Society – Page 22 – Difficult Run

Increasing Alcoholism: A Follow-Up

August 22, 2017 by Walker

I posted an article a week or so ago on a new study claiming a rise in alcoholism. The study has been met with some major criticism. From Vox:

some researchers are pushing back. They argue that the data used in the study is based on a federal survey [NESARC] that underwent major methodological changes between 2001-’02 and 2012-’13 — meaning the increase in alcoholism rates could be entirely explained just by differences in how the survey was carried out between the two time periods. And they point out that the study’s conclusions are sharply contradicted by another major federal survey…That survey has actually found a decrease in alcohol use disorder from 2002 to 2013: In 2002, the percent of Americans 12 and older who qualified as having alcohol use disorder was 7.7 percent. In 2013, that dropped to 6.6 percent.

One key difference is the NESARC used data of people 18 years and older, while NSDUH used data of people 12 years and older. But even if you isolate older groups in NSDUH, the rates of alcoholism still dropped or remained relatively flat — certainly not the big rise the NESARC reported.

Now, the NSDUH isn’t perfect. For one, it surveys households — so it misses imprisoned and homeless populations, which are fairly big segments of the population and likely to have higher rates of drug use. But NESARC also shares these limitations, so it doesn’t explain the difference seen in the surveys.

Here are some of the major changes to the NESARC:

The NESARC changed some questions from wave to wave, which could lead survey takers to respond differently.
In the 2001-’02 wave, NESARC respondents were not given monetary rewards. In the 2012-’13 wave, they were. That could have incentivized different people to respond.
No biological samples were collected in the first wave, while saliva samples were collected in the second. What’s more, respondents were notified of this at the start of the survey — which could have led them to respond differently, since they knew they’d be tested for their drug use.
Census Bureau workers were used for the 2001-’02 survey, but private workers were used for the 2012-’13 survey. That could lead to big differences: As Grucza told me, “Some researchers speculate that using government employees might suppress reporting of socially undesirable behaviors.”

The article continues,

Researchers from SAMHSA told me that they would caution against trying to use the different waves of NESARC to gauge trends.

“Given these points, we would strongly caution against using two points in time as an indicator in trend, especially when the data for these two points in time were collected using very different methods and do not appear to be comparable,” SAMHSA researchers wrote in an email. “We would encourage the consideration of data from multiple sources and more than two time points, in order to paint a more complete and accurate portrayal of substance use and substance use disorder in the nation.”

In short, it looks like the JAMA Psychiatry study was based on some fairly faulty data.

When I asked about these problems surrounding the study, lead author Bridget Grant, with NIAAA, shot back by email: “There were no changes in NESARC methodology between waves and NSDUH folks know nothing about the NESARC. Please do not contact me again as I don’t know NSDUH methodology and would not be so presumptuous to believe I did.”

But based on SAMHSA’s and Grucza’s separate reviews of NESARC, its methodology did change.

When I pressed on this, Grant again responded, “Please do NOT contact me again.”

After this article was published, Grant confirmed NESARC went through some methodological changes between 2001-’02 and 2012-’13. But she argued that there’s no evidence such changes would have a significant impact on the results.

It concludes,

None of that means America doesn’t have an alcohol problem. Between 2001 and 2015, the number of alcohol-induced deaths (those that involve direct health complications from alcohol, like liver cirrhosis) rose from about 20,000 to more than 33,000. Before the latest increases, an analysis of data from 2006 to 2010 by the Centers for Disease Control and Prevention (CDC) already estimated that alcohol is linked to 88,000 deaths a year — more than all drug overdose deaths combined.

And another study found that rates of heavy drinking and binge drinking increased in most US counties from 2005 to 2012, even as the percentage of people who drink any alcohol has remained relatively flat.

But for now, it’s hard to say if a massive increase in alcohol use disorder is behind the negative trends — because the evidence for that just isn’t reliable.

Migration and Terrorism

April 10, 2018August 18, 2017 by Walker

A new study examines the link between immigrants and terrorism:

In our recent work (Dreher et al. 2017) we provide a detailed analysis of how the number of foreigners living in a country has affected the number of terrorist attacks made by foreigners on citizens of their host countries. According to the raw data, in OECD countries between 1980 and 2010, for every million foreigners in the population, 0.8 terror attacks are committed per year, per country (there were 662 transnational attacks). While it is obvious that the number of attacks increases with the number of people living in a country (after all, with no foreigners in a country, no foreigners would commit any attacks), on average these numbers amount to about one attack by foreigners per year and host country, and 1.3 people die from these attacks in the average country and year.

