In the lab: Those with an East German background cheat significantly more on an abstract die-rolling task than those with a West German background, but only when exposed to West Germany's system

The impact of two different economic systems on dishonesty. Dan Ariely et al. European Journal of Political Economy, March 13 2019. https://doi.org/10.1016/j.ejpoleco.2019.02.010

Abstract: Using an artefactual field experiment, this paper tests the long-term implications of living in a specific economic system on individual dishonesty. By comparing cheating behaviour across individuals from the former socialist East with those of the capitalist West of Germany, we examine behavioural differences within a single country. We find long-term implications of living in a specific economic system for individual dishonesty when social interactions are possible: participants with an East German background cheat significantly more on an abstract die-rolling task than those with a West German background, but only when exposed to the enduring system of former West Germany. Moreover, our results indicate that the longer individuals had experienced socialist East Germany, the more likely they were to cheat on the behavioural task.

Keywords: Social behaviourCheatingDishonestyArtefactual field experiment

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1. Introduction
Individual dishonesty is very costly for society as a whole. For example, the average annual tax gap in the US for the years 2008-2010 is estimated to be $458 billion (Internal Revenue Service, 2016). The costs of insurance fraud (excluding health insurance) is estimated to be more than $40 billion per year, which translates to costs in the form of increased premiums between $400 and $700 per year for an average US family (U.S. Department of Justice, 2018). Understanding determinants of dishonest behaviour is thus a major concern for society. In this paper, we investigate a context effect of dishonesty by asking the question: What are the long-term implications of living in a specific economic system for individual dishonesty? We explore whether existing economic systems, socialism and capitalism, have a different effect on people’s dishonesty. To understand how exposure to real economic systems influences individual behaviour, we make use of an historical event, the division of Germany into two different formerly existing economic regimes within a single country, socialist East and capitalist West Germany. Specifically, we compare cheating behaviour between East Germans, who were exposed to socialism for over 40 years, and West Germans, who were at the same time living in a market economy. Several studies have documented differences in individual behaviour between citizens of former East and West Germany, for example in national solidarity (Ockenfels and Weimann, 1999; Brosig-Koch et al., 2011) or preferences for redistribution and levels of social trust (Alesina and Fuchs-Schündeln, 2017; Heineck and Süssmuth, 2013). Heineck and Süssmuth (2013) even find that specific cultural traits appear to be passed down through generations. We add to this strand of literature by investigating the persistence of behavioural differences in dishonesty. Moreover, we compare the impact of two really existing economic systems within one single country to better understand the effect of extant socialistic versus capitalistic regimes on individual dishonesty. 
 It is important to note that social interactions—between friends as well as strangers—seem to influence dishonest behaviour. People cheat more when they observe others behaving dishonestly (Gino et al., 2009). In this vein, Diekmann et al. (2016) and Rauhut (2013) find support for social conformity to the observed behaviour in the form of contagiousness and spread of norm violations. Mann et al. (2014) also show a transmission through social networks and documents that people’s tendency to lie is associated with the lying behaviour of their friends and family members. However, social interaction can also lead to anti-conformity in behaviour. An experimental study by Fortin et al. (2007) finds evidence of a social anti-conformity effect, which suggests that individuals prefer to deviate from the tax compliance behaviour of their reference group, using an income reporting task. In the specific case of former East and West Germany, fairness considerations may spur social interaction effects, since fervent debate erupted after the reunification of Germany about whether economic and social injustice befell parts of the country because of the reunion (Schmitt and Maes, 1998). Individuals who believe they were treated unfairly in an interaction with another person are more likely to cheat in a subsequent unrelated game (Houser et al., 2012). Moreover, when individuals are aware that they were poorly compensated relative to another group, they cheat more to increase their earnings (John et al., 2014). In this paper, we explore whether social interactions might explain individual differences in dishonesty between citizens from socialist versus capitalist systems. We use an artefactual field experiment (Harrison and List, 2004) to investigate the impact of different economic systems within a single country on individual cheating behaviour. We compare cheating among people exposed to the two existing economic systems of former Germany. In particular, we compare Germans living in Berlin, where citizens with East and West German backgrounds co-exist, with Germans living outside of Berlin, where citizens typically live with peers sharing only the same historical background. To measure cheating behaviour, we use a die task adapted from previous research where participants were paid based on the number of dots on reported die rolls (Fischbacher and Föllmi-Heusi, 2013; Jiang, 2013; Mann et al. 2016; see Garbarino et al. 2016 and Abeler et al. 2018 for two excellent meta studies). It has been shown that even abstract cheating tasks predict behaviour in the field. A widely used reporting task in the lab significantly predicts classroom misbehavior in middle and high school students (Cohn and Maréchal, 2018) and it has been shown that abstract as well as contextualized cheating tasks in the lab correlate with rule violations in real life (Dai et al., 2017). Our results show that social interaction is an important mechanism underlying individual cheating: participants with an East German background cheat significantly more on an abstract die-rolling task than those with a West German background, but only when exposed to the enduring capitalist system of West Germany. Moreover, our results indicate that the longer individuals living in Berlin had experienced socialist East Germany, the more likely they were to cheat on the behavioural task. In contrast, we did not observe differences in cheating behaviour between East and West German individuals living in the respective cities of Leipzig (East Germany) and Dortmund (West Germany). Unlike in Berlin, individuals from Leipzig and Dortmund have less opportunity for comparison against the alternative economic system, due to being situated at some distance from the former inner German border. The remainder of the paper is structured as follows. Section 2 outlines related literature on differences between former East and West Germany. Thereafter, section 3 presents our materials and methods. Section 4 lays out the empirical results of our study. Section 5 concludes. 



