Emodiversity: Robust Predictor of Outcomes or Statistical Artifact?

Brown, N. J. L., & Coyne, J. C. (2017). Emodiversity: Robust Predictor of Outcomes or Statistical Artifact? Journal of Experimental Psychology. General. DOI: 10.1037/xge0000330

Abstract: This article examines the concept of emodiversity, put forward by Quoidbach et al. (2014) as a novel source of information about “the health of the human emotional ecosystem” (p. 2057). Quoidbach et al. drew an analogy between emodiversity as a desirable property of a person’s emotional make-up and biological diversity as a desirable property of an ecosystem. They claimed that emodiversity was an independent predictor of better mental and physical health outcomes in two large-scale studies. Here, we show that Quoidbach et al.’s construct of emodiversity suffers from several theoretical and practical deficiencies, which make these authors’ use of Shannon’s (1948) entropy formula to measure emodiversity highly questionable. Our reanalysis of Quoidbach et al.’s two studies shows that the apparently substantial effects that these authors reported are likely due to a failure to conduct appropriate hierarchical regression in one case, and to suppression effects in the other. It appears that Quoidbach et al.’s claims about emodiversity may reduce to little more than a set of computational and statistical artifacts.

Big Data Surveillance: The Case of Policing

Big Data Surveillance: The Case of Policing. Sarah Brayne. American Sociological Review. https://doi.org/10.1177/0003122417725865

Abstract: This article examines the intersection of two structural developments: the growth of surveillance and the rise of “big data.” Drawing on observations and interviews conducted within the Los Angeles Police Department, I offer an empirical account of how the adoption of big data analytics does—and does not—transform police surveillance practices. I argue that the adoption of big data analytics facilitates amplifications of prior surveillance practices and fundamental transformations in surveillance activities. First, discretionary assessments of risk are supplemented and quantified using risk scores. Second, data are used for predictive, rather than reactive or explanatory, purposes. Third, the proliferation of automatic alert systems makes it possible to systematically surveil an unprecedentedly large number of people. Fourth, the threshold for inclusion in law enforcement databases is lower, now including individuals who have not had direct police contact. Fifth, previously separate data systems are merged, facilitating the spread of surveillance into a wide range of institutions. Based on these findings, I develop a theoretical model of big data surveillance that can be applied to institutional domains beyond the criminal justice system. Finally, I highlight the social consequences of big data surveillance for law and social inequality.

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For example, after a series of copper wire thefts in the city, the police found the car involved by drawing a radius in Palantir around the three places the wire was stolen from, setting up time bounds around the time they knew the thefts occurred at each site, and querying the system for any license plates captured by ALPRs in all three locations during those time periods.

[...]

I encountered several other examples of law enforcement using external data originally collected for non–criminal justice purposes, including data from repossession and collections agencies; social media, foreclosure, and electronic toll pass data; and address and usage information from utility bills. Respondents also indicated they were working on integrating hospital, pay parking lot, and university camera feeds; rebate data such as address information from contact lens rebates; and call data from pizza chains, including names, addresses, and phone numbers from Papa Johns and Pizza Hut. In some instances, it is simply easier for law enforcement to purchase privately collected data than to rely on in-house data because there are fewer constitutional protections, reporting requirements, and appellate checks on private sector surveillance and data collection (Pasquale 2014).  Moreover, respondents explained, privately collected data is sometimes more up-to-date.

How quickly can we adapt to change? An assessment of hurricane damage mitigation efforts using forecast uncertainty.

How quickly can we adapt to change? An assessment of hurricane damage mitigation efforts using forecast uncertainty. By Andrew Martinez

Abstract: Our ability to adapt to extreme weather is increasingly relevant as the frequency and intensity of these events alters due to climate change. It is important to understand the effectiveness of adaptation given the uncertainty associated with future climate events. However, there has been little analysis of short-term adaptation efforts. We propose a novel approach of using errors from hurricane forecasts to evaluate short-term hurricane damage mitigation efforts. We construct a statistical model of damages for all hurricanes to strike the continental United States since 1955. While we allow for many possible drivers of damages, using model selection methods we find that a small subset explains most of the variation. We also find evidence supporting short-term adaptation effects prior to a hurricane landfall. Our results show that the 67 percent improvement in hurricane forecasts over the past 60 years is associated with damages being 16-63 percent lower than they otherwise would have been. Accounting for outlying observations narrows this range to 16-24 percent.