Transnational terror is dwarfed in absolute numbers by the number of attacks made by the domestic population. In the 20 OECD countries that our sample covers, there were 2,740 attacks arising from the domestic population. In relative terms though, the picture is different – there were fewer than 0.18 terrorist attacks for every one million locally born citizens in a typical country and year. Overall, while the probability that foreigners are involved in an attack on the domestic population was much higher than the risk that citizens were involved in attacks on their own country, the risk associated with each additional foreigner was tiny.

In our statistical analysis, we investigate whether, and to what extent, an increase in the foreign population of the average OECD country would increase the risk of terrorist attacks from foreigners in a host country. We identify exogenous variation in the number of foreigners living in an OECD country using changes in migration resulting from natural disasters. These changes affected host countries differently, according to the specifics of each host- and origin-country pair.

Using data for 20 OECD host countries, and 187 countries of origin between 1980 and 2010, we find that the number of terror attacks increased with the number of foreigners living in a host country. This scale effect that relates larger numbers of foreigners to more attacks does not imply, however, that foreigners are more likely to become terrorists than the domestic population. When we calculate the effect of a larger local population on the frequency of terror attacks by locals, the effect is of a comparable size. We conclude that, in this period, migrants were not more likely to become terrorists than the locals of the country in which they were living.

To put these results in perspective, consider the expected effect of a decrease in the domestic population of 0.0002% (which is the average decrease in the domestic population of the 20 OECD countries we studied in 2015, according to the OECD). According to our model, this would have reduced the number of terrorist attacks by 0.00025 per country and year. The increase in the stock of foreigners living in these countries was 3.6% in the same year. According to our estimates, this would have created 0.04 additional attacks. We might argue that this hardly justifies a ban on foreigners as a group.

We find little evidence that terror had been systematically imported from countries with large Muslim populations. The exceptions were Algeria and Iran, where we found a statistically higher risk of being involved in terrorist attacks against the local population, compared to the average effect of foreigners from non-Muslim countries. In this light, the phrases ‘Muslim terror’ or ‘Islamist terror’ does not seem accurate or useful. Only 6% of the terrorist attacks in the US between 1980 and 2005 period were carried out by Muslims, and less than 2% of all attacks in Europe had a religious motivation between 2009 and 2013 (Alnatour 2017).

I’ve written before about how European labor laws may play a role in radicalization. The authors make a similar case for immigration bans:

Contrary to the expectations of many politicians and pundits, introducing strict laws that regulate the integration and rights of migrants does not seem to have been effective in preventing terror attacks from foreign-born residents. We rather find that repressing migrants already living in the country with these laws has alienated a substantial share of this population, which increases the risk of terror. Stricter laws on immigration thus have the potential to increase the risk of terror, at least immediately following the ban.

…Our results illustrate an important trade-off. While stricter immigration laws could reduce the inflow of (violent) foreigners and thus potentially the number of future terrorist attacks, the restrictions would also increase the probability that those foreigners already living in the country become more violent. Immigration bans, like those recently introduced in the US, would arguably increase the short-term risk of attacks, before potentially reducing risk when the number of foreigners in the population has decreased.

Last year, I linked to a Cato study on the likelihood of a foreign terrorist attack (TL;DR: it’s astronomically low). With Charlottesville in the news, this piece from Foreign Policy was particularly interesting:

The FBI and the Department of Homeland Security in May warned that white supremacist groups had already carried out more attacks than any other domestic extremist group over the past 16 years and were likely to carry out more attacks over the next year, according to an intelligence bulletin obtained by Foreign Policy.

Even as President Donald Trump continues to resist calling out white supremacists for violence, federal law enforcement has made clear that it sees these types of domestic extremists as a severe threat. The report, dated May 10, says the FBI and DHS believe that members of the white supremacist movement “likely will continue to pose a threat of lethal violence over the next year.”