 2. Differences between former East and West Germany
From 1961 to 1989, the Berlin Wall divided one nation into two distinct economic and political regimes: socialism (East Germany) and capitalism (West Germany). Socialist systems in the past have been characterized by extensive scarcity, which in the case of East Germany, ultimately led to the collapse of the German Democratic Republic (GDR). In many instances, socialism pressured or forced people to work around official laws. For example, in East Germany stealing a load of building materials in order to trade it for a television set might have been the only way for a person to be able to acquire such a valuable good and connect to the outside world (Hornuf and Rieger, 2017). Moreover, a high degree of infiltration by intelligence apparatuses is also considered as a key characteristic of socialist systems. In East Germany, the secret service (Staatssicherheit) kept records on more than one third of its citizens (Koehler, 1999). Unlike in democratic societies, freedom of speech was not a virtue upheld in socialist regimes and it was therefore often necessary for citizens to misrepresent their thoughts to avoid repression. Earlier studies have shown differing degrees of national solidarity between East and West Germans. In a laboratory experiment with economics students, East Germans showed significantly less solidarity five years after the German reunification (Ockenfels and Weimann, 1999). Asked how much money Germans would be willing to hand over to anonymous future losers if they won 10 Deutsche Mark in a solidarity game, East Germans were willing to give up roughly half as much as West Germans. Interestingly, East Germans also expected to receive much less from potential winners. These results were recently confirmed by another study showing that there was no convergence in solidarity 20 years after the German reunification, which the authors attribute to slow changes in social behaviour due to the necessity of coordination on social norms in the society as well as complementarities involved in individual social behaviour (Brosig-Koch et al., 2011). Based on data from the German Socioeconomic Panel (GSOEP), Alesina and Fuchs-Schündeln (2007) provide evidence that East Germans have stronger preferences for public policies that involve redistribution. They find that economic and political regimes greatly shape individual preferences for state interventions and that these preferences tend to change slowly. According to the authors’ analysis, one fourth of the effect that East Germans’ have stronger preferences for state intervention is because East Germans became poorer during the socialist epoch, while the remainder can be attributed to the impact of socialism on individual preferences itself. Yet, one limitation of the study is that people might distort their true preferences when responding to a survey like GSOEP. For example, people might overstate their willingness to contribute to redistributive policies because they do not actually have to pay for them. Using a discrete choice experiment, another study shows that the stated preferencesof East Germans towards redistribution indeed differ from their actual preferences (Pfarr et al., 2013). While East Germans indicate that they prefer higher degrees of redistribution, they are not actually willing to pay for such policies. Based on the GSOEP data Heineck and Süssmuth (2013) investigate the effect of the economic regime on individuals’ trust and risk preferences as well as their cooperativeness. Relative to West Germans, East Germans showed persistently lower levels of social trust and were less inclined to see others as fair. This study also suggested that East Germans are more risk-loving. Most importantly, the authors find that these cultural traits appear to be passed down through generations. While this research provides valuable insights on differences in solidarity and individual social preferences, little is known about how the economic systems of former East and West Germany influenced individual dishonesty. Torgler (2003) indicates that at one point in time, East Germans were more likely than West Germans to say that cheating on their taxes cannot be justified, but that this difference disappeared seven years after the German reunification. However, this finding is based on self-stated preferences in a survey and therefore might not reflect individuals’ actual behaviour when put in a position where dishonesty financially pays off.

What are the cognitive and emotional effects of CAPTCHA tests? They are associated with feelings of alienation and the user’s self-perception of humanity is influenced

You need to show that you are not a robot. Leopoldina Fortunati et al. New Media & Society, March 14, 2019. https://doi.org/10.1177/1461444819831971

Abstract: Given that today 60% of Internet traffic is generated by bots, ‘CAPTCHA’ (Completely Automated Public Turing Test to tell Computers and Humans Apart) tests that are supposedly impossible to be done by robots have been introduced. What are the cognitive and emotional effects of these tests on Internet users? Does this request to demonstrate they are not a robot affect users’ identity as human beings? To answer these questions, we selected two groups (117 and 116 respondents, respectively). An online questionnaire that differed only in the task was proposed: we asked the first group to complete some CAPTCHA tests, and the second group to complete some logic tests. In addition to other questions in both versions, we introduced the TLX scale (NASA). Preliminary results show that CAPTCHA execution is associated with feelings of alienation and that the user’s self-perception of humanity is influenced by the execution of the two different types of test.

Keywords Bots, CAPTCHA, human identity, TLX scale, Turing test

Effects of boardroom gender diversity on CEO compensation & dismissal decisions: Largely disappear when we account for geographic distance (more remote from HQ & more reliant on hard info)

Alam, Zinat S. and Chen, Mark A. and Ciccotello, Conrad S. and Ryan, Harley E., Gender and Geography in the Boardroom: What Really Matters for Board Decisions? (December 18, 2018). SSRN, https://ssrn.com/abstract=3336445

Abstract: Recent literature has shown that gender diversity in the boardroom seems to influence key monitoring decisions of boards. In this paper, we examine whether the observed relation between gender diversity and board decisions is due to a confounding factor, namely, directors’ geographic distance from headquarters. Using data on residential addresses for over 4,000 directors of S&P 1500 firms, we document that female directors cluster in large metropolitan areas and tend to live much farther away from headquarters compared to their male counterparts. We also reexamine prior findings in the literature on how boardroom gender diversity affects key board decisions. We use data on direct airline flights between U.S. locations to carry out an instrumental variables approach that exploits plausibly exogenous variation in both gender diversity and geographic distance. The results show that the effects of boardroom gender diversity on CEO compensation and CEO dismissal decisions found in the prior literature largely disappear when we account for geographic distance. Overall, our results support the view that gender-diverse boards are “tougher monitors” not because of gender differences per se, but rather because they are more geographically remote from headquarters and hence more reliant on hard information such as stock prices. The findings thus suggest that board gender policies, such as quotas, could have unintended consequences for some firms.

Keywords: Board of Directors, Gender, Geography

For women, sex economy has some poor, some middle class, & some millionaires; for men, there is a small number of super-billionaires & huge masses with almost nothing

Attraction Inequality and the Dating Economy. Bradford Tuckfield    . Quillette, March 12, 2019, https://quillette.com/2019/03/12/attraction-inequality-and-the-dating-economy/


[...] The economist Robin Hanson has written some fascinating articles that use the cold and inhuman logic economists are famous for to compare inequality of income to inequality of access to sex. If we follow a few steps of his reasoning, we can imagine the world of dating as something like an economy, in which people possess different amounts of attractiveness (the dating economy’s version of dollars) and those with more attractiveness can access more and better romantic experiences (the dating economy’s version of consumer goods). If we think of dating in this way, we can use the analytical tools of economics to reason about romance in the same way we reason about economies.

One of the useful tools that economists use to study inequality is the Gini coefficient. This is simply a number between zero and one that is meant to represent the degree of income inequality in any given nation or group. An egalitarian group in which each individual has the same income would have a Gini coefficient of zero, while an unequal group in which one individual had all the income and the rest had none would have a Gini coefficient close to one. [...]