• Department of Economics Discussion Paper Series (Ref: 831 )

Keywords: Adaptation, Natural Disasters, Uncertainty


JEL Reference: C51, C52, Q51, Q54

Sex differences in jealousy: the (Lack of) influence of researcher theoretical perspective

Sex differences in jealousy: the (Lack of) influence of researcher theoretical perspective. John Edlund et al. The Journal of Social Psycholog, http://dx.doi.org/10.1080/00224545.2017.1365686

According to the theory of evolved sex differences in jealousy (Buss, Larsen, Westen, & Semmelroth, 1992), ancestral women’s challenge of ensuring paternal investment exerted selective pressures that increased women’s jealousy in response to emotional infidelity, whereas ancestral men’s challenge of paternal uncertainty exerted selective pressures that increased men’s jealousy in response to sexual infidelity. Observing that women experience greater jealousy in response to emotional infidelity (relative to men) and that men experience greater jealousy in response to sexual infidelity (relative to women) is known as the sex differences in jealousy effect. This effect has been explored in several ways (see Edlund & Sagarin, 2017 for a comprehensive review). Most relevant to the goals of this paper are the approaches taken by Sheets and Wolfe (2001), in which participants were asked to imagine an infidelity and respond to forced-choice questions , and the approach taken by Edlund, Heider, Scherer, Farc, and Sagarin (2006), in which participants were asked to respond on continuous items .

However, this sex difference in jealousy effect has not been without significant controversy in the literature. For instance, DeSteno and colleagues (DeSteno, Bartlett, Bravermann, & Salovey, 2002) have suggested that men and women had differential interpretations of the forced-choice questions (called the “double-shot” hypothesis); however, Buss and colleagues (Buss et al., 1999) later demonstrated that the double-shot hypothesis cannot explain the relationship between participant sex and jealousy. Harris (2002) questioned whether sex differences in cognitive focus (not emotional jealousy) in response to actual experiences with infidelity mirrored hypothetical reactions and whether the effect in the hypothetical reaction literature was an artifact of the forced-choice measure. Edlund and colleagues (Edlund et al., 2006) later demonstrated that sex differences in jealousy in response to actual experiences with infidelity mirrored hypothetical reactions in both forced-choice and continuous measures. Importantly, meta-analyses have confirmed that this effect reliably emerges with both forced-choice (Harris, 2002) and continuous measures of jealousy (Sagarin et al., 2012).  One significant point of contention in the literature, and the one addressed in the current study, is whether successfully observing a sex difference in jealousy effect is driven by the theoretical perspective of the researchers. Many of the studies attempting to refute the theory of evolved sex differences in jealousy have been published in general psychological journals (e.g., Psychological Science) and in social psychology journals (e.g., Personality and Social Psychology Bulletin). Conversely, many of the studies supporting the sex difference in jealousy have been published in journals oriented toward evolutionary psychology (e.g., Evolutionary Psychology). This discrepancy is highlighted by Edlund and Sagarin (2017), who demonstrate the differences in the publication outlets, but they do not offer evidence as to whether the theoretical perspective of the researchers is the driving factor in the eventual publication outlets.

Given the disparate nature of the researchers on both sides of the debate and the resultant diversity in the outlets in which their findings have been published, one of the goals of the present research was to bring diverse researchers together (many of whom have never published with one another) to investigate whether theoretical perspective impacted the sex difference in jealousy. We also sought to provide another replication of the sex difference in jealousy effect while examining both continuous and forced-choice measures. Finally, we sought to extend the sex difference in jealousy literature by incorporating an individual difference measure – a self-perceived measure of how high quality a mate one is (i.e., mate value) – as a potential moderator of the sex difference in jealousy effect. For instance, research has shown that mate value moderates one’s preferences in a mate (e.g., Edlund & Sagarin, 2010; Reeve, Kelly, & Welling, 2017), as well as one’s intention to commit an infidelity (Starratt, Weekes-Shackelford, & Shackelford, 2017). As such, given mate value’s impact on intentions towards infidelity, we wanted to explore if it would similarly impact the reactions to an infidelity.

[...]

In summary, we have demonstrated that the sex difference in jealousy occurs in both forcedchoice and continuous response scale formats. We also demonstrated that the theoretical perspective of the researchers has no bearing on the results obtained when using identical tools. Finally, we have demonstrated that mate value moderates the sex difference in jealousy.

Kevin Bryan on “The Development Effects of the Extractive Colonial Economy,” M. Dell & B. Olken

Kevin Bryan's comments on “The Development Effects of the Extractive Colonial Economy,” M. Dell & B. Olken (2017).