…The FBI…has already concluded that white supremacists, including neo-Nazi supporters and members of the Ku Klux Klan, are in fact responsible for the lion’s share of violent attacks among domestic extremist groups. White supremacists “were responsible for 49 homicides in 26 attacks from 2000 to 2016 … more than any other domestic extremist movement,” reads the joint intelligence bulletin.

The report, titled “White Supremacist Extremism Poses Persistent Threat of Lethal Violence,” was prepared by the FBI and DHS.

The bulletin’s numbers appear to correspond with outside estimates. An independent database compiled by the Investigative Fund at the Nation Institute found that between 2008 and 2016, far-right plots and attacks outnumbered Islamist incidents by almost 2 to 1.

Now, granted, when we consider that the Southern Poverty Law Center “estimates that [today] there are between 5,000 and 8,000 Klan members, split among dozens of different – and often warring – organizations that use the Klan name,” that’s a huge improvement over the 4 million in the mid-1920s. But I find it ironic that groups that worry about the influx of immigrants in part due to potential terror attacks are more likely to commit said attacks in recent years.[ref]Recent is important since Islamic terrorism still comes out on top when the last 3+ decades are considered. Either way, the chance of dying at the hands of a terrorist is still extremely small.[/ref]

Minimum Wage Hikes and Automation Risks

March 29, 2018August 16, 2017 by Walker

A couple years ago, I wrote,

Other studies show that an increased minimum wage causes firms to incrementally move toward automation. Now, this too could be seen as a trade-off: automation and technological progress tend to make processes more efficient and therefore increase productivity (and eventually wages), raising living standards for consumers (which include the poor). Nonetheless, the point is that while unemployment in the short-term may be insignificant, the long-term effects could be much bigger. For example, one study finds that minimum wage hikes lead to lower rates of job growth: about 0.05 percentage points a year. That’s not much in a single year, but it accumulates over time and largely impacts the young and uneducated.

A couple new studies this year demonstrate the link between minimum wage hikes, automation, and job loss. As reported from AEI’s James Pethokoukis,

Now comes the new NBER working paper, “People Versus Machines: The Impact of Minimum Wages on Automatable Jobs” by Grace Lordan and David Neumark (bold is mine):

“Based on CPS data from 1980-2015, we find that increasing the minimum wage decreases significantly the share of automatable employment held by low-skilled workers. The average effects mask significant heterogeneity by industry and demographic group. For example, one striking result is that the share in automatable employment declines most sharply for older workers. An analysis of individual transitions from employment to unemployment (or to employment in a different occupation) leads to similar overall conclusions, and also some evidence of adverse effects for older workers in particular industries. … Our work suggests that sharp minimum wage increases in the United States in coming years will shape the types of jobs held by low-skilled workers, and create employment challenges for some of them. … Therefore, it is important to acknowledge that increases in minimum wage will give incentives for firm to adopt new technologies that replace workers earlier. While these adoptions undoubtedly lead to some new jobs, there are workers who will be displaced that do not have the skills to do the new tasks. Our paper has identified workers whose vulnerability to being replaced by machines has been amplified by minimum wage increases. Such effects may spread to more workers in the future.”

Three things: First this study is a great companion piece to a recent one by Daron Acemoglu and Pascual Restrepo analyzing the effect of increased industrial robot usage between 1990 and 2007 on US local labor markets: “According to our estimates, one more robot per thousand workers reduces the employment to population ratio by about 0.18-0.34 percentage points and wages by 0.25-0.5 percent.”

Second, Lordan and Neumark note that minimum wage literature often, in effect, ends up focusing on teenager employment as it presents aggregate results. But that approach “masks” bigger adverse impacts on some subgroups like older workers who are “more likely to be major contributors to their families’ incomes.” This seems like an important point.

Third, some policy folks argue that it’s a feature not a bug that a higher minimum wage will nudge firms to adopt labor-saving automation. (Thought not those arguing for robot taxes.) The result would be higher productivity and economic growth. But perhaps we are “getting too much of the wrong kind of innovation.”

As the St. Louis Fed explains, “labor share declined 3.3 percentage points in advanced economies from 1980 to 2015”:

One of the explanations for the decline of the labor share has been an increase in productivity that has outpaced an increase in real wages, with several studies attributing half the decline to this trend.

