We investigate the impact of papal visits to Italian provinces on abortions from 1979 to 2012: Find a 10–20% decrease in abortions that commences in the 3rd month and persists until the 14th month

Papal visits and abortions: evidence from Italy. Egidio Farina, Vikram Pathania. Journal of Population Economics, December 31 2019. https://link.springer.com/article/10.1007/s00148-019-00759-0

Abstract: We investigate the impact of papal visits to Italian provinces on abortions from 1979 to 2012. Using administrative data, we find a 10–20% decrease in the number of abortions that commences in the 3rd month and persists until the 14th month after the visits. However, we find no significant change in the number of live births. A decline in unintended pregnancies best explains our results. Papal visits generate intense local media coverage, and likely make salient the Catholic Church’s stance against abortions. We show that papal visits lead to increased church attendance, and that the decline in abortions is greater when the Pope mentions abortion in his speeches.

Keywords: Abortion Religion Pope Culture Fertility

7 Discussion

To recap, we find a large and statistically significant decrease in abortions, ranging from − 10 to − 20%, that commences about 3 months after the papal visit and persists until the 14th month. We do not find any subsequent uptick in live births. The findings taken together suggest a strong indirect effect—papal visits induce a reduction in unintended pregnancies that starts around the time of the visits and persists for almost 1 year. In contrast, any direct effects of restriction in demand and/or supply of abortion appear to play limited role.

We have been agnostic whether the decrease in unintended pregnancies is being driven by increased abstinence or increased usage of contraceptives; we have bundled both behaviours as contraception. Despite extensive search, we were unable to locate extant Italian household survey data that would allow us to measure how papal visits affect the frequency of sexual intercourse or usage of contraceptives. But even if surveys were available, one would have to be careful in interpreting behavioural changes that would be the net effects of an increased desire for intended pregnancy among some women and also an increased aversion to unintended pregnancy among other women.21

In discussing the indirect effect, two other features of Church doctrine are relevant. First, the Church regards sexual intercourse as a sin if conducted outside sacramental marriage, or, even within wedlock, if deprived of its procreative function. Therefore, during and after papal visits, women might practise more abstinence unless they are planning on getting pregnant. Note that this is separate type of stigma that would independently drive down unintended pregnancies. Second, the Church explicitly discourages contraceptive usage because it breaks the connection between sex and reproduction within marriage and encourages recreational sex out of wedlock.22 For women seeking to minimise the risk of an unintended pregnancy, this poses a dilemma. One way out is to more abstinence. But some may opt for more usage of contraceptives as the “lesser of two evils”. Therefore, a mix of both behaviours—more abstinence and more contraceptive usage—may be driving the reduction in unintended pregnancies.

Thus far, we have assumed that all women have the chance to modify their behaviour in response to papal visits. But there is also a group of women who would have gotten pregnant before the papal visit became salient, and detected the (unintentional) pregnancy shortly before or after the visit. Faced with an increased cost of abortion, some women who would have chosen to abort might opt not to do so. We would then expect to see a contemporaneous drop in abortion, albeit smaller than the subsequent decline, and a spike in births 9 months later. But we find no such effects. It appears that, if already pregnant, papal visits do not change women’s abortion decisions. In other words, even the heightened cost of abortion is less than the cost of an unwanted birth. Looking back at the model in Section 4 (and Table 4), we can infer that among women who choose to abort, most are type II (always-aborters) and only a few are type III (switchers). Earlier, we had drawn the same inference from the lack of increase in births reported in Section 5.2. It is worth noting that the proportion of switchers is endogenous to the change in the cost of abortion; the greater the increase, the higher that proportion. The fact that switchers form only a small proportion suggests that the heightened stigma of abortion from papal visits is not sufficiently large to induce women to switch away from abortions, conditional on being pregnant.

Could our findings be driven by under-reporting?23 Stigma could drive women to switch to “back-street” abortion providers to keep the procedure secret. We cannot rule out under-reporting as a factor but it is unlikely to be the main explanation for our findings. With under-reporting, one would expect to see a drop in abortions contemporaneous with the papal visit.

How do our findings compare to those reported in the recent paper by Bassi and Rasul (2017) on the effect of the papal visit to Brazil in 1991? Their methodology is different from ours. For identification, they exploit the fortuitous timing of the 1991 DHS survey in Brazil which was fielded in the weeks before, during

and after the papal visit. They study how short-run beliefs and long-run behaviour of individuals respond to the papal persuasion. They report a substantial increase in the frequency of sexual intercourse, and a large reduction in the use of contraceptives among women interviewed post-visit. The net effect is a 26% increase in the frequency of unprotected sex that drives a positive fertility response with a spike in births 9 months post-visit. In contrast, in the Italian setting, we find no net effect on births. One plausible explanation for the difference is that a papal visit to Brazil is a much bigger event because it is so rare. This could have much larger effects on the perceived costs of abortion as well as on fertility preferences of Brazilian women.

As already noted, abortion ratios are highest among teenagers (see Table 2). We test how teen abortions respond to papal visits. The findings are reported in the Appendix A-Table 15. The effect on abortions is smaller for teenagers; there is a statistically significant decline of about 10% in the third quarter following the visit but no significant declines in other periods. We also test for the impact separately by age group, education level and marital status. The results are summarised in Appendix A, Figs. 9, 10 and 11. In general, the effects appear similar across groups except for a few differences. The decline appears to be larger for married women than for unmarried ones, and to a lesser degree, for less educated women versus higher educated ones. The finding of smaller effects for unmarried women is not surprising given what we find for teens, who likely make up a big part of the unmarried group. However, when we use pooled regressions to explicitly test whether the difference between married versus unmarried is statistically significant, we cannot reject the null of similar sized effects. The same is true for the difference by education level.

7.1 Is there a pattern to papal visits?

We ask whether papal visits are planned in response to underlying trends in abortions or religiosity. The concern is that if the Pope is more likely to visit provinces that are exhibiting a trend of relatively increasing religiosity (and concurrently a decreasing relative abortion rate), our estimate of the impact is not ‘causal’ but merely reflects this underlying trend.

First, we note that our identification strategy relies on the precise timing of the event, and in the preferred specifications, we control for province-specific quadratic time trends. Reassuringly, we find no discernible effect in the quarters (or months) preceding the event. Hence, we would argue that our estimates can be credibly inferred as causal.

Nevertheless, the time and place of a papal visit are unlikely to be random.24 The decision made by the Pope to visit a place could be driven by specific factors, e.g. motivated by the desire to reverse a general decline in religiosity among the local population that leads to an increasing abortion rate. If so, a papal visit may coincide with other (unobserved) ongoing activities by the Church in that province and during that time that could be driving our results.

We test whether the Pope is more likely to visit a province or region that exhibits an increase in the number of abortions or a decrease in the number of births or in the level of religiosity in the 1, 2 or 5 years preceding the papal event, using the following specification:

Pr(Pope Eventp,y=1)=α+β<percent>ΔiZp,y−1+γp×t+δp×t2+θp+θa+up,y$$Pr({\text{Pope Event}}_{p,y}=1)=\alpha +\beta <percent>{\Delta }_i{Z}_{p,y-1}+{\gamma }_p\times t+{\delta }_p\times t^2+{\theta }_p+{\theta }_a+u_{p,y}$$

(3)

where Pope Eventp,y is a binary indicator taking the value 1 if province p was visited by the Pope in year y; %ΔiZp,y− 1 represents the percentage change in the number of abortions, births or in the average religiosity indicator, as defined in Section 6.3, over the 1, 2 or 5 years preceding the visit of the Pope in province p, γp × t and δp × t2 are provincial yearly trends; 𝜃p and 𝜃y are province and year fixed effects.25

Table 10 displays the results from estimating Eq. 3. Starting from panel A, the papal visits do not respond to any pre-trend in the number of abortions registered in the 1, 2 or 5 years preceding the visit to a specific province. We repeat the same exercise to test whether the papal visits are influenced by changes in births (panel B) or religiosity (panel C). Also, in these cases, no statistically significant effect is detected. Overall, the results bolster confidence that our main findings are causal effects of papal visits and not merely picking up underlying trends in local religiosity and Church activities.