Some enterprising data nerds have taken on the challenge of estimating Gini coefficients for the dating “economy.” Among heterosexuals, this actually means calculating two Gini coefficients: one for men, and one for women. This is because heterosexual men and heterosexual women essentially occupy two distinct “economies” or “worlds,” with men competing only with each other for women and women competing only with each other for men. The Gini coefficient for men collectively is determined by women’s collective preferences, and vice versa. If women all find every man equally attractive, the male dating economy will have a Gini coefficient of zero. If men all find the same one woman attractive and consider all other women unattractive, the female dating economy will have a Gini coefficient close to one. The two coefficients do not directly influence each other at all, and each sex collectively sets the Gini coefficient—that is, the level of inequality—for the other sex.

A data scientist representing the popular dating app “Hinge” reported on the Gini coefficients he had found in his company’s abundant data, treating “likes” as the equivalent of income. He reported that heterosexual females faced a Gini coefficient of 0.324, while heterosexual males faced a much higher Gini coefficient of 0.542. So neither sex has complete equality: in both cases, there are some “wealthy” people with access to more romantic experiences and some “poor” who have access to few or none. But while the situation for women is something like an economy with some poor, some middle class, and some millionaires, the situation for men is closer to a world with a small number of super-billionaires surrounded by huge masses who possess almost nothing. According to the Hinge analyst:

On a list of 149 countries’ Gini indices provided by the CIA World Factbook, this would place the female dating economy as 75th most unequal (average—think Western Europe) and the male dating economy as the 8th most unequal (kleptocracy, apartheid, perpetual civil war—think South Africa).

Quartz reported on this finding, and also cited another article about an experiment with Tinder that claimed that that “the bottom 80% of men (in terms of attractiveness) are competing for the bottom 22% of women and the top 78% of women are competing for the top 20% of men.” These studies examined “likes” and “swipes” on Hinge and Tinder, respectively, which are required if there is to be any contact (via messages) between prospective matches.

Another study, reported in Business Insider, found a pattern in messaging on dating apps that is consistent with these findings. Yet another study, run by OkCupid on their huge datasets, found that women rate 80 percent of men as “worse-looking than medium,” and that this 80 percent “below-average” block received replies to messages only about 30 percent of the time or less. By contrast, men rate women as worse-looking than medium only about 50 percent of the time, and this 50 percent below-average block received message replies closer to 40 percent of the time or higher.

If these findings are to be believed, the great majority of women are only willing to communicate romantically with a small minority of men while most men are willing to communicate romantically with most women. The degree of inequality in “likes” and “matches” credibly measures the degree of inequality in attractiveness, and necessarily implies at least that degree of inequality in romantic experiences. It seems hard to avoid a basic conclusion: that the majority of women find the majority of men unattractive and not worth engaging with romantically, while the reverse is not true. Stated in another way, it seems that men collectively create a “dating economy” for women with relatively low inequality, while women collectively create a “dating economy” for men with very high inequality.

[...]

There are no villains in this story. Nobody can or should be blamed for his or her honest preferences, and if women collectively believe that most men are unattractive, what grounds does anyone, male or female, have to argue with them? We may pity the large majority of men who are regarded as unattractive and who have few or no romantic experiences while a small percentage of attractive men have many. Just as much, consider that we live in a monogamous culture, and so the 20 percent of men who are regarded as attractive can only be in committed relationships with at most 20 percent of women. We may just as well pity the rest of the women, who are destined to be in committed relationships, if they pursue a relationship at all, with someone who they regard as unattractive. The only villain in this story is nature, which has molded our preferences so that this tragic mismatch of attraction and availability occurs.

To those who study nature, the various gender gaps in romantic life will not come as a surprise. Evolutionary biologists have seen these types of patterns many times before and can explain each of them. The relative perceived attractiveness of younger women vs. older can be explained by the higher fertility of younger adult women. The libido gap can be explained by the different mating strategies instinctively pursued by the distinct sexes.

As for the different Gini coefficients consistently reported for men and women, they are not consistent with a monogamous social structure in which most people can pair with someone of comparable perceived attractiveness. However, this is not surprising: monogamy is rare in nature. The revealed preference among most women to attempt to engage romantically only with the same small percentage of men who are perceived as attractive is consistent with the social system called “polygyny,” in which a small percentage of males monopolize the mating opportunities with all females, while many other males have no access to mates. Again, this will not come as a surprise to scientists. The evolutionary biologist David P. Barash wrote an article in Psychology Today titled “People Are Polygynous,” citing extensive biological and historical evidence that throughout most of history, our species has practiced “harem polygyny,” a form of polygamy.

There are many animals of all kinds that practice polygyny in one form or another, including many of our primate relatives like gorillas and lemurs. For animals, social structures are not an object of reflection or systematic attempted reform—they just do what their instincts and upbringing dictate. But  it is the destiny of humans to constantly fight against nature. We light fires for warmth, build air conditioners for cooling, invent soap and plumbing and antibiotics and trains and radios in an effort to conquer the constraints of nature. But when we turn on our smartphones built on ingeniously developed transistors that show we can overcome nature’s entropy, we log on to dating apps and enter a world that is built on shadows of the social structures of our primeval savanna ancestors. Technology has not enabled us to escape the brutal social inequalities dictated by our animal natures.

This is not to say that we haven’t tried. The institution of monogamy is itself a “redistributive” type of policy: like capping the income of billionaires, it caps the total allowed romantic partners of the most attractive, so that unattractive people have much better chances to find a partner. The marriages that we read about in historical accounts that are based on prudence and family arrangement make more sense when we realize that basing marriage on mutual attraction leads so many—both men and women—to be unsatisfied with the outcome, since most women find most men unattractive. All of the world’s great religious traditions have extolled chastity as a great virtue and taught that there are higher goals than sexual satisfaction—these teachings add meaning to the otherwise “poor” lives of the majority of people who are regarded as perpetually unattractive.

Even in centuries-old fairy tales like The Frog Prince and Beauty and the Beast, we see our culture’s attempt to come to terms with the paradigm of a woman regarded as attractive pairing with a man who she regards as unattractive. The differing Gini coefficients faced by men and women guarantee that this will be a common—or even the most common—romantic pairing in a monogamous culture. In these fairy tales (depending on which version you read), the beautiful woman first accepts or even loves the hideous man. The sincere love of a woman transforms the unattractive man into something better: more handsome, richer, and royal. Allegorically, these stories are trying to show men and women a way to relate one-on-one even though most women find most men unattractive; they are trying to show that sincerely offered love, and love based on something other than sexual attraction, can transmute ugliness to beauty and make even a relationship with unmatching attractiveness levels successful.

[...]