This increase in productivity has been driven by technological progress, as manifested in a decline in the relative price of investment (that is, the price of investment relative to the price of consumption).As the relative price of investment decreases, the cost of capital goes down, and firms have an incentive to substitute capital for labor. As a result, the labor share declines.

The decline in the labor share that results from a decline in the relative price of investment has contributed to an increase in inequality: A decrease in the cost of capital tends to induce automation in routine tasks, such as bookkeeping, clerical work, and repetitive production and monitoring activities. These are tasks performed mainly by middle-skill workers.

Hence, these are the segments of the population that are more affected by a reduction in the relative price of investment. The figure below displays the correlation between changes in the advanced economies’ labor share and their Gini coefficients (which measure income inequality).

The Fed concludes,

Technological progress promotes economic growth, but as the findings above suggest, it can also reduce the welfare of a large part of the working population and eventually have a negative effect on economic growth.[ref]For more information on this last claim, see the recent research from the IMF.[/ref]

An important role for policymakers would be to smooth the transition when more jobs are taken over by the de-routinization process. At the end of the day, technology should relieve people from performing repetitive tasks and increase the utility of our everyday lives.

Alcoholism on the Rise

August 14, 2017 by Walker

From The Washington Post:

A new study published in JAMA Psychiatry this month finds that the rate of alcohol use disorder, or what’s colloquially known as “alcoholism,” rose by a shocking 49 percent in the first decade of the 2000s. One in eight American adults, or 12.7 percent of the U.S. population, now meets diagnostic criteria for alcohol use disorder, according to the study.

The study’s authors characterize the findings as a serious and overlooked public health crisis, noting that alcoholism is a significant driver of mortality from a cornucopia of ailments: “fetal alcohol spectrum disorders, hypertension, cardiovascular diseases, stroke, liver cirrhosis, several types of cancer and infections, pancreatitis, type 2 diabetes, and various injuries.”

Indeed, the study’s findings are bolstered by the fact that deaths from a number of these conditions, particularly alcohol-related cirrhosis and hypertension, have risen concurrently over the study period. The Centers for Disease Control and Prevention estimates that 88,000 people a year die of alcohol-related causes, more than twice the annual death toll of opiate overdose.

…The study found that rates of alcoholism were higher among men (16.7 percent), Native Americans (16.6 percent), people below the poverty threshold (14.3 percent), and people living in the Midwest (14.8 percent). Stunningly, nearly 1 in 4 adults under age 30 (23.4 percent) met the diagnostic criteria for alcoholism.

…The study’s data go only through 2013. If the observed trend continues, the true rate of alcoholism today would be even higher.

How is “alcoholic” defined? The study defined “alcohol abuse” with the following criteria:

Recurrent use of alcohol resulting in a failure to fulfill major role obligations at work, school, or home (e.g., repeated absences or poor work performance related to alcohol use; alcohol-related absences, suspensions, or expulsions from school; neglect of children or household).
Recurrent alcohol use in situations in which it is physically hazardous (e.g., driving an automobile or operating a machine when impaired by alcohol use).
Recurrent alcohol-related legal problems (e.g., arrests for alcohol-related disorderly conduct).
Continued alcohol use despite having persistent or recurrent social or interpersonal problems caused or exacerbated by the effects of alcohol (e.g., arguments with spouse about consequences of intoxication).

And “alcohol dependence” by the following:

Need for markedly increased amounts of alcohol to achieve intoxication or desired effect; or markedly diminished effect with continued use of the same amount of alcohol.
The characteristic withdrawal syndrome for alcohol; or drinking (or using a closely related substance) to relieve or avoid withdrawal symptoms.
Drinking in larger amounts or over a longer period than intended.
Persistent desire or one or more unsuccessful efforts to cut down or control drinking.
Important social, occupational, or recreational activities given up or reduced because of drinking.
A great deal of time spent in activities necessary to obtain, to use, or to recover from the effects of drinking.
Continued drinking despite knowledge of having a persistent or recurrent physical or psychological problem that is likely to be caused or exacerbated by drinking.

Why the rise?

“I think the increases are due to stress and despair and the use of alcohol as a coping mechanism,” said the study’s lead author, Bridget Grant, a researcher at the National Institutes of Health. The study notes that the increases in alcohol use disorder were “much greater among minorities than among white individuals,” likely reflecting widening social inequalities after the 2008 recession.