Behavioral correlates in twin studies: There was a moderate genetic influence on subjective sleep quality and sleep duration

Twin studies of subjective sleep quality and sleep duration, and their behavioral correlates: Systematic review and meta-analysis of heritability estimates. Juan J Madrid-Valero et al. Neuroscience & Biobehavioral Reviews, December 30 2019. https://doi.org/10.1016/j.neubiorev.2019.12.028

Highlights
• There was a moderate genetic influence on subjective sleep quality and sleep duration.
• There was substantial heterogeneity across studies.
• Heritability did not differ by geographical zone.
• Age and sex were not significant moderators of the heritability of subjective sleep quality or sleep duration.
• Genetic factors played a role in the associations between sleep variables and other factors.

Abstract: Twin studies have shown that a substantial proportion of the variance for sleep variables is due to genetic factors. However, there is still considerable heterogeneity among research reports. Our main objectives were to: 1) Review the twin literature regarding sleep quality and duration, as well as their behavioural correlates; 2) Estimate the mean heritability of subjective sleep quality and sleep duration; 3) Assess heterogeneity among studies on these topics; and 4) Search for moderator variables. Two parallel meta-analyses were carried out for sleep quality and sleep duration. Seventeen articles were included in the meta-analysis. Mean MZ correlations were consistently higher than DZ correlations. A mean heritability of 0.31 (95% CI: 0.20, 0.41) was found for subjective sleep quality (range: 0-0.43) and 0.38 (95% CI: 0.16, 0.56) for sleep duration (range: 0-1). Heterogeneity indexes were significant for both sleep quality (I2 = 98.77, p < .001) and sleep duration (I2 = 99.73, p < .001). The high heterogeneity warrants further research considering possible moderators that may affect heritability.

Keywords: HeritabilityMeta-AnalysisTwinSleep qualitySleep duration

Impacts of market integration on the development of American manufacturing, as railroads expanded through the latter half of the XIX century: Much larger aggregate economic gains than previous estimates

Railroads, Reallocation, and the Rise of American Manufacturing. Richard Hornbeck, Martin Rotemberg. NBER Working Paper No. 26594, December 2019. https://www.nber.org/papers/w26594

Program: We examine impacts of market integration on the development of American manufacturing, as railroads expanded through the latter half of the 19th century. Using new county-by-industry data from the Census of Manufactures, we estimate substantial impacts on manufacturing productivity from relative increases in county market access as railroads expanded. In particular, the railroads increased economic activity in marginally productive counties. Allowing for the presence of factor misallocation generates much larger aggregate economic gains from the railroads than previous estimates. Our estimates highlight how broadly-used infrastructure or technologies can have much larger economic impacts when there are inefficiencies in the economy.

VI Interpretation

We estimate that the railroads substantially increased the scale of the United States’ economy: increasing the production and use of materials, spurring increased capital investment,
and encouraging population growth. The economic consequences of this expansion are substantially greater than previously considered because, in most counties, the value marginal
products of materials, capital, and labor were greater than their marginal costs. We do not
estimate that railroads reduced these market distortions, whether due to firm markups or
input frictions, but the railroads generated substantial economic gains by encouraging the
expansion of an economy with market distortions.
We calculate that aggregate productivity would have been 25% lower in 1890 in the
absence of the railroads, through declines in reallocative efficiency alone. We assume that
technical efficiency would have been unchanged in the counterfactuals, but this decline in
reallocative efficiency is equivalent to a 25% decline in technical efficiency (or total factor
productivity, TFP). It is challenging to estimate aggregate TFP growth, with the proper
price deflators, but estimates suggest that annual TFP growth was approximately 0.37%
from 1855 to 1890 and 1.24% from 1890 to 1927 (Abramovitz and David, 1973). That is,
the railroads effectively contributed 31 years worth of technological innovation, by driving
increases in reallocative efficiency.79
The railroads’ 25% impact on productivity was worth 25% of GDP in 1890, or $3 billion
in 1890 dollars. As a comparison, the estimated cost of the railroad network in 1890 was $8
billion (Adams, 1895). We estimate that the railroads generated an annual private return
of 3.5% in 1890,80 which increases to an annual social return of 7.5% – 8.3% once including
estimates from Fogel (1964) or Donaldson and Hornbeck (2016) and increases to 43% when
also including our estimated impact on productivity.81 These estimates imply that the
railroad sector was capturing roughly 8% of its social return in 1890.
Our estimated increases in productivity do not include the direct benefits of the railroads
from decreasing resources spent on transportation. To see this, consider that we would mechanically estimate no impact on productivity from the railroads if there were no differences
between value marginal product and marginal cost, whereas the economy would still benefit
through decreases in transportation costs. In our model, those decreases in transportation
costs are capitalized into higher land values.
Donaldson and Hornbeck (2016) estimate that agricultural land values would have fallen
by 60% without the railroad network, which, multiplying by an interest rate, generates
annual economic losses equal to 3.2% of GDP. The total loss of all agricultural land would
only generate annual economic losses equal to 5.35% of GDP, so an analysis of agricultural
land values could never find larger economic impacts.82
The crucial difference in our approaches is that Donaldson and Hornbeck (2016) assume
an efficient economy, in which value marginal product is equal to marginal cost, such that all
output value is paid to factors. By contrast, our estimated increases in reallocative efficiency
reflect the creation of output value that is not paid to factors. In both of our analyses, the
railroads increase the scale of the US economy, but because we allow for the marginal value
of product to exceed marginal costs, this increase in economic activity generates surplus
or “profit” that is reflected in aggregate productivity growth rather than increases in land
values. Further, our estimated impacts on productivity do not include any economic gains
reflected in increased factor payments, and so our estimated impact on productivity is in
addition to impacts on land value.
A general implication for measuring economic incidence is that factor payments do not
include all economic gains when there are market distortions. More inelastically supplied
factors will bear more economic incidence, but there are additional economic gains that are
not captured by factor payments. We show that these additional economic gains can be
substantively large, particularly when new infrastructure investment or new technologies are
broadly used and encourage broad expansion of economic activity.
The additional economic gains, from decreasing resources spent on transportation, could
instead be measured directly by calculating the decreases in transportation costs using the
railroads instead of the waterways. This is precisely the social savings calculation in Fogel
(1964), which implies that our estimated impact on productivity is in addition to Fogel’s
estimate of 2.7% of GDP.
In considering why Fogel’s estimates do not include our estimated economic gains, we
highlight the importance of resource misallocation in welfare analysis more generally. Fogel
(1964) proposes a social savings calculation to bound the economic gains from the railroads.
Fogel focuses on the transportation sector, and looks to calculate the additional cost from
using waterways to transport goods instead of the railroads. This calculation is closely
related to aggregate productivity in the transportation sector, measured as revenue minus
costs: for transporting the same quantity of goods (fixing revenue), calculating the increase
in costs without the railroads.83
David (1969) critiques Fogel’s calculations on several grounds, but much of this critique
is essentially calling attention to Fogel’s implicit assumption that value marginal product is
equal to marginal cost.84 This assumption is required for the increase in transportation costs
without the railroads to equal the value lost from decreased production in non-transportation
sectors. David (1969) proposes that this assumption would be violated by increasing returns
to scale, and Fogel (1979) responds by disputing the empirical magnitude of increasing
returns to scale.85 Fogel (1979) also makes this assumption more explicit: that in nontransportation sectors, firms’ value marginal product is equal to their marginal cost.
Our analysis relaxes this assumption, and estimates the economic consequences from
a broader range of market distortions, which restates the above critique by David (1969).
Rather than appealing to increasing returns to scale, we allow for a wide variety of distortions that can drive a wedge between the social benefit and private cost of firms expanding
production (e.g., firm markups, credit constraints, taxes and regulation, imperfect property
rights).86 The railroads decrease transportation costs, effectively subsidizing the expansion
of economic activities throughout the economy that have a positive social return (i.e., whose
value marginal product exceeds their marginal cost).
Fogel (1964, 1979) emphasizes that assuming an inelastic demand for transportation
provides an upper bound estimate on the railroads’ impacts, for the social savings calculation,
but the opposite is true in the presence of market distortions. A greater elasticity of demand
for transportation magnifies the economic impacts of the railroads by yielding greater changes
in activities whose value marginal product exceeds marginal cost across other sectors in the
economy. Fogel does not consider the indirect losses in other sectors due to reductions in
transported goods, because he aims to calculate the costs of maintaining the same levels of
transportation, but it is precisely because transportation would fall that there are such large
indirect losses in other sectors.87
More generally, there is an analogous need to consider resource misallocation in partial
equilibrium welfare analysis. Harberger (1964) lays the foundation for much welfare analysis
in economics, using the example of calculating the economic cost of a tax, making a powerful
assumption that there are no other distortions in the economy. This assumption means that
it is not necessary to consider how a marginal tax affects other activities, which reflect
only small welfare “triangles,” and the welfare effects of the tax are largely captured by the
demand curve for the taxed activity.88 Harberger (1964) makes this assumption clear, and
notes that it probably has the effect of understating the true cost of a tax, but this assumption
is often overlooked in applications due to its substantial analytical convenience.89 In Fogel’s
application, when analyzing the impacts of a higher transportation cost (similar to a higher
tax), the demand curve for transported goods is used to capture the welfare effects, and the
mistake is to not consider impacts from resulting changes in other activities.
Our estimated impacts of the railroads are a reminder that indirect effects on other
economic activities can generate substantial economic benefits, which are missed in partial
equilibrium welfare analysis. When there is resource misallocation, such as due to firm
markups or capital constraints, and other activities are under-provided then there are firstorder welfare gains from their encouragement. Only in a special case, when there are no
market distortions and other economic activities are efficient, can we invoke the envelope
theorem and consider only the direct economic effects