The result of these cultural changes is that the highly unequal social structures of the prehistoric savanna homo sapiens are reasserting themselves, and with them the dissatisfactions of the unattractive “sexually underprivileged” majority are coming back. It is ironic that the progressives who cheer on the decline of religion and the weakening of “outdated” institutions like monogamy are actually acting as the ultimate reactionaries, returning us to the oldest and most barbaric, unequal animal social structures that have ever existed. In this case it is the conservatives who are cheering for the progressive ideal of “sexual income redistribution” through a novel invention: monogamy.

[...]

Danish data on the minimum wage: The hourly wage jumps up by 40pct at the discontinuity of minimum wage rules; employment falls by 33pct and total input of hours decreases by 45pct

Do Lower Minimum Wages for Young Workers Raise Their Employment? Evidence From a Danish Discontinuity. Claus Thustrup Kreiner,  Daniel Reck and  Peer Ebbesen Skov. Review of Economics and Statistics, March 04, 2019. https://doi.org/10.1162/rest_a_00825


Abstract : We estimate the impact of youth minimum wages on youth employment by exploiting a large discontinuity in Danish minimum wage rules at age 18, using monthly payroll records for the Danish population. The hourly wage jumps up by 40 percent at the discontinuity. Employment falls by 33 percent and total input of hours decreases by 45 percent, leaving the aggregate wage payment almost unchanged. We show theoretically how the discontinuity may be exploited to evaluate policy changes. The relevant elasticity for evaluating the effect on youth employment of changes in their minimum wage is in the range 0.6-1.1.