What Does Scientific Research Say About the Infamous Google Memo?

April 3, 2018August 11, 2017 by Walker

I’m sure most of you have heard about the controversial Google Memo making the rounds throughout the media. Social psychologists Sean Stevens and Jonathan Haidt provide an excellent source[ref]Thanks to Megan Conley for linking to this on Facebook.[/ref] for those interested in browsing the academic literature on the subject. They provide both supportive and critical responses[ref]Update: Add this to the critical pile.[/ref] to the memo as well as highlight findings within the research that both agree and disagree with the memo’s assertions. Overall, they conclude,

1. Gender differences in math/science ability, achievement, and performance are small or nil.* (See especially the studies by Hyde; see also this review paper by Spelke, 2005). The one exception to this statement seems to be spatial abilities, such as the ability to rotate 3-dimensional objects in one’s mind. This ability may be relevant in some areas of engineering, but it’s not clear why it would matter for coding. Thus, the large gender gap in coding (and in tech in general) cannot be explained as resulting to any substantial degree from differences in ability between men and women.

2. Gender differences in interest and enjoyment of math, coding, and highly “systemizing” activities are large. The difference on traits related to preferences for “people vs. things” is found consistently and is very large, with some effect sizes exceeding 1.0. (See especially the meta-analyses by Su and her colleagues, and also see this review paper by Ceci & Williams, 2015).

3. Culture and context matter, in complicated ways. Some gender differences have decreased over time as women have achieved greater equality, showing that these differences are responsive to changes in culture and environment. But the cross-national findings sometimes show “paradoxical” effects: progress toward gender equality in rights and opportunities sometimes leads to larger gender differences in some traits and career choices. Nonetheless, it seems that actions taken today by parents, teachers, politicians, and designers of tech products may increase the likelihood that girls will grow up to pursue careers in tech, and this is true whether or not biology plays a role in producing any particular population difference. (See this review paper by Eagly and Wood, 2013).

Check out the research for yourself.

Corruption: Another Driver of Migration

April 6, 2018August 10, 2017 by Walker

“One issue frequently raised by migrants whom I have met in transit (most recently in Agadez, Niger) is the absence of a level playing field in their home country,” reports Richard Danziger at the World Economic Forum. “Without knowing the right people, being from the right community or having the money to buy their way into a job, they will never get ahead. This, in turn, is part of the broader problem of poor governance, which has resulted in everything from a sharp drop in the quality of education and other services; to investment in sectors that lead to impressive GDP growth indicators, but result in little by way of job creation. While an increasing number of countries across Africa are holding free and fair elections, democratically elected governments have yet to prove themselves truly responsive to the needs of vast swathes of the population. In countries where there is already a tradition of migration, for many, voting with one’s feet can appear more effective than a trip to the ballot box.”

Danziger continues,

There has been relatively little research on the issue of poor governance as a driver of migration. A recently published study by GIZ specifically examining the link between corruption and migration and forced displacement is a rare example of research into this sensitive subject…The concept of poor governance is very broad and can cover a multitude of areas of mismanagement or criminal activity horizontally across sectors and vertically across levels of management. Indonesians for example talk of KKN: the Bahasa acronym for Corruption, Collusion and Nepotism. It might perhaps be more useful to explain what is meant by good governance and here we can refer to Sustainable Development Goal (SDG) 16: “Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels.” More specifically 16.5, 16.6 and 16.7; address corruption, transparency and accountability, and inclusiveness and participation.

As for the link to migration, the New York Declaration for Refugees and Migrants refers to the promotion of the rule of law and the need to address marginalization and exclusion. It also refers to the SDGs and the promotion of peaceful and inclusive societies, carefully sidestepping any mention of KKN or related abuses. Yet if we are to create a truly new and effective global migration framework there is a need for the Global Compact on Migration to include explicit reference to these issues.

I have an article currently under review at BYU Studies Quarterly on the subject of the Church and immigration. The more I read about the subject, the more I’m convinced I’m right.

What Motivates Support for Redistribution?