British adolescent twins study: Contact with the justice system—through spending a night in jail/prison, being issued an anti‐social behaviour order (ASBO), or having an official record—promotes delinquency

Does contact with the justice system deter or promote future delinquency? Results from a longitudinal study of British adolescent twins. Ryan T. Motz et al. Criminology, December 29 2019. https://doi.org/10.1111/1745-9125.12236

Abstract: What impact does formal punishment have on antisocial conduct—does it deter or promote it? The findings from a long line of research on the labeling tradition indicate formal punishments have the opposite‐of‐intended consequence of promoting future misbehavior. In another body of work, the results show support for deterrence‐based hypotheses that punishment deters future misbehavior. So, which is it? We draw on a nationally representative sample of British adolescent twins from the Environmental Risk (E‐Risk) Longitudinal Twin Study to perform a robust test of the deterrence versus labeling question. We leverage a powerful research design in which twins can serve as the counterfactual for their co‐twin, thereby ruling out many sources of confounding that have likely impacted prior studies. The pattern of findings provides support for labeling theory, showing that contact with the justice system—through spending a night in jail/prison, being issued an anti‐social behaviour order (ASBO), or having an official record—promotes delinquency. We conclude by discussing the impact these findings may have on criminologists’ and practitioners’ perspective on the role of the juvenile justice system in society.

Keywords: delinquency     family fixed effects     labeling     specific deterrence     twins

4 DISCUSSION AND CONCLUSION

We sought to conduct a rigorous test between deterrence and labeling hypotheses. Drawing on data from a nationally representative and longitudinal birth cohort of British adolescent twins, we found that contact with the justice system—through spending a night in jail/prison, being issued an ASBO, or having an official crime record—promotes misbehavior, which supports the labeling hypothesis. With this in mind, we highlight four contributions from this study that warrant consideration. We then discuss some of the broader implications our findings might have for the justice system.

4.1 Contributions

First, we followed the call of previous research (see, e.g., Farrington, 2003; Murray et al., 2009; Piquero et al., 2011; Pogarsky, 2002) and employed one of the most rigorous nonexperimental methodological designs capable of accounting for a wide range of selection effects and confounding influences. Using the family fixed‐effects model (Kohler et al., 2011), we leveraged nationally representative twin data to take advantage of the natural experiment that twins provide by the fact that they share their family environment and their genetic endowments. Such family effects work to make twins similar to one another. By focusing on within‐twin pair differences, then, we were able to rule out the effects of these family environments and genetic influences, providing us the opportunity to glean some of the most precise estimates for the impact of justice system contact on future behavior. In doing so, we have demonstrated that twin samples and methods have utility for criminological theory testing that reaches beyond the typical strategy of estimating heritability (see Moffitt & Beckley, 2015).
A second feature of this study is that we drew on three separate measures—two that were self‐reported and one obtained from official Ministry of Justice records—of justice system contact. The pattern of findings was substantively consistent across these specifications, providing robust support for the labeling hypothesis. The findings across such forms of contact demonstrate that even sanctions that do not penetrate far into the justice system are potentially criminogenic, an outcome that has important implications for policy. Of interest to labeling theorists, the effect of ASBO was found to be a substantively strong predictor of later misbehavior. This is important because, in our opinion, the ASBO represented an archetypal label—recall that it was not intended to be punitive; rather, it was intended to be preventative by identifying those who were at risk of bad behavior. It was also intended to be a public label, and that is exactly the effect it seemed to have had. We believe the findings from the ASBO analysis are particularly revealing given this context even though ASBOs are no longer in use.
Third, we analyzed as an outcome broad‐spectrum delinquency rather than an official outcome (e.g., rearrest or reconviction) that is more commonly assessed in the deterrence and labeling literatures. The results for justice system outcomes like rearrest may be biased because individuals who experience such contact are often at an increased risk for future contact with the justice system simply because they are known by its actors, such as arresting police officers. An outcome variable such as delinquency, therefore, allowed for us to observe change in behavior that is not biased by the actions of justice system actors. Furthermore, by relying on self‐reported delinquency, we can capture delinquent and illegal acts done by the participants that may not be known to the justice system, which would not be captured if we were to rely on official records. For these reasons, we believe the focus on self‐reported delinquency represents an important contribution to the labeling literature.
Fourth, we relied on a sample of individuals who are within the primary age range for engaging in antisocial behavior (i.e., 18‐year‐olds). This is important as it captures the impact of justice system contact for those who are peaking in their criminal careers. The impact of such contact for this population is notable as the increase in problem behavior may lead to a downward spiral of cumulative continuity for certain youth (e.g., Caspi, Bem, & Elder, 1989; Moffitt, 1993; Nagin & Paternoster, 1991; Sampson & Laub, 1992). Yet it should be noted that, at this time, we cannot observe how the increases in scores for delinquency will go on to affect participants’ criminal trajectories. Follow‐up analyses of this cohort with future phases of data collection will be better suited to answer that question.