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Minimumwages,setbylaworbycollectiveagreement,existin3/4ofOECDcountries(OECD,2015).IntheUnitedStates,minimumwageincreaseshavebeenhighonthepolicyagendainrecentyears,motivatedinpartbymanystudies ndingsmallemploymente ectsofminimumwagehikes.Somecities(e.g.LA,Seattle)andthestateofCaliforniahaverecentlylegislatedaminimumwagerateof$15,amuchhigherratethanthecurrentFederalminimumof$7.25perhour.Ashigherminimumwagesbecomecommon,policy-makersareconfrontedwithasecondquestion:shouldahighminimumwageapplytoeveryone?Inparticular,shoulditapplytoyoungerworkers?Youngworkersarelow-skilledandenterthelabormarketwithoutworkexperience,whichmakethempotentiallyvulnerabletohighminimumwages.ManyUSstatesandcities,includingCalifornia,Minnesota,SouthDakota,KansasCityandDesMoines,whichhaverecentlyincreasedtheirminimumwage,havedebated,andattimeslegislatedorplacedontheballot,anexceptionforyoungerworkers(Kreiner,ReckandSkov,2018).Similarly,manyEuropeancountrieswithhighminimumwageshavelowerminimumwagesforyoungerworkers(OECD,2015).Themainquestionweseektoansweris:Holdingtheadultminimumwage xedatagivenlevel,whatisthee ectofachangeintheminimumwageapplyingtoyoungworkersontheiremployment?ExistingUSevidenceandmostotherevidencecannotanswerthisquestionasitstudieschangesinaglobalminimumwageratherthanayouth-speci cminimumwage.Forexample,theelasticityofyouthemploymentwithrespecttotheminimumwageof0.075reportedbytheUSCongressionalBudgetO ceisbasedonchangesinaglobalminimumwage(CongressionalBudgetO ce,2014).OurempiricalevidenceexploitsalargediscontinuityinDanishminimumwagerulesoccurringwhenworkersreachage18.TheDanishcontextisidealforourpurpose.Denmarkhaslargechangesinminimumwagerateswhenworkersturn18(andnochangeatanyotherages)andahighadultminimumwagecomparabletothe$15levelinplaceinCaliforniaandunderconsiderationmoregenerallyintheUS.1Furthermore,wecanstudythee ectoftheagediscontinuityusinghigh-qualitymonthlydataonwages,employment,andhoursworkedfortheentireDanishworkforce.Ourmain ndingsarecontainedinFigure1,whichshowsthattheagediscontinuityinminimumwageshasalargeimpactonemploymentaroundage18.Weexplainthedetailsbehindtheconstructionofthedataset,measurementissues,andthesourceofidentifyingvariationbelow.Figure1aplotsaveragehourlywages,imputedbydividingreportedmonthlywagesbyreportedhoursworkedforeachindividual,asafunctionofage(measuredinmonths),fortwoyearsbeforeandaftertheir18thbirthday.TheaveragehourlywageratejumpsbyDKK46,orabout$7,correspondingtoa40percentchangeinthewagelevelatage18computedusingthemidpointmethod.Figure1bplotstheshareofindividualswhoareemployedbymonthlyage.Weobservea15percentage-pointdecreaseinemploymentatage18,whichcorrespondstoa33percentdecreaseinthenumberofemployedindividuals.Forcomparison,notethatthewageandemploymentratesdevelopsmoothlywhenindividualsturn17and19yearsold,andthatittakestwoyearsbeforetheemploymentrateisbackatthelevelitattainsjustbeforethejumpdownwardsatage18.Subsequentanalysesrevealthatthedropinemploymentwhenworkersturn18re ectsadiscretechangeinjoblosswithoutanydiscretechangeinhiring(wedoobserveasmallanticipatoryslow-downinhiringasworkersapproachage18).Asimpleestimateoftheemploymentelasticity(theextensivemargin)withrespecttothewagechangeisobtainedbydividingtheestimatesofthepercentagechangesinemploymentandhourlywage.Thisgivesanelasticityaround-0.8.Whenlookingattotalhoursworked(theintensiveandextensivemargin),we ndanelasticityof-1.1,indicatingthatmostoftheresponseoccursalongtheextensivemargin.Recallthataunitelasticitywouldimplythattheaveragewagepaymentofallindividuals,includingbothemployedandnon-employedworkers,shouldstayunchangedwhenthewagerateisraised,becauseitse ectontheaveragewagepaymentisfullyo setbyadecreaseinemployment.Consistentwiththisreasoning,we ndnearlynoe ectonaverageearnings.Thisprovidesalternativeevidenceofatotalhoursworkedelasticityaround-1,notdependingonthemeasurementofhourlywages.Weuseeconomictheorytomotivateourempiricalspeci cationandtoshowthat,un-derreasonableassumptions,theestimatedemploymentelasticitymaybeusedtocalculatethee ectonyouthemploymentofachangeintheminimumwagespeci callyforyoungerworkers.First,weprovideasimplemodelinwhichtheelasticityweestimateusingtheagediscontinuityisexactlythesameastheelasticityneededforthedesiredcounterfactualpolicyanalysis.Inthemodel,workershaveexogenous,heterogeneousproductivitiesandarehirediftheirproductivityexceedstheminimumwage(correspondingtoahorizontaldemandforlabormeasuredine ectiveunits).Inthissimplesetting,cross-workere ectsarezero.Accordingtothisbasicmodel,wemaycomputetheconsequencesofincreasingtheminimumwageforyoungworkers(thoseunder18)uptothehigherlevelapplyingtoadultsbyusingourestimatedelasticity.Thiscalculationgivesa15percentagepointdropinyouthemployment,correspondingto33percentofinitialemployment.Amodelwithdownwardslopinglabordemandforlow-skilledworkwouldinsteadsug-gestthattherearecross-workere ects,implyingthatahigheryouthminimumwagemayincreaselow-skilledadultemployment.Suchcross-workere ectsposeapotentialthreattotheidenti cationstrategy.However,weshowthatonecanobtainalowerboundfortheyouthemploymentelasticitybyconsideringtheextremecaseofa xeddemandforlow-skilledwork(implyingthattheemploymente ectfromthediscontinuityanalysisisentirelydrivenbycross-workere ects).Thelowerboundmaybecomputedfromourestimatedelasticityandthewageshareofyoungerworkersinthelow-skilledlabormarket.Wethuscomputethewageshareoflow-skilledworkersunderage18,usingvariousde nitionsofthelow-skilledworkersthatareperfectlysubstitutableforworkersunderage18.Inthemostex-tremeofthesecalculations,inwhichonlyworkersaged18-19aredeemedtobe low-skilled substitutesforworkersunderage18,thelowerboundoftheyouthemploymentelasticitybecomes0.6.Increasingtheminimumwageforyoungworkersuptothelevelofadultwork-erswouldthendecreaseemploymentbyatleast11percentagepoints,or25percentofyouthemployment,whichisstillasubstantialemploymente ect.Wealsoembedoursimplemodelinanequilibriumsearchframeworkincorporatingdy-namicsforaging.Inaccordancewiththeempiricalevidence,themodelpredictsthatthedropinemploymentatage18re ectsadiscretechangeinjobloss,ratherthanadiscretechangeinhiring.Themodelalsopredictsspillovere ectsofanincreaseintheyouthminimumwageonadultemployment,butinthiscasethesignofthespillovere ectisambiguous.Inanycase,ourelasticityestimateisagainagoodapproximationofthee ectonyouthemploymentifyoungworkersconstitutealowshareoftotallow-skilledemployment."Additionalanalysisdemonstratesthatourinterpretationoftheempiricalresultsiscor-rectandstudiesheterogeneityinemploymente ectsacrossworkers.Mostimportantly,wedemonstratethatotherpoliciesthatchangewhenworkersturn18,suchastheeligibilityforDanishsocialwelfareprograms,arenotdrivingourresults.Wealsoshowthatthesizeoftheemploymentelasticityisonlyslightlylargerforworkersoflowerability,asproxiedbyschoolGPAin9thgradeortheincomeofparents.Finally,weprovidesuggestiveevidencethatjoblosseshavepersistente ectsonworkers.Twoyearsaftertheworkers'18thbirthdays,theemploymentrateisabout15percentagepointslowerforworkersloosingtheirjobatage18relativetoworkerswhokepttheirjob.Ourpapercontributestothesizableliteratureonminimumwagesandemployment,asreviewedinCardandKrueger(2015)andNeumarkandWascher(2008).Mostofthislit-eraturestudiesemploymente ectsofglobalminimumwagehikes,whileourfocusisonthee ectsofage-speci cminimumwages,whereevidenceislimited.NeumarkandWascher(2004)showthatcountrieswithhighminimumwagesalsotendtohavehighyouthunem-ployment,but,consistentwithourresults,thiscorrelationisweakerwhencountrieshavealowerminimumwageforyoungworkers.Onenewstudy,Kabátek(2015),analyzesanagediscontinuity,inthiscaseseveralsmallagediscontinuitiesinDutchminimumwages.Theobservedchangesinwagesandemploymentaroundworkers'birthdaysarethereforemuchsmallerandmoredi usethaninourcontext.Theimpliedemploymentelasticityisslightlysmallerthanours.Combiningonelargediscontinuitywiththoroughtheoreticalrea-soningandrichdataallowsustointerpretoure ectsinmoredetailandtoperformcrediblecounterfactualpolicyexercises.Ourresultsmaymakesomereadersconcernedabouttheimpactofglobalincreasesintheminimumwageonemployment,asubjectofintenseongoingdebate.SeveralDDstudies,mostfamouslyCardandKrueger(1994), ndlittletonoimpactofglobalminimumwagehikesonemployment.2Ourestimatesofthee ectofanincreaseinminimumwagesonemploymentaremuchlargerthanthosetypicallyestimatedforglobalminimumwagehikesusingDDdesigns.Therearethreefactorsthatcouldexplainthisdi erence.First,estimatesinexistingDDstudiesmightbeattenuatedbyshort-runfrictions(Baker,BenjaminandStanger,1999;Sorkin,2015;MeerandWest,2015;Aaronson,FrenchandSorkin,2017),whicharenotrelevantinoursetting.Second,ourstudyisbasedonahighminimumwagelevelcomparedtomostpreviousstudies.Minimumwagesmaynotbebindingatlowlevelsand,ifbinding,theymayincreaseemploymentduetolabormarketimperfections(Manning,2003).Third,ourresultsmightbedrivenbycross-agesubstitutionratherthanpurelyadisemploymente ectoftheminimumwage.The rsttwoofthesefactorssuggestthatourresultsaddressshortcomingsoftheexistingliteratureonglobalminimumwagehikes.However,thethirdisanimportantlimitationofourstudy'sabilitytospeaktothisdebate.Cross-agesubstitutionwouldimplythatweestimatehigheremploymentelasticityinoursettingthanwouldbeseenwithaglobalminimumwagechange.Theextenttowhichthisparticularfactordrivesourlargeestimatedeterminestheextenttowhichreadersshouldupdatetheirbeliefsabouttheemploymente ectsofglobalminimumwagehikes.Onthewhole,therefore,itisdi culttoimaginethatour ndingswillmakereaderslessconcernedaboutemploymente ectsofhighminimumwages,butwhetherandtowhatextenttheyshouldbemoreconcerneddependsonwhattheybelieveaboutthemechanismsbehindourresults.Ourworkalsocontributestothetheoreticalliteratureonthee ectsofminimumwages.MuchoftheliteratureattemptstorationalizeearlyDDstudies ndingsmallorevenpositiveemploymente ectsusingmodelswithmonopsonypowerorotherlabormarketimperfections(RebitzerandTaylor,1995;Manning,2003;Flinn,2006).Our ndingsoflarge,negativeemploymente ectsaroundage-basedminimumwagesalignbetterwithbindingminimumwagesinacompetitivelabormarketmodel.Theminimumwageliteratureoftenassumesthatworkers/jobsarehomogenouswithadownwardslopinglabordemandduetoade-creasingmarginalproductoflabor.Thisisincontrasttotheoptimalincometaxliteraturenormallyassumingheterogeneousproductivities(Mirrlees,1971).Ourexplanationsoftheempirical ndingsarebasedontheorywithheterogeneousproductivities,similartootherrecentminimumwageresearch(ClemensandWither,2016;ClemensandStrain,2017).Thefactthatsomeindividualslosetheirjobwhentheyturn18,whileotherskeeptheirjob,stronglysuggeststhatheterogeneousproductivityisanimportantaspectofthelow-skilledlabormarket.