April 12, 2018August 9, 2017 by Walker

When it comes to the motivations behind redistribution, it turns out that fairness has little to do with it. Instead, researchers find[ref]Full access here.[/ref] that compassion, envy, and self-interest are the main drivers. From the abstract:

Why do people support economic redistribution? Hypotheses include inequity aversion, a moral sense that inequality is intrinsically unfair, and cultural explanations such as exposure to and assimilation of culturally transmitted ideologies. However, humans have been interacting with worse-off and better-off individuals over evolutionary time, and our motivational systems may have been naturally selected to navigate the opportunities and challenges posed by such recurrent interactions. We hypothesize that modern redistribution is perceived as an ancestral scene involving three notional players: the needy other, the better-off other, and the actor herself. We explore how three motivational systems—compassion, self-interest, and envy—guide responses to the needy other and the better-off other, and how they pattern responses to redistribution. Data from the United States, the United Kingdom, India, and Israel support this model. Endorsement of redistribution is independently predicted by dispositional compassion, dispositional envy, and the expectation of personal gain from redistribution. By contrast, a taste for fairness, in the sense of (i) universality in the application of laws and standards, or (ii) low variance in group-level payoffs, fails to predict attitudes about redistribution.

Let’s dive into the details:

We conducted 13 studies with 6,024 participants in four countries to test the hypothesis that compassion, envy, and self-interest jointly predict support for redistribution. Participants completed instruments measuring their (i) support for redistribution; (ii) dispositional compassion; (iii) dispositional envy; (iv) expected personal gain or loss from redistribution (our measure of self-interest); (v) political party identification; (vi) aid given personally to the poor; (vii) wealthy-harming preferences; (viii) endorsement of procedural fairness; (ix) endorsement of distributional fairness; (x) age; (xi) gender; and (xii) socioeconomic status (SES) (pg. 8422).

The results?:

To test this prediction, we regressed participants’ support for redistribution simultaneously on their dispositional compassion, their dispositional envy, and their expected personal gain (or loss) from redistribution. As predicted, the three motives have positive, significant, and independent effects on support for redistribution. This is true in the four countries tested: the United States (US) (study 1a), India (IN) (study 1b), the United Kingdom (GB) (study 1c), and Israel (IL) (study 1d)—standardized regression coefficients (β values): compassion, 0.28–0.39; envy, 0.10–0.14; self-interest, 0.18–0.30. Jointly, these motives account for 13–28% of the variance in support for redistribution. Adding to the regression models age and gender, or age, gender, and S[ocio]E[conomic]S[tatus], does not appreciably alter the effect of the emotion/motivation triplet, or the total variance accounted for. We note that age did not have significant effects in any country. Gender had significant effects in the United States and the United Kingdom (females more opposed to redistribution), but not in India or Israel. SES had a significant (negative) effect in the United Kingdom, but not in the other countries (Ibid.).

Unsurprisingly, in the U.S. “self-described Democrats endorsed redistribution to a greater extent than Republicans and Libertarians did. Democrats also reported more compassion and more expected personal gain from redistribution than Republicans and Libertarians did; envy did not differ by party” (Ibid.). Interestingly enough, “dispositional compassion was the only reliable predictor of giving aid to the poor” in all four countries. However, “support for government redistribution was not a unique predictor of personally aiding the poor in the regressions…Support for government redistribution is not aiding the needy writ large—in the United States, data from the General Social Survey indicate that support for redistribution is associated with lower charitable contributions to religious and nonreligious causes” (Ibid.).

Image result for compassion fullmetal alchemist — Compassion

Now consider these absurd answers from the survey respondents:

Participants in the United States, India, and the United Kingdom (studies 1a–c) were given two hypothetical scenarios and asked to indicate their preferred one. In one scenario, the wealthy pay an additional 10% in taxes, and the poor receive an additional sum of money. In the other scenario, the wealthy pay an additional 50% in taxes (i.e., a tax increment five times greater than in the first scenario), and the poor receive (only) one-half the additional amount that they receive in the first scenario. That is, higher taxes paid by the wealthy yielded relatively less money for the poor, and vice versa (63). To clarify the rationale for this trade-off, we told participants that the wealthy earned more when tax rates were low, thereby generating more tax revenue that could be used to help the poor. Fourteen percent to 18% of the American, Indian, and British participants indicated a preference for the scenario featuring a higher tax rate for the wealthy even though it produced less money to help the poor…We regressed this wealthy-harming preference simultaneously on support for redistribution, the emotion/motivation triplet, age, gender, and SES. Dispositional envy was the only reliable predictor (Ibid.).