4.3 Broader considerations

With the contributions of this study in mind, we now consider the broader substantive, theoretical, and ethical concerns that may stem from them. Particularly, we focus on the concerns revolving around the role of the justice system and its impact on juveniles. With evidence that the impact of contact with the justice system is a substantively negative one, an interesting question can be raised: Why would we have expected contact with the justice system to have a deterrent effect? Perhaps if justice system contact caused people to “fear their future self,” we would see deterrent effects (Paternoster & Bushway, 2009). But what we found is that justice system contact may have the opposite effect—rather than causing people to fear their future self, it may cause them to lose confidence in their future self. Therefore, the current system may work in a way that does not motivate individuals to conform to the norms of society. Instead, it leads young people to doubt their ability to get themselves out of the hole they have dug.
This makes sense when we consider the real‐life consequences of spending time in jail—the event itself is often embarrassing and shameful. Typically, it consists of (at least) an overnight stay followed by a visit with a judge the next morning. The family often has to get involved for the young person to be released back into the community, which then causes anger, hostility, and embarrassment among family members. Given that family is an important part of the desistance process, weakening those social bonds is unlikely to have a crime‐reducing effect. Furthermore, these reactions are often extended out to other interpersonal relationships in different settings, and as these relationships are ruined, prosocial connections are further attenuated, pushing the labeled adolescent further away from conventional society.
What does this mean for the justice system as it is currently constructed? We do not believe our findings show support for a shift to nonintervention. Rather, we believe it is important for the justice system and its actors to recognize the potentially negative impact it has. The public should be aware that the system is for the purpose of justice and retribution and that a utilitarian outcome such as specific deterrence is unlikely. With this in mind, our findings can be used to extend two policy recommendations.
First, although not a test of these hypotheses, we believe our findings fall in line with the arguments of the principles of effective intervention (see Andrews, 1995; Bonta & Andrews, 2016; Gendreau, 1996), which propose low‐risk offenders should not be funneled through official justice system channels. There should be diversionary programs set up for these types of offenders so that they may be able to avoid the labeling process. A metaphor might help explain: Medical doctors do not send a patient suffering from a cold to the emergency room. Even though the patient can certainly get treatment there, the visit would likely be counterproductive as the patient would be exposed to far more harmful viruses and diseases that may ultimately result in worse health. Study findings have repeatedly shown that when low‐risk offenders are brought into the justice system, the outcome is almost exclusively iatrogenic (e.g., Gatti, Tremblay, & Vitaro, 2009; Lowenkamp, Latessa, & Holsinger, 2006; Nagin, Cullen, & Jonson, 2009; Sperber, Latessa, & Makarios, 2013).
Second, our findings do not show support for fewer (or more) juvenile arrests. But they do indicate that if arrest rates are going to be maintained at their current level (or if they are to be heightened), then there should be a concerted effort toward offsetting the negative pathways that they create. If policy makers gain an understanding of these processes and pathways, they can develop and implement strategies to prevent labeling effects. Only then will the system have a chance of deterring criminal activity by way of contact with the offender.

Weak evidence that the national homicide rate spiked in 2015: The 2015 homicide rate increased above the 90% prediction interval for our model, but not more conservative intervals

Yim, Ha-Neul, Jordan R. Riddell, and Andrew P. Wheeler. 2019. “Is the Recent Increase in National Homicide Abnormal? Testing the Application of Fan Charts in Monitoring National Homicide Trends over Time.” SocArXiv. November 4. doi:10.31235/osf.io/7g32n

Abstract
Purpose: The goal of this study is to compare the increase in the 2015 national homicide rate to the historical data series and other violent crime rate changes. 
Methods: We use ARIMA models and a one-step ahead forecasting technique to predict national homicide, rape, robbery, and aggravated assault rates in the United States. Annual Uniform Crime Report data published by the Federal Bureau of Investigation are used in our analysis.
Results: The 2015 homicide rate increased above the 90% prediction interval for our model, but not more conservative intervals. Predictions intervals for other national level crime rates consistently produced correct coverage using our forecasting approach.
Conclusions: Our findings provide weak evidence that the national homicide rate spiked in 2015, though data for 2016 – 2018 do not show a continued anomalous increase in the U.S. homicide rate.

Data and code to replicate the findings can be downloaded from https://www.dropbox.com/sh/3086vtoqly5qho6/AABq_weh2LTMtBp426vhZ0EHa?dl=0