Suppressing thoughts often leads to a “rebound” effect; unpleasant thoughts were more prone to rebound in dreams than pleasant ones; may be support for an emotion‐processing theory of dream function

The effects of dream rebound: evidence for emotion‐processing theories of dreaming. Josie Malinowski, Michelle Carr, Christopher Edwards , Anya Ingarfill, , Alexandra Pinto. Journal of Sleep Research, March 12 2019. https://doi.org/10.1111/jsr.12827

Abstract: Suppressing thoughts often leads to a “rebound” effect, both in waking cognition (thoughts) and in sleep cognition (dreams). Rebound may be influenced by the valence of the suppressed thought, but there is currently no research on the effects of valence on dream rebound. Further, the effects of dream rebound on subsequent emotional response to a suppressed thought have not been studied before. The present experiment aimed to investigate whether emotional valence of a suppressed thought affects dream rebound, and whether dream rebound subsequently influences subjective emotional response to the suppressed thought. Participants (N = 77) were randomly assigned to a pleasant or unpleasant thought suppression condition, suppressed their target thought for 5 min pre‐sleep every evening, reported the extent to which they successfully suppressed the thought, and reported their dreams every morning for 7 days. It was found that unpleasant thoughts were more prone to dream rebound than pleasant thoughts. There was no effect of valence on the success or failure of suppression during wakefulness. Dream rebound and successful suppression were each found to have beneficial effects for subjective emotional response to both pleasant and unpleasant thoughts. The results may lend support for an emotion‐processing theory of dream function.

Foraging: age of peak productivity between 30 and 35 years of age, though high skill is maintained throughout much of adulthood

The Life History of Human Foraging: Cross-Cultural and Individual Variation. Jeremy Koster et al. bioXiv Mar 12 2019, https://doi.org/10.1101/574483

Abstract: Human adaptation depends upon the integration of slow life history, complex production skills, and extensive sociality. Refining and testing models of the evolution of human life history and cultural learning will benefit from increasingly accurate measurement of knowledge, skills, and rates of production with age. We pursue this goal by inferring individual hunters' of hunting skill gain and loss from approximately 23,000 hunting records generated by more than 1,800 individuals at 40 locations. The model provides an improved picture of ages of peak productivity as well as variation within and among ages. The data reveal an average age of peak productivity between 30 and 35 years of age, though high skill is maintained throughout much of adulthood. In addition, there is substantial variation both among individuals and sites. Within study sites, variation among individuals depends more upon heterogeneity in rates of decline than in rates of increase. This analysis sharpens questions about the co-evolution of human life history and cultural adaptation. It also demonstrates new statistical algorithms and models that expand the potential inferences drawn from detailed quantitative data collected in the field.

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THE LIFE HISTORY OF HUMAN FORAGING:CROSS-CULTURAL AND INDIVIDUAL VARIATIONJEREMY KOSTER1;2, RICHARD MCELREATH2;3, KIM HILL4, DOUGLAS YU5;6,GLENN SHEPARD JR.7, NATHALIE VAN VLIET8, MICHAEL GURVEN9,HILLARD KAPLAN10, BENJAMIN TRUMBLE4;11, REBECCA BLIEGE BIRD12,5DOUGLAS BIRD12, BRIAN CODDING13, LAUREN COAD8;14, LUIS PACHECO-COBOS15,BRUCE WINTERHALDER3, KAREN LUPO16, DAVE SCHMITT17, PAUL SILLITOE18,MARGARET FRANZEN19, MICHAEL ALVARD20, VIVEK VENKATARAMAN21,1Department of Anthropology, University of Cincinnati, Cincinnati OH 45221-03802Max Planck Institute for Evolutionary Anthropology3Department of Anthropology & Graduate Group in Ecology, University of California,Davis4School of Human Evolution and Social Change, Arizona State University5State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology6School of Biological Sciences, University of East Anglia7Museu Paraense Emílio Goeldi8Centre for International Forestry Research9Department of Anthropology, University of California, Santa Barbara10Economic Science Institute, Chapman University11Center for Evolution and Medicine, Arizona State University12Department of Anthropology, Pennsylvania State University13Department of Anthropology, University of Utah14School of Life Sciences, University of Sussex15Facultad de Biologia, Xalapa Universidad Veracruzana16Department of Anthropology, Southern Methodist University17Department of Anthropology, Southern Methodist University18Anthropology Department, Durham University19Unaffiliated20Department of Anthropology, Texas A&M University21Institute for Advanced Study in Toulouse22Department of Anthropology, Dartmouth College23Departments of Biology and Anthropology, University of Richmond24Department of Anthropology, SIL International25Department of Conservation Biology, University of Göttingen, Germany and BiologyDepartment- FMIPA, Cenderawasih University, Papua Indonesia26Department of Geography, University of Helsinki27Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona and Institut deCiència i Tecnologia Ambientals (ICTA), Universitat Autònoma de Barcelona28Institut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona29Eco-anthropology & Ethnobiology Laboratory, UMR 7206 (CNRS-MNHN). Musée del’Homme - Muséum national d’Histoire naturelle, Paris30Metapopulation Research Centre (MRC), Department of Biosciences, University ofHelsinki31Faculty of Archaeology, Leiden University, Netherlands32School of Anthropology and Conservation, University of Kent33Department of Anthropology, Boise State University34Department of Food and Resource Economics, University of Copenhagen35School of Culture, History and Language, Australian National UniversityE-mail address:jeremy.koster@uc.edu.1.CC-BY-NC-ND 4.0 International licenseIt is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint. http://dx.doi.org/10.1101/574483doi: bioRxiv preprint first posted online Mar. 12, 2019;