Find the full text, links to other info, etc., in A Fine Theorem blog, Jun 22 2017, https://afinetheorem.wordpress.com/2017/06/22/the-development-effects-of-the-extractive-colonial-economy-m-dell-b-olken-2017/

A good rule of thumb is that you will want to read any working paper Melissa Dell puts out. Her main interest is the long-run path-dependent effect of historical institutions, with rigorous quantitative investigation of the subtle conditionality of the past. For instance, in her earlier work on Peru (Econometrica, 2010), mine slavery in the colonial era led to fewer hacienda style plantations at the end of the era, which led to less political power without those large landholders in the early democratic era, which led to fewer public goods throughout the 20th century, which led to less education and income today in eras that used to have mine slavery. One way to read this is that local inequality is the past may, through political institutions, be a good thing today! History is not as simple as “inequality is the past causes bad outcomes today” or “extractive institutions in the past cause bad outcomes today” or “colonial economic distortions cause bad outcomes today”. [...]

Dell’s new paper looks at the cultuurstelsel, a policy the Dutch imposed on Java in the mid-19th century. Essentially, the Netherlands was broke and Java was suitable for sugar, so the Dutch required villages in certain regions to use huge portions of their arable land, and labor effort, to produce sugar for export. They built roads and some rail, as well as sugar factories (now generally long gone), as part of this effort, and the land used for sugar production generally became public village land controlled at the behest of local leaders. This was back in the mid-1800s, so surely it shouldn’t affect anything of substance today?

But it did! Take a look at villages near the old sugar plantations, or that were forced to plant sugar, and you’ll find higher incomes, higher education levels, high school attendance rates even back in the late colonial era, higher population densities, and more workers today in retail and manufacturing. Dell and Olken did some wild data matching using a great database of geographic names collected by the US government to match the historic villages where these sugar plants, and these labor requirements, were located with modern village and town locations. They then constructed “placebo” factories – locations along coastal rivers in sugar growing regions with appropriate topography where a plant could have been located but wasn’t. In particular, as in the famous Salop circle, you won’t locate a factory too close to an existing one, but there are many counterfactual equilibria where we just shift all the factories one way or the other. By comparing the predicted effect of distance from the real factory on outcomes today with the predicted effect of distance from the huge number of hypothetical factories, you can isolate the historic local influence of the real factory from other local features which can’t be controlled for.

Consumption right next to old, long-destroyed factories is 14% higher than even five kilometers away, education is 1.25 years longer on average, electrification, road, and rail density are all substantially higher, and industrial production upstream and downstream from sugar (e.g., farm machinery upstream, and processed foods downstream) are also much more likely to be located in villages with historic factories even if there is no sugar production anymore in that region!

It’s not just the factory and Dutch investments that matter, however. Consider the villages, up to 10 kilometers away, which were forced to grow the raw cane. Their elites took private land for this purpose, and land inequality remains higher in villages that were forced to grow cane compared to villages right next door that were outside the Dutch-imposed boundary. But this public land permitted surplus extraction in an agricultural society which could be used for public goods, like schooling, which would later become important! These villages were much more likely to have schools especially before the 1970s, when public schooling in Indonesia was limited, and today are higher density, richer, more educated, and less agricultural than villages nearby which weren’t forced to grow cane. This all has shades of the long debate on “forward linkages” in agricultural societies, where it is hypothesized that agricultural surplus benefits industrialization by providing the surplus necessary for education and capital to be purchased; [...].

Are you surprised by these results? [me: NO!] They fascinate me, honestly. Think through the logic: forced labor (in the surrounding villages) and extractive capital (rail and factories built solely to export a crop in little use domestically) both have positive long-run local effects! They do so by affecting institutions – whether villages have the ability to produce public goods like education – and by affecting incentives – the production of capital used up- and downstream. One can easily imagine cases where forced labor and extractive capital have negative long-run effects, and we have great papers by Daron Acemoglu, Nathan Nunn, Sara Lowes and others on precisely this point. But it is also very easy for societies to get trapped in bad path dependent equilibria, for which outside intervention, even ethically shameful ones, can (perhaps inadvertently) cause useful shifts in incentives and institutions! I recall a visit to Babeldaob, the main island in Palau. During the Japanese colonial period, the island was heavily industrialized as part of Japan’s war machine. These factories were destroyed by the Allies in World War 2. Yet despite their extractive history, a local told me many on the island believe that the industrial development of the region was permanently harmed when those factories were damaged. [...]

2017 Working Paper is here [https://scholar.harvard.edu/files/dell/files/170414draft.pdf] (no RePEc IDEAS version). For more on sugar and institutions, I highly recommend Christian Dippel, Avner Greif and Dan Trefler’s recent paper on Caribbean sugar. The price of sugar fell enormously in the late 19th century, yet wages on islands which lost the ability to productively export sugar rose. Why? Planters in places like Barbados had so much money from their sugar exports that they could manipulate local governance and the police, while planters in places like the Virgin Islands became too poor to do the same. This decreased labor coercion, permitting workers on sugar plantations to work small plots or move to other industries, raising wages in the end. [...].