Conclusion

Media outlets reported the seemingly abrupt increase in homicide in big cities in the United
States and scholars examining homicide trends also argued the change in homicide was substantial
enough to demand scrutiny and attention in the interest of public safety (Rosenfeld et al., 2017).
Although a growing number of studies have explored the significance of the 2015 increase in homicide,
their findings are limited by a reliance on percent change or trend analysis, which have limitations in
finding the abnormal patterns in recent changes over time (Wheeler, 2016; Wheeler & Kovandzic, 2018).
This study builds upon the prior literature by using a forecasting method with an accompanying fan
chart to examine whether the 2015 homicide rate increase is significant or just a result of random
fluctuations. Past research using percent change techniques has suggested the 2015 increase in the
national homicide rate was significant. In conjunction with their findings, we offer a similar
interpretation of the homicide increase in 2015, although having arrived at the conclusion through the
use of ARIMA prediction models and fan charts – strengthening the overall conclusion that the national
homicide rate significantly increased in 2015. Our model estimates that the 2015 increase was near a 1
in 100 chance occurrence given the historical data, which we believe is sufficient to suggest that 2015
was an anomalous increase. Although such a bright line is ultimately arguable, individuals can set such a
threshold themselves to determine if such a chance occurrence is worth further investigation or any
subsequent responses.
Our comparative analysis indicates that the change in 2015 is pronounced only in homicides and
not in other violent crime. The analysis above for homicides finds that the 90% prediction intervals
failed to cover the 2015 homicide rise while observed annual rates for the other three crime types are
covered by the prediction bands. The overall temporal patterns for robbery, a crime that trends closely
with homicide, are similar to those of homicide during the predicted period; marked by a sudden decline
in the 1990s, slight fluctuations in the 2000s, and decreasing and seemingly spiking patterns in the 2010s.
Despite the relative similarity in the patterns between homicide and robbery, the homicide rise in 2015
was significant while the increase in robbery was not. In short, comparative analysis found that the
rising pattern is only significant in homicides, implying the rise in the 2015 homicide rate was abnormal
compared to other violent crimes. It is important to note here that while homicide, robbery, and
aggravated assault rates increased from 2014 to 2016 and decreased from 2016 to 2018, the rape rate
failed to decline and increased from 2013 to 2018. It is beyond the scope of our work here to explain the
reason the rape rate did not decrease after 2016 as the other violent crime rates did, but future research
can work to uncover potential causal factors.
Even though our results align with prior studies, our paper contributes to the literature on
temporal crime patterns by suggesting and demonstrating an alternative way to monitor crime trends and
annual fluctuations in crime statistics. Federal government employees have mainly used percent change
calculations in their official reports, which can result in chasing the noise and inefficient uses of
resources. As a result, policy implemented in response to such analysis may not be as effective or could
result in unintended consequences; however, the primary concern here is that percent change
calculations cause one to draw the conclusion that a policy response is necessary when it may not be
needed. For example, the government responded to the Police Executive Research Forum (PERF)
request for financial support in response to crime increases from 2004 to 2006 by funding them to hire
more police officers, increase training and provide technical assistance. However, Rosenfeld and Oliver
(2008) later found the crime increase was not substantial and just the result of external variables rather
than a lack of law enforcement officers. Similar inferences may be made based on more recent trends,
where short term fluctuations should not be viewed as so anomalous that they demand immediate
attention and investment. If practitioners or policymakers employ the suggested method in the policymaking
process, it may lead to timelier and more effective policy responses. Lawmakers and police
departments can use the forecasting method with daily, weekly, monthly, or yearly data to estimate the
number of police officers they will need to respond to a certain number of crime incidents. Academics
can do the same to produce new knowledge of crime trends without relying on a method that is biased
and volatile (percent change) or having to wait to complete a retrospective study of longer term trends
(via structural breaks).
The methods used in this study can be also be directly applied by criminal justice practitioners.
Specifically, the one-step ahead forecasting technique can be applied as part of the problem-oriented
policing approach to law enforcement crime prevention strategies. State and federal prison bureaus can
use this to estimate future prison populations and ensure they have the appropriate capacity each year.
Prosecutors, defense attorneys, and courts at any level can do the same to adequately provide counsel
and other court functions for cases in their jurisdiction. Utilizing ARIMA models instead of percent
change or trend analysis may be able to functionally improve budget and personnel appropriations.
A current limitation of applying such advice is that the FBI is often a year behind in reporting
national level homicide estimates. Thus such forecasts are too old to be effectively used for allocation
decisions, at least at the federal level. However, the utility of such forecasts to assist in anomaly
detection are still relevant. It would be easily possible for the FBI to not only release direct estimates of
homicides, but also provide such error intervals to better contextualize such ups and downs. This would
preempt overinterpreting minor fluctuations, whether it be by the media, politicians, or criminal justice
researchers.
We do not suppose prediction modelling to serve as the only method for exploring recent
changes in criminal justice related issues, for in our own work we recognize there are limitations
involving data and analysis. One of the limitations of this study is our reliance upon official UCR data.
While homicide is likely to be detected or reported, the other violent crimes could be underreported and
introduce bias into our estimates. Also, the use of national estimates of crime may cause some errors.
The national estimates of crime rates are the result of the imputation process for incomplete information
on reported crime or missing cases. The analysis results may be less valid due to the use of data different
from true figures. Such errors can lead one to misinterpret the result that homicide increase is abnormal.
Still, the forecasting and fan chart approach offers a viable alternative method, as shown by the coverage
of the prediction intervals for not only homicides but other national level violence crime estimates here.
But its application will always have limitations. The prediction intervals are only as good as the
specified model and the data it is supplied.
Another limitation is that we rely on statistical methods in which the underlying data have no
errors themselves. The same idea of a fan chart could in principle be applied to the National Crime
Victimization Survey (Rennison, 2000), although they have estimates of the prevalence of particular acts,
not an official number. Future research may attempt to build models that not only take into account
forecast error in identifying significant shifts in crime, but also sampling error as well.
Another limitation of the work is that while we can identify if an increase is abnormal or within
the prior historical changes, we cannot directly identify a cause of those changes. So while we identify
that the 2015 homicide rate has some evidence it is anomalous compared to historical patterns, we
cannot attribute it to any particular cause, such as de-policing. We do not include any structural level
variables like past work (Gaston et al., 2019; Gross & Mann, 2017; Rosenfeld, 2016; Rosenfeld &
Wallman, 2019) that tries to tease out the effect of changing drug markets, civilian concerns of police
violence, or de-policing strategies. While incorporating different covariates may help identify different
factors that contribute to macro level homicide trends, including covariates rarely increases forecasting
accuracy (Hyndman & Athanasopoulos, 2018). Since those variables often themselves have uncertainty,
they will not necessarily improve forecasts of future crime trends. Still, this technique can be used as a
first stage test as to whether some recent shock is causing homicides to increase. If recent changes fall
within historical prediction bands, it does not offer evidence that anything abnormal is currently
occurring. One should not use minor fluctuations in homicide rates (or any crimes) to ex ante attribute
those short term fluctuations to any particular exogenous shock.
Finally, we are unable to determine if the observed abnormality in the 2015 homicide rate is a
singular outlier, or part of a structural change in the homicide trend. For several possibilities, recent
increases in macro level crime trends have not been uniform, so it is not clear if the long term crime
drop beginning in the 1990’s has plateaued, or if overall crime is starting to slowly increase in an entire
reversal from the prior crime decline. Another is that different factors over time, such as the lethality of
violence (Berg, 2019), may be causing longer term trends, but are too small to be effectively detected by
this technique. It is also the case that singular cities can disproportionately contribute to national level
homicide increases (Rosenfeld, 2016).
Even if identifying an anomalous national level change, it would not directly say whether such a
shock is relevant for the entire U.S., or just a few select jurisdictions. It is the case though that similar
analysis can be conducted at the city level to identify anomalous changes (Wheeler & Kovandzic, 2018).
But city level analyses will have more error in the forecasts than national level estimates, due to the fact
that homicide is a quite rare event. Still, we believe national level analysis are still relevant, especially
given they are often used to justify particular policy responses. Given that many cities often follow
national level trends (McDowall & Loftin, 2009), it may be better to start with national level analysis
from a monitoring perspective, and when an anomaly is detected to drill further into the data to attempt
to uncover if particular cities are driving that change.
Researchers, practitioners, media outlets, and politicians should continue to monitor violent
crime rates, comparing them to one another and within the data series before making a definitive
statement on the “trend” of any type of violence. Crime analysts employed by larger police departments
have direct access to calls for service, incident, and arrest data for their city. It is becoming more
common for police departments in big cities to make their crime data publicly available (e.g.,
opendata.gov) and to update an online database each week or month, facilitating timely access by
researchers independent of the police department. Such data availability allows studies of local crime
rates, though aggregated national studies will still lag behind actual crime occurrences due to the length
of time (up to 18 months) it takes for official UCR data to be published. Given we find evidence that
homicide trends follow a random walk pattern, as do other researchers (McDowall, 2002; McDowall &
Loftin, 2005), it would suggest the overall rate may meander up-and-down for in the short run for any
particular period, and so two or three year increases are not guaranteed proof that the crime declines
observed in the prior 25 years are reversing. The fan charts we illustrate here can help identify short run
aberrations, and thus give more immediate feedback to policy makers on crime trends, without needing
to wait for years to identify long term trends.

False awakenings in lucid dreamers: How they relate with lucid dreams, and how lucid dreamers relate with them

False awakenings in lucid dreamers: How they relate with lucid dreams, and how lucid dreamers relate with them. Buzzi, Giorgio. Dreaming, Vol 29(4), Dec 2019, 323-338. https://psycnet.apa.org/buy/2019-78466-004

Abstract: In this article, some previously unreported findings from an old web survey about false awakenings (FAs) in 90 lucid dreamers will be discussed. FAs have been told to be frequent concomitants of lucid dreams, but objective data are lacking. In the present study, a positive correlation was found between the reported frequencies of FAs and lucid dreams, r = .51, p < .001, and 56 (62%) subjects reported experiencing habitual transitions from FAs to lucid dreams (or/and vice versa). These findings confirm previous anecdotal reports with objective data and suggest a similar neurophysiologic basis for the two kinds of experience. FAs appear to be characterized by a strong propensity of the experients to exercise a metacognitive judgment upon their state by means of reality checks (76% of respondents). Reality checkers reported that lucid dreams were a habitual termination of their FAs significantly more often than nonreality checkers (p < .001). This appears to be the first empirical datum in support of the frequently self-reported ability of lucid dreamers to turn “actively” their FAs into lucid dreams. Given the similarity between FAs and sleep paralysis in terms of possible state overlap, getting practice in performing reality checks could be a useful tool to manage some cases of recurrent sleep paralysis as well.

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From False Awakenings in Lucid Dreamers. Michelle Carr. Psychology Today, Dec 29 2019. https://www.psychologytoday.com/intl/blog/dream-factory/201912/false-awakenings-in-lucid-dreamers:

Fifty-six subjects (62%) reported that they noticed anomalies or bizarre situations during False Awakenings — for example, details out of place or devices not working properly (e.g., light switches or digital clocks).

“Usually (my False Awakenings) start with me waking up in bed. I get up and go check on my children to see if they are sleeping. I may go into the living room or back into the bedroom ... then I go back to sleep and when I wake up for real I realize that some things were out place and that I had yet another false awakening”

Sixty-eight subjects (76%) actively tested the dream to confirm whether they were awake or asleep, and 45 claimed that they used false awakenings as a bridge to lucidity:

“...a good way of inducing lucid dreams as I often perform reality checks during False Awakening.s”

“...hold my nose and breathe through it (you can if you’re dreaming).”