In short, “Compassion and envy motivate the attainment of different ends. Compassion, but not envy, predicts personally helping the poor. Envy, but not compassion, predicts a desire to tax the wealthy even when that costs the poor” (Ibid.). The cries for fairness, though, have little to do with support for redistribution:

To sum up the set of fairness studies, in predicting support for redistribution, the effect of fairness as a group-wide concern is unreliable and of far smaller magnitude than the effect of the emotion/motivation triplet. This is true whether fairness is operationalized as uniformity in the application of laws and standards or as low (or null) variance in payoffs; whether distributional fairness is assayed between individuals (studies 2a, 2c, S1a, S2a, S2b) or between groups (“the rich,” “the poor”; studies 2b, S1b, S2c, S2d); and whether allocational decisions are hypothetical (studies 2a, 2b, S1a, S1b, S2c, S2d) or consequential (studies 2c, S2a, S2b) (pg. 8423).

So, why do people support redistribution?

They care about the poor and want to help them (Compassion).
They hate the rich (Envy).
They expect to gain from redistribution (Greed).

Sounds about right.

Consumption Inequality: 1960-2014

August 8, 2017 by Walker

Image result for consumption gif — Consumption

Economists Bruce D. Meyer and James X. Sullivan have a brand new NBER paper on inequality; specifically consumption inequality. From the abstract:

Official income inequality statistics indicate a sharp rise in inequality over the past five decades. These statistics do not accurately reflect inequality because income is poorly measured, particularly in the tails of the distribution, and current income differs from permanent income, failing to capture the consumption paid for through borrowing and dissaving and the consumption of durables such as houses and cars. We examine income inequality between 1963 and 2014 using the Current Population Survey and consumption inequality between 1960 and 2014 using the Consumer Expenditure Survey. We construct improved measures of consumption, focusing on its well-measured components that are reported at a high and stable rate relative to national accounts. While overall income inequality (as measured by the 90/10 ratio) rose over the past five decades, the rise in overall consumption inequality was small. The patterns for the two measures differ by decade, and they moved in opposite directions after 2006. Income inequality rose in both the top and bottom halves of the distribution, but increases in consumption inequality are only evident in the top half. The differences are also concentrated in single parent families and single individuals. Although changing demographics can account for some of the changes in consumption inequality, they account for little of the changes in income inequality. Consumption smoothing cannot explain the differences between income and consumption at the very bottom, but the declining quality of income data can. Asset price changes likely account for some of the differences between the measures in recent years for the top half of the distribution.

Meyer and Sullivan have been updating their data over the years. As the 2013 version (which measures inequality from 1960 to 2011) concludes,

Consumption inequality is less pronounced than income inequality and changes in consumption inequality differ considerably from changes in income inequality. While income inequality falls in the 1960s, consumption inequality rises slightly. Both consumption and income indicate rising inequality during the 1980s, but the rise is more noticeably for income. Since the mid-2000s, income inequality has risen while consumption inequality has fallen. Over the past three decades, both income and consumption inequality have risen, but the rise is much more noticeable for income (45 percent) than for consumption (19 percent). Differences between income and consumption are also evident for different parts of the distribution. Income inequality in the top half of the distribution rose steadily between 1980 and 2011, while consumption inequality for the top half of the distribution rose between 1980 and 2005, but then fell noticeably. Although changing demographics can account for some of the changes in consumption inequality, they do not account for changes in income inequality.

Comparisons of survey data to administrative records and national income accounts data indicate under-reporting of both income and consumption. There is evidence of considerable under-reporting of government transfers in income surveys, and the extent of under-reporting has grown overtime. Such under-reporting could lead to significant bias in the level and pattern of income inequality. There is also evidence of under-reporting of consumption data, although major components of consumption such as food at home and housing are reported at a high and stable rate relative to aggregate data. The differences between income and consumption inequality changes through 2005 are almost exclusively in the bottom half of the distribution, indicating that the under-reporting of consumption by the rich is not an explanation for the differences (pg. 21).

To quote Tyler Cