings of Study 1 in an advertising context: exposure to an ad featuring a sexy female model (vs. the same ad without the model) increases single men’s hunger and their intention to eat a high-calorie food item (a burger). In Study 3 (N=218), these results do not replicate for a low-calorie item (an apple). Studies 1 to 3 also investigate the underlying mechanism of this effect: sexual stimuli trigger hunger by eliciting male competitiveness. Study 4 (N=242) confirms this mechanism: by manipulating the operational sex ratio in an advert, we show that male intrasexual competitiveness increases hunger for a high-calorie food item. This research suggests that sexualized advertising triggers men to prepare for competition against other men, by pursuing opportunities for somatic investment, and hence high-calorie foods. This behavioral response can have dramatic consequences in a modern environment, where sexualized female models are just as ubiquitous as fatty and sugary foods.

The Impact of Chinese Trade on U.S. Employment: The Good, The Bad, and The Apocryphal

The Impact of Chinese Trade on U.S. Employment: The Good, The Bad, and The Apocryphal. Nicholas Bloom, Kyle Handley, André Kurmann, and Philip Luck. March 19, 2019. https://d101vc9winf8ln.cloudfront.net/documents/30626/original/BHKL_3-20-19_v2.pdf?1554902707

Abstract: Using Census micro data we find that the impact of Chinese import competition on US manufacturing had a striking regional variation. In high-human capital areas (for example, much ofthe West Coast or New England) most manufacturing job losses came from establishments industry switching to services. The establishment remained open but changed to research, design, management or wholesale. In the low human-capital areas (for example, much of the South and mid-West) manufacturing job-losses came from plant closure without much offsetting gain in service employment. Offshoring appears to drive these manufacturing job losses - the Chinese trade impact arose primarily in large importing firms that were simultaneously expanding service sector employment. Hence, our data suggest Chinese trade redistributed jobs from manufacturing in lower income areas to services in higher income areas. Finally, the impact of Chinese imports appear to have disappeared after 2007 – we find strong employment impacts from 2000 to 2007, but nothing since from2008 to 2015.

Performing crimes in line with masculine norms are rewarded with higher social standing, whereas crimes counter to those norms leads to lower social standing, independent of personal subscription to those norms

Stern, Pär, and Timothy J. Luke. 2019. “The Crimes That Pay: Criminality as a Claim to Masculine Social Capital.” PsyArXiv. April 15. doi:10.31234/osf.io/4bwuq

Abstract: Can men use criminality as a means to assert their masculinity and thereby elevate their social standing? We report five studies that provide insight into that question. The first two studies focused on measuring how performing masculine or non-masculine behavior affected the social standing of the actor (i.e. their amount of social capital). The following two studies assessed the respondents’ estimation of how masculine committing 40 different crimes were perceived to be. In the final study, we built on the encouraging results of the first four and thus used the crime masculinity measures in context of how committing such a crime would affect the imagined criminal's amount of social capital. The respondents were asked to assess how the crime affected the change in social standing in three ways: to them personally, the actor's peers, and to society at large. The results suggest that performing crimes in line with masculine norms are rewarded with higher social standing, whereas crimes counter to those norms leads to lower social standing, independent of personal subscription to those norms. Additionally, subscription to masculine norms moderated the extent to which respondents themselves would reward the criminal behavior, such that those who subscribed to masculine norms more tended to ascribe more social capital to more masculine crimes.




Table 7: Means and standard deviations of the MCI-40 masculinity assessments

Crime
M
SD
1
Fist fighting
6.97
2.12
2
Vigilantism
6.97
2.06
3
Street racing
6.95
1.75
4
Mob enforcer
6.87
2.15
5
Street fighting
6.67
2.44
6
Carrying a knife
6.21
2.04
7
Robbery
6.18
2.36
8
Assault
6.15
2.59
9
Armed robbery
5.97
2.51
10
Being a pimp
5.97
2.41
11
Carrying a gun without a permit
5.95
1.97
12
Car theft
5.92
2.21
13
Gun crime
5.90
2.11
14
Trespassing
5.82
1.80
15
Running an illegal gambling den
5.74
2.34
16
Fraud/Using a fake ID
5.59
2.05
17
Helmet law
5.47
2.36
18
Speeding
5.46
1.79
19
Reckless road rage
5.44
2.28
20
Incitement to riot
5.26
2.19
21
Burglary
5.13
2.47
22
Driving with suspended license
5.13
1.99
23
Public drinking
5.08
1.87
24
Drug dealing
5.00
2.21
25
Graffiti/Vandalism
5.00
2.36
26
Terrorist
4.82
2.43
27
Throwing rocks at cars
4.46
2.53
28
Insider trading
4.41
2.01
29
Drunk driving
4.38
3.16
30
Refusing to cooperate
4.21
2.34
31
Handling stolen goods
4.38
1.90
32
Embezzlement
4.31
2.17
33
Jewel thief
4.10
2.14
34
Shoplifting
4.05
2.14
35
Credit card fraud
4.03
2.01
36
Prank calling 911
3.95
2.31
37
Domestic abuse
3.85
2.67
38
Desertion *
2.95
2.49
39
Inability to pay child support *
2.92
2.07
40
Prostitution *
2.72
1.99

* = Crimes expected to be rated especially low
 
MCI = Masculine Crimes Inventory