“...turn something on; if it’s a dream it usually comes with mechanical failure.”

Surprise: Consuming 1–5 cups of coffee/day was related to lower mortality among never smokers; they forgot to discount/adjust for pack-years of smoking, healthy & unhealthy foods, & added sugar

Dietary research on coffee: Improving adjustment for confounding. David R Thomas, Ian D Hodges. Current Developments in Nutrition, nzz142, December 26 2019. https://doi.org/10.1093/cdn/nzz142

Abstract: Meta-analyses have reported higher levels of coffee consumption to be associated with lower mortality. In contrast, some systematic reviews have linked coffee consumption to increased risks for lung cancer and hypertension. Given these inconsistencies, this narrative review critically evaluated the methods and analyses of cohort studies investigating coffee and mortality. A specific focus was adjustment for confounding related to smoking, healthy and unhealthy foods and alcohol. Assessment of 36 cohort samples showed many did not adequately adjust for smoking. Consuming 1–5 cups of coffee per day was related to lower mortality among never smokers, in studies which adjusted for pack-years of smoking, and studies adjusting for healthy and unhealthy foods. Possible reduced health benefits for coffee with added sugar have not been adequately investigated. Research on coffee and health should report separate analyses for never smokers, adjust for consumption of healthy and unhealthy foods, and for sugar added to coffee.

Keywords: Coffee, diet, methods, confounding variables, covariates, adjustment, smoking, healthy foods, cohort studies, added sugar

[Check also the study after the discussion below]

Discussion

Cohort studies provide a crucial source of evidence for investigating the long-term effects of
specific foods and dietary patterns on health. Critical analyses can help improve the quality
of cohort research designs. The focus of this narrative review was to assess the quality of
adjustment for potential confounding in research on coffee. Although this report is based on
a small number of published articles, as far as we are aware, it is the first to systematically
examine the relationship between adequacy of adjustment for smoking and food as
covariates, and the significance of these findings for research on coffee and health
outcomes such as mortality. Evidence from 34 published studies supported the view that
inadequate adjustment for confounding for both smoking and unhealthy foods reduced the
likelihood of finding a significant health protective effect for coffee. The review also noted
that the potentially negative health effects of sugar added to coffee have not been
adequately investigated.
Inadequate adjustment for confounding between coffee consumption and smoking has led to
misleading findings in both cohort studies and meta-analyses, particularly for the association
between coffee and lung cancer and pancreatic cancer. Two meta-analyses reported a
significant association between higher coffee consumption and increased risk of lung cancer
(15, 79). Both these meta-analyses did not assess the effectiveness of adjustment for
smoking in the individual studies included in the meta-analyses. An association has been
reported between coffee consumption and pancreatic cancer, but this association becomes
non-significant among non-smokers and studies which have adjusted for smoking (13). As
noted earlier, the Grosso et al. meta-analysis reported a significant linear inverse trend
between coffee intake and mortality rates among never-smokers (8). The differences
between never and ever smokers were most evident in cancer deaths. Never smokers
showed significantly lower cancer death rates with increasing coffee consumption. In
contrast, among former and current smokers, increasing coffee consumption did not reduce
risks for cancer mortality.
Given the inadequacy of adjustments commonly used for smoking, studies reporting on the
health effects of coffee, and other exposures that may be linked to smoking, should report
relative risks separately for never smokers (5, 80). As some groups show large differences in
smoking rates between men and women, separate analyses by sex should also be reported.
Future studies may also need to include the use of e-cigarettes (vaping).
These findings have implications for other dietary studies making adjustments for smoking
status, where smoking may be associated with other food variables or lifestyle patterns. For
example, an extensive review on risk thresholds for alcohol consumption based on 83
prospective studies, used a binary variable (current smoker versus non-smoker) to adjust for
smoking (81). Adjustment using binary variables is more likely to be linked to misleading
assessments of relative risks, especially where an exposure variable (alcohol) and the
potential confounder (smoking) have a non-linear association. Many published reports which
have used binary covariates to adjust for smoking may have residual confounding resulting
from smoking-associated health risks.
When smoking adjustments are used, authors should report explicitly how the variable or
variables were constructed for smoking adjustment and how these variables were entered
into regression analyses. Only one article included in the current review reported this detail
(52). Another concern is avoiding use of the term ‘non-smokers.’ This term is ambiguous,
and has been used to refer to both ‘never smokers’ and ‘non-current smokers.’ It is better to
use the terms for which the meaning is clear such as; never, former (past, previous) and
current smokers.
Higher levels of coffee intake were commonly associated with consumption of unhealthy
foods in the studies reviewed. Additional evidence for this association is evident in studies
on dietary patterns using factor analysis. Six systematic reviews focussed on food patterns
were found where coffee was reported among the specific foods related to healthy and
unhealthy eating patterns (82-87). From the six reviews, 101 individual studies using factor
analysis were examined. Among the individual studies, 14 reported the association of coffee
with the primary factors. Eleven out of 14 studies reported coffee as loading on factors
commonly labelled as ‘western’ or unhealthy among samples from nine countries. This
‘unhealthy’ pattern consisted of red meat, processed meat, refined grains, alcohol, sweet
foods and coffee (82, 84, 86, 87). These findings are consistent with the importance of
adjusting for food groups. Where potential covariation between coffee and unhealthy foods
has not been adjusted, it is less likely that higher coffee consumption will be associated with
reduced mortality and morbidity.
Current research indicates that added sugar is a risk factor for health problems such as
obesity, cardiovascular disease and diabetes (42, 88, 89). Taking coffee with added sugar,
and flavoured coffees with sugar as a sweetener, are likely to reduce the health benefits of
coffee (90). A literature search for studies investigating the association between coffee and
health outcomes found few which reported the proportion of coffee drinkers who added
sugar and none which reported the amount of sugar added.
The omission of sugar as a potential confounder in research on coffee may be based on the
assumption that sugar intake has negligible health effects. The continued omission of added
sugar is likely to be a legacy from dietary questionnaires constructed prior to 2000, which are
unlikely to have included questions to measure sugar added to coffee. The influential
NHANES question set used for repeated national surveys in the US illustrates this problem.
In NHANES, sugar added to coffee was measured by the following questions;
123 How many cups of coffee, caffeinated or decaffeinated, did you drink? (over the
last 12 months)
Ten response categories were provided from ‘None’ to ‘6 or more cups per day.’
126 How often did you add sugar or honey to your coffee or tea?
Ten response categories were provided from ‘Never’ to ‘6 or more times per day.’
The question on added sugar or honey does not provide a quantity estimate for added sugar
(e.g. teaspoons). Reports on tea and coffee consumption based on the NHANES surveys
have ignored added sugar in the profiles of groups consuming tea or coffee. For example, a
2016 paper reported around 75% of adults in the US drinking coffee in the past 12 months,
and around 49% drank coffee daily (1). No mention was made of the proportion of coffee
drinkers who added sugar.
In contrast, the more recently constructed UK Biobank question set, used in a cohort for
which recruitment started in 2003, does allow for calculation of added sugar (91). This
survey included the following question;
How much sugar did you add to your coffee (per drink)?
Six responses categories were provided, from none to 3+ teaspoons.
One of the few studies which mentioned sugar in coffee was a report on the US NIH-AARP
Diet and Health case-control study of older people (50-71 years at recruitment) (92). Among
242,171 tea and coffee drinkers in the control group, 49% did not add sugar or honey to tea
or coffee, 25% added sugar or honey and 26% added other sweeteners. Those who did not
add sweeteners to tea or coffee had a lower risk for depression than people who added any
type of sweetener (92). A Korean study reported that instant coffee mixes with added sugar
were associated with an increased risk of metabolic syndrome, compared to other types of
coffee (93).
There has been sufficient evidence at least since 2010 to justify the inclusion of added sugar
as a potential confounder in studies of the association between coffee and health outcomes.
In two umbrella reviews on coffee and health, one did not mention sugar at all (6) and the
other mentioned it as a possible limitation of the existing research (5). Given the practice of
ignoring added sugar in studies of coffee and health, nearly all the published findings on
health outcomes from drinking coffee may reflect unadjusted confounding which could
reduce the likelihood of finding health benefits from coffee. Confounding is most likely for
health outcomes where sugar has been reported as a risk factor, such as weight gain,
obesity, metabolic syndrome, diabetes and blood lipids. Confounding with added sugar may
be most likely to occur among people drinking three or more cups of coffee per day and who
add more than 1 teaspoon of sugar per cup. For example, a person drinking 5 cups per day
with 2 teaspoons of sugar per cup would have an added sugar intake of around 50 grams
per day (1 teaspoon ≈ 5 grams) just from their coffee consumption. It is possible that part of
the reduced protective effect of coffee for consumption of 5+ cups/day, which some studies
have reported, may be due to added sugar.
In terms of implications for further research related to the health effects of coffee, there were
several topics for which no research was found. These include studies investigating the
association of coffee consumption with other dietary and lifestyle patterns. A particular
pattern of interest is the use of coffee as a substitute for other beverages such as alcohol or
SSB, especially in social settings. In addition, no studies were found which included selfreporting of reactions to coffee, especially among occasional drinkers of coffee. Some
people may be allergic to coffee or have adverse reactions to caffeine. Research is needed
on the strategies people use to self-manage coffee consumption at comfortable levels. One
RCT, examining the effects of coffee, required participants to drink one litre of coffee every
day for two weeks. Negative reactions (‘palpitations and tremor during the first days of
drinking the cafetière coffee’) were reported as being sufficiently severe for one participant to
drop out of the study. However, as was evident in this report (94), RCT studies are unlikely
to gather information about participants’ reactions to exposures, in this case drinking more
coffee than usual.
For some people, coffee drinking is associated with social contact and social support (95,
96). Studies linking coffee consumption with, for example, reduced risk of depression, may
have confounding due to increased social contact and support being associated with coffee
consumption (97). More research is needed on the social contexts associated with coffee
consumption and the extent to which these contexts may have beneficial effects on health.
A pattern evident among Japanese men, and which may occur in other societies, is that
some people may consume coffee instead of alcohol in settings where both types of drinks
are available. This pattern of substitution does not appear to have been investigated. As
well, changes in coffee consumption over time, and reasons for change, appear not to have
been investigated. Only a few studies have reported consistency of coffee consumption over
a period of several years (72). A pattern needing further research is the extent to which
people take up, or increase, their coffee consumption as a substitute for drinking alcohol or
sugar-sweetened beverages.
Research using self-report measures of coffee consumption should clearly describe the
questions used to measure coffee and should note whether added sugar was measured,
including flavourings which include sugar. No information was found about the various types
of milks added to coffee. For some consumers, milk may be used instead of sweeteners to
reduce the bitterness of coffee and is likely to be a healthier option than sugar.
More reviews are needed to document which research studies on food groups and patterns
have included coffee as a food variable, and which included coffee but did not report it
because it was not associated with outcomes of interest. Research using cohort samples
should assess whether coffee drinking is associated with unhealthy eating patterns and if so,
allow for this association when adjusting for potential confounders.
This review has several limitations. It was restricted to cohort or observational studies.
Cohort studies may have unadjusted confounding which is a limitation for attributing a causal
relationship between exposures and health outcomes. The findings which have been
reported here were dependent on the assumptions made when assessing the quality of
smoking adjustment and adjustment for healthy and unhealthy foods. The assessment of the
quality and levels of significance between coffee consumption and mortality was dependent
on the information reported in each of the 34 articles reviewed. If this information was
inaccurate or incomplete, it could affect the findings reported.

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Check also Caffeine extends life span, improves healthspan, and delays age-associated pathology in Caenorhabditis elegans. George L Sutphin, Emma Bishop, Melana E Yanos, Richard M Moller & Matt Kaeberlein. Longevity & Healthspan volume 1, December 1 2012. https://longevityandhealthspan.biomedcentral.com/articles/10.1186/2046-2395-1-9. This is very important because: .1 makes clearer this may be shared with other species; .2 makes more difficult that the root cause is more healthy people drinking coffee, and easier that the coffee is what is beneficial; .3 there are less confounders (no tobacco, no sugar, & diet can be optimized).

Abstract
Background: The longevity of an organism is influenced by both genetic and environmental factors. With respect to genetic factors, a significant effort is being made to identify pharmacological agents that extend life span by targeting pathways with a defined role in the aging process. On the environmental side, the molecular mechanisms responsible for the positive influence of interventions such as dietary restriction are being explored. The environment experienced by humans in modern societies already contains countless compounds that may influence longevity. Understanding the role played by common compounds that substantially affect the aging process will be critical for predicting and interpreting the outcome of introducing new interventions. Caffeine is the most widely used psychoactive drug worldwide. Prior studies in flies, worms, and mice indicate that caffeine may positively impact age-associated neurodegenerative pathology, such as that observed in Alzheimer’s disease.

Results: Here we report that caffeine is capable of extending life span and improving healthspan in Caenorhabditis elegans, a finding that is in agreement with a recently published screen looking for FDA-approved compounds capable of extending worm life span. Life span extension using caffeine displays epistatic interaction with two known longevity interventions: dietary restriction and reduced insulin signaling. Caffeine treatment also delays pathology in a nematode model of polyglutamine disease.

Conclusions: The identification of caffeine as a relevant factor in aging and healthspan in worms, combined with prior work in both humans and rodents linking caffeine consumption to reduced risk of age-associated disease, suggests that caffeine may target conserved longevity pathways. Further, it may be important to consider caffeine consumption when developing clinical interventions, particularly those designed to mimic dietary restriction or modulate insulin/IGF-1-like signaling. The positive impact of caffeine on a worm model of polyglutamine disease suggests that chronic caffeine consumption may generally enhance resistance to proteotoxic stress and may be relevant to assessing risk and developing treatments for human diseases like Alzheimer’s and Huntington’s disease. Future work addressing the relevant targets of caffeine in models of aging and healthspan will help to clarify the underlying mechanisms and potentially identify new molecular targets for disease intervention.

Persistent effects of cohort size & nonmarital births on cohort-specific homicide rates: These effects follow Black birth cohorts across the life course, leading to higher rates of homicides (victims & perpetrators)

The Enduring Influence of Cohort Characteristics on Race-Specific Homicide Rates. Matt Vogel, Kristina J Thompson, Steven F Messner. Social Forces, soz127, October 30 2019. https://doi.org/10.1093/sf/soz127

Abstract: This study extends research on cohort effects and crime by considering how bifurcated population dynamics and institutional constraints explain variation in homicide rates across racial groups in the United States. Drawing upon the extensive research on racial residential segregation and institutional segmentation, we theorize how the criminogenic influences of cohort characteristics elucidated in prior work will be greater for Black cohorts than for White cohorts. We assess our hypothesis by estimating Age-Period-Cohort Characteristic models with data for the total population and separately for the Black and White populations over the 1975–2014 period. The results reveal persistent effects of relative cohort size and nonmarital births on Black cohort-specific homicide rates but null effects among the White population. These effects follow Black birth cohorts across the life course, leading to higher rates of both homicide arrest and homicide victimization.

Summary and Discussion

Building on the seminal work of Richard Easterlin (1987), this study provides a
novel extension of the empirical literature on population dynamics and criminal
homicide. Consistent with the Easterlin thesis, our research is guided by the
assumption that large birth cohorts strain the regulatory capacity of social
institutions (e.g., families and schools), decrease informal social control, amplify
cross-cohort socialization, and increase competition for entry-level positions,
ultimately contributing to higher rates of cohort-specific violence. We depart
from prior scholarship by arguing that the study of cohort effects remains incomplete because scholars have yet to consider how the pernicious consequences
of residential segregation and labor segmentation entrenched within American
society have conditioned the influence of cohort characteristics on homicide
rates. Insofar as the key mechanism linking relative cohort size to criminal
conduct is the ability of social institutions to effectively integrate large birth
cohorts, it follows that the strongest cohort effects should operate within racial
groups over time. As such, the present study examined the relationships among
relative cohort size, nonmarital births, and age-by-race specific rates of homicide
for the years spanning 1975–2014.
The results from the empirical models generally support our racially bifurcated
perspective on cohort characteristics and crime. We find consistent evidence that
the proportion of nonmarital childbirths is positively associated with overall
rates of homicide arrest and victimization but no evidence of an effect of
relative cohort size on overall cohort-specific homicide rates over the past four
decades. As we elaborate in greater detail below, the discrepancy between our
findings and some prior research (e.g., O’Brien et al. 1999; Savolainen 2000) is
likely attributed to necessary differences in data source and observation period
between our work and others. When we turn to the APCC models separated by
race, the results are striking. The proportion of nonmarital births and relative
cohort size exert strong, positive effects on cohort-specific homicide arrest and
victimization rates among Blacks. The highest rates of homicide victimization
and arrest are observed among Blacks born during periods of relatively high
fertility and those born during times of high nonmarital births. We interpret
these findings in line with our key theoretical argument—the effects of cohort
composition on criminal homicide reveal themselves most strongly among the
Black population. As evidenced by the discrepancy in findings between the first
and third models in Tables 2 and 3, focusing on aggregated cohort effects for
the total population obscures important nuances in the racialized nature of the
influence of population dynamics on homicide trends in recent decades.
We speculate that the most likely culprit driving these differential effects is
a labor market that allows large White birth cohorts to edge Blacks out of
low-skilled positions. Such a mechanism does not require a far stretch of the
imagination. All else equal, labor market shocks, such as those associated with
large birth cohorts, portend that a large number of young adults will be vying
for a proportionately fewer of entry-level positions. When labor supply exceeds
demand, employers can be more discriminating in staffing decisions. Given the
storied history of discriminatory hiring practices in the United States and a labor
market clearly differentiated by race, it seems reasonable to expect that Blacks
will be hit especially hard during times of labor surplus. From this vantage
point, Black Americans may find themselves at a disadvantage when they are
born during a time of high fertility because they will encounter greater levels of
competition with other young African Americans and greater competition with
Whites who may edge into traditionally segmented positions. The deleterious
effects of cohort size follow Black birth cohorts across the life course, translating
into elevated rates of homicide victimization and arrest. This finding helps shed
light on the lack of an association between relative White cohort size and
homicide rates—because social institutions are better able to accommodate large
White cohorts by further disadvantaging Blacks, the otherwise criminogenic
influence of cohort size is mitigated among the White population.
Importantly, the relative cohort size findings are robust in the presence of
race-specific nonmarital fertility, which emerges as an independent predictor
of homicide for the age-specific total and Black crime rates, but not for agespecific White crime rates. This suggests that differences in the impact of relative
cohort size on race-specific crime rates cannot be attributed to differences in
supervision that arise from disparate nonmarital fertility trends. Additionally, it
hints at further ways in which the supervision capacities of institutions may be
more adaptive for White cohorts compared to Black cohorts.
To be clear, our intention is not to refute the rich scholarship stressing the
importance of neighborhood deprivation, subcultural norms, or persistent structural inequalities that are often invoked to explain differences in violence across
racial and ethnic groups. Instead, we hope to illuminate an often overlooked
demographic component of violent crime trends. Rather than supplant prior
explanations, we adopt the view that the racialized nature of cohort effects on
crime complements contemporary thinking on criminal violence. The inveterate
legacy of segregation and discrimination in American society has generated
vastly different social institutions clearly delineated along racial lines (Peterson
and Krivo 2010). Insofar as the mechanisms linking cohort characteristics to
criminal violence involve the capacity of such institutions to assimilate successive
generations, it follows that generations of Blacks and Whites have been raised
by families living in segregated neighborhoods, attended segregated schools, and
entered into segmented labor markets. The ability of such segregated institutions
to effectively socialize large cohorts of children is a defining factor in the
perpetuation of social inequalities, which have generated profound differences
in homicide rates across racial groups in the United States.
We would be remiss not to acknowledge several caveats with our analyses
that limit our ability to engage more directly with prior scholarship in this
area. For one, our empirical models necessitate age-by-race specific measures
of homicide and victimization. The SHR provides the longest-running source of
this information, but these data only extend back as far as 1975. While there
are clear advantages to using the SHR, our models do not directly align with
previous research (which has often relied on measures gleaned from the UCR
arrest records). Accordingly, the discrepancies between our findings for total
homicide offending and prior work may be due to broader issues with official
data collection over time. Because our analyses span different time frames,
we speculate that the differences in results for total homicide offending rates
may arise from differences in arrest reporting and subsequent disaggregation.
Regardless, our goal was not to dispute the pioneering work of others but rather
to explore more nuanced pathways through which race, population dynamics,
and segregation have influenced trends in criminal homicides in the United
States.
Relatedly, a growing body of literature demonstrates how exogenous shocks
and large-scale social changes can influence long-term crime trends (Rosenfeld
2018; Baumer, Vélez, and Rosenfeld 2018). When considered in the context
of the current study, we might anticipate such period effects to influence not
only homicide rates but to indirectly contribute to cohort characteristics by
differentially influencing the relative size and rates of nonmarital birth characterizing Black and White cohorts overtime. The most obvious examples are
the crack cocaine epidemic and the differential impact of mass incarceration on
communities of color. Indeed, both mass incarceration and the homicide boom of
the late 1980s had a disproportionate effect on specific cohorts of young, Black
men. While beyond the purview of the current study, it is entirely possible that
these period effects reshaped the long-term life chances of this cohort, far beyond
the historical moment which such events occurred.
We are also limited by our inability to examine the more proximate mechanisms linking cohort characteristics and criminal homicide, such as indicators
of school crowding, educational attainment, or labor market outcomes. And
indeed, the threats posed by omitted variable bias remain a considerable hurdle
in APCC models (O’Brien 2014). The most glaring omission in this regard is our
lack of measures of age-by-race specific unemployment rates, which would allow
us to examine directly whether labor market edging explains the strong effect of
cohort size on Black homicide rates. To our knowledge, no such measures exist
for the full period of observation used in our analyses. Rather than view this as
a critical limitation, we echo Robert K. Merton (1987) who argued that “before
one proceeds to explain or to interpret a phenomenon, it is advisable to establish
that the phenomenon actually exists, that it is enough of a regularity to require
and to allow explanation” (3). We view the empirical contributions described
here as a necessary first step toward confirming the differential impact of
population dynamics on violence within racial groups, thus laying the foundation
for further research into the mechanisms driving the disproportionate influence
of cohort characteristics on criminal homicide over the past 60 years.
Despite these caveats, our findings reaffirm the importance of systematically
incorporating demographic processes into criminological research. Social control
theories have long extolled the central role played by social institutions in
suppressing violent crime, but these theories have devoted little attention to the
ways in which rapid population growth might strain institutional capacity. Our
work further underscores the inexorable linkages between population dynamics
and institutional constraints in the propagation of racial inequality in the United
States. To paraphrase Richard Easterlin, year of birth indeed marks a generation
for life. In the context of criminal homicide in the United States, it is clear
that the enduring consequences of cohort characteristics for homicide offending
and victimization unfolded differently depending on race. Consistent with a
growing body of scholarship, the results presented here suggest that crime,
violence, and the perpetuation of racial inequality in the United States can
be best viewed through a historicized understanding of bifurcated population
dynamics.