2KOSTER ET AL.THOMAS KRAFT9, KIRK ENDICOTT22, STEPHEN BECKERMAN12, STUART A. MARKS23,THOMAS HEADLAND24, MARGARETHA PANGAU-ADAM25, ANDERS SIREN26, KAREN10KRAMER13, RUSSELL GREAVES13, VICTORIA REYES-GARCÍA27, MAXIMILIEN GUÈZE28,ROMAIN DUDA29, ÁLVARO FERNÁNDEZ-LLAMAZARES30, SANDRINE GALLOIS31,LUCENTEZZA NAPITUPULU28, ROY ELLEN32, JOHN ZIKER33, MARTIN R. NIELSEN34,ELSPETH READY2, CHRISTOPHER HEALEY35, AND CODY ROSS2March 11, 201915Abstract. Human adaptation depends upon the integration of slow life history, complexproduction skills, and extensive sociality. Refining and testing models of the evolution ofhuman life history and cultural learning will benefit from increasingly accurate measurementof knowledge, skills, and rates of production with age. We pursue this goal by inferringindividual hunters’ of hunting skill gain and loss from approximately 23,000 hunting records20generated by more than 1,800 individuals at 40 locations. The model provides an improvedpicture of ages of peak productivity as well as variation within and among ages. The datareveal an average age of peak productivity between 30 and 35 years of age, though high skillis maintained throughout much of adulthood. In addition, there is substantial variationboth among individuals and sites. Within study sites, variation among individuals depends25more upon heterogeneity in rates of decline than in rates of increase. This analysis sharpensquestions about the co-evolution of human life history and cultural adaptation. It alsodemonstrates new statistical algorithms and models that expand the potential inferencesdrawn from detailed quantitative data collected in the field.Keywords:Human evolution, foraging skill, hunting, life history, Bayesian data analysis30.CC-BY-NC-ND 4.0 International licenseIt is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint. http://dx.doi.org/10.1101/574483doi: bioRxiv preprint first posted online Mar. 12, 2019;

LIFE HISTORY OF HUMAN FORAGING IN 40 SOCIETIES31. IntroductionAs a slow-developing primate, humans exhibit puzzling life history traits. Primates ingeneral, and especially the apes, have slow life histories, with late age of first reproductionand singleton births. But even compared to other hominoids, humans have longer child-hoods, shorter inter-birth intervals, and extended post-reproductive lifespans (Jones 2011).35That is, human children are slower to develop and more dependent, but we nonetheless havemore of them, more quickly. These traits are plausibly unique to the genusHomo, but thetiming and adaptive origins of the human life history strategy remains unsettled (Schwartz2012).One way for humans to ease the costs of expensive childhoods is through alloparental40investments from highly productive adults (Kramer 2010). There are at least two majorquestions lurking within, however. The first is: Which individuals provide allocare? Anyanswer to this question will have implications for how selection operates on other aspectsof life history. The second: Is childhood itself more than just a period required for growinglarge and physically adept? Is it also required for individuals to learn complex, culturally-45evolved skills (Gurven et al. 2006)? What role does childhood play in the cultural evolutionof complex, productive skills in the first place (Henrich and McElreath 2003)?Any satisfactory model of human life history must address the integration of growth,reproduction, cognitive development, skill development, sociality, and cultural evolution.This is not easy. As a result, existing models make progress by omitting some features. The50most advanced attempt we know is the optimal control model of González-Forero et al.(2017). While this model omits cultural dynamics for acquired skills, it does successfullyintegrate growth, cognitive and skill development, and reproduction in overlapping gen-erations. By solving for the optimal life history, the model suggests natural selection fordelayed growth, early investment in cognition, and delayed reproduction. The brain gets55big first, and only then the body, because this allows a longer window of learning and ulti-mately higher adult productivity. These results are similar to theembodied capital hypothesis(Kaplan et al. 2000), in which highly productive foraging and food sharing by adult mensupports alloparental investments in offspring. From this point of view, human life historytraits stem from the highly complex human foraging niche, which selects for delayed mat-60uration by requiring an extended period of learning before adults are able to achieve highproductivity. In contrast, Hawkes et al. (1998) emphasize provisioning of grandchildren bypost-reproductive women, which selects for longer lifespans. This perspective sees child-hood as a consequence of prolonged lifespan, not a trait that needs to be explained as havingits own direct function (Charnov 1993). A spectrum of models exists, in which adult forag-65ing is variably influenced by size, skill, and culturally-transmitted knowledge, and differentamounts of time are needed for individuals to acquire and perfect adult skills.To develop and test models, anthropologists have used observational studies of subsis-tence hunting, with a focus on variation across the lifespan. For example, Walker et al.(2002) and Gurven et al. (2006) report data from the southern Neotropics that subsistence70hunters achieve high proficiency only after reaching advanced ages, roughly 35 to 45 yearsold. Because hunters achieve adult size and strength much earlier in life, these results areconsistent with the embodied capital hypothesis and its emphasis on the gradual masteryof cognitively complex hunting strategies. But comparative data from other contexts havebeen scarce. Among the few other empirical studies, some find slow skill development (e.g.,75Ohtsuka 1989) while others do not (Bird and Bliege Bird 2005)..CC-BY-NC-ND 4.0 International licenseIt is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint. http://dx.doi.org/10.1101/574483doi: bioRxiv preprint first posted online Mar. 12, 2019;

4KOSTER ET AL.More and better estimates of age-related foraging skill are necessary inputs into all evo-lutionary models of human life history. Associations between brain development, culturalknowledge, physical skill, and foraging performance at each age constrain the models wespecify: quantitative and representative estimates of these variables are needed to param-80eterize optimal life history models like González-Forero et al. (2017). Variation acrossindividuals informs models of food sharing and other investments, both within and be-tween generations. Variation across sites and contexts informs models of tradeoffs and howindividuals cope with them.In principle, skill and production in other subsistence economies is equally relevant to85understanding human life history. Garden production and animal husbandry depend uponthe same cognitive and developmental foundations as hunting and gathering. We focus onsubsistence hunting for two reasons. First, the data are easier to model than are gardeningand herding—hunting returns are easier to identify with specific individuals and labor al-locations. Second, hunting is practiced, to some extent, everywhere. It is both a primitive90economy and a modern one that has endured the emergence of other subsistence strate-gies. The breadth of hunting in diverse ecological settings provides a compelling range ofevidence.Studies of hunting returns are nevertheless inferentially challenging. A typical outcomevariable, such as kilograms of harvested meat, may be a mixture of zeros and skewed positive95values that violate assumptions of conventional regression models (McElreath and Koster2014). The available foraging data often exhibit imbalanced sampling of individuals and agegroups. Predictor variables may be missing or measured with uncertainty. These problemsare surmountable in any individual study, but comparative inferences are challenging whenstudies rely on heterogeneous statistical solutions.100In this paper, we address the inferential and comparative challenges within a novel sta-tistical framework. We assemble the largest yet data base of individual human huntingrecords, comprising over 21,000 trips from 40 different study sites. These data elucidatethe extent to which the ontogeny and decline of hunting skill are attributable to individual-level or site-level factors, and the comparative analysis help to mitigate over-generalization105from individual studies. The results of this study consequently inform subsequent theorizingabout the evolution of life history traits in humans.Our statistical approach accepts the imperfections of the sample and conservatively poolsinformation, both among individuals within sites and among sites within the total sample.The goal is not to substantiate any particular theoretical model of human evolution, nor to110pretend that the data are sufficient for all inferential objectives. Rather, the goal is to showwhat can be inferred from a statistical approach that uses all available data and treats missingdata and measurement error conservatively. One of the most important aims is to highlightthe limits of existing data and approaches so that future empirical and inferential projectscan make further progress.115Our analysis supports the general conclusion that skill peaks between 30 and 35 years ofage, well after the age of reproductive maturity. Peak skill is typically not much higher thanskill during early adulthood, however. Declines with age are typically slow—an average56 year old has the same proportion of maximum skill as an average 18 year old. There isconsiderable variation both among sites and individual hunters within study sites. Variation120among individuals is described more by heterogeneity in the rate of decline than the rateof gain. Partly owing to heterogeneous data collection methods across sites and anticipated.CC-BY-NC-ND 4.0 International licenseIt is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint. http://dx.doi.org/10.1101/574483doi: bioRxiv preprint first posted online Mar. 12, 2019;

6KOSTER ET AL.Table 1. Study sites and their numerical and text codes. See the help fileof thecchuntspackage for related citations.Number Code CountryGroupDataset incchuntspackage1CRE CanadaCreeWinterhalder2 MYA BelizeMayaPacheco3 MYN NicaraguaMayangnaKoster4 QUIEcuadorQuichuaSiren5 ECH ColombiaEmbera Chami Ross6 WAO EcuadorWaoraniFranzen7 BARVenezuelaBariBeckerman8 INUCanadaInuitReady9 MTS PeruMatsigenkaYu_et_al10 PIRPeruPiroAlvard11 CLBColombiaVan_Vliet_et_al_South_America_sites12 PME VenezuelaPumeKramer_Greaves13 TS1BoliviaTsimaneFernandez_Llamazares14 TS2BoliviaTsimaneReyes-Garcia15 TS3BoliviaTsimaneTrumble_Gurven16 ACH ParaguayAcheHill_Kintigh17 GB1GabonCoad18 GB2GabonVan_Vliet_et_al_Gabon19 GB3GabonVan_Vliet_et_al_Ovan20 CN1DR CongoVan_Vliet_et_al_Phalanga21 GB4GabonVan_Vliet_et_al_Djoutou22 BK1CameroonBakaGallois23 BK2CameroonBakaDuda24 CN2CongoVan_Vliet_et_al_Ingolo25 CN3CongoVan_Vliet_et_al_Ngombe26 BFACentral African Republic Bofi and AkaLupo_Schmitt27 CN4DR CongoVan_Vliet_et_al_Baego28 BISZambiaValley BisaMarks29 HEH TanzaniaNielsen30 DLG RussiaDolganZiker31 BTKMalaysiaBatekVenkataraman_et_al32 PN1IndonesiaPunanGueze33 PN2IndonesiaPunanNapitupulu34 AGT PhilippinesAgtaHeadland35 MRT AustraliaMartuBird_Bird_Codding36 NUA IndonesiaNuauluEllen37 NIM IndonesiaNimboranPangau_Adam38 NEN Papua New GuineaNenHealey_Nen_PNG39 MAR Papua New GuineaMaringHealey40 WOL Papua New GuineaWolaSillitoe.CC-BY-NC-ND 4.0 International licenseIt is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint. http://dx.doi.org/10.1101/574483doi: bioRxiv preprint first posted online Mar. 12, 2019;

LIFE HISTORY OF HUMAN FORAGING IN 40 SOCIETIES7either the same or a different functional relationship with age.) Most sites contribute pri-marily cross-sectional data, while a few others exhibit impressive time series. The statisticalframework is designed to make use of all these data.1503. The life history foraging modelSince skill cannot be directly observed, what is required is a model with latent age-varyingskill. This unobservable skill feeds into a production function for observable hunting re-turns. In this section, we define a framework that satisfies this requirement. We explainit one piece at a time, with a focus on the scientific justification. The presentation in the155supplemental contains more mathematical detail, and the model code itself is available toresolve any remaining ambiguities about the approach. Our framework was developed andreviewed in the initial grant proposal (NSF #1534548) prior to seeing the assembled sam-ple. Therefore, whatever the model’s flaws, they do not include being designed specially forthese observations or chosen to produce a desired result.160One advantage of the latent skill approach is that it allows us to use different observationsfrom different contexts—both solo and group hunting, for example—to infer a commonunderlying dimension of skill. But modeling even the simplest foraging data benefits fromthis approach, as hunting returns often are highly zero-augmented. Separate productionfunctions for zeros and non-zeros are needed to describe such data. In principle, more165than one dimension of latent skill could be modeled. We restrict ourselves to only one inthe current analysis. With more detailed data, describing additional dimensions should bepossible.We implemented the model both as a forward simulation and as a statistical model. Theforward simulation generates data with known parameter values, which are used to confirm170that the estimated statistical model can recover the parameters. The code is available as partof thecchuntsR package.3.1.Latent skill model.One of the simplest life history models is the von Bertalanffy(1934) asymptotic growth model. We use this model to represent the increasing compo-nents of hunting skill as a function of age. These increasing components include knowledge,175strength, cognitive function, and many other aspects that contribute to hunting success andincrease but decelerate with age. For convenience, label the composite of these componentsknowledge. Assume that the rate of change in knowledge with respect to agexis given bydK=dx=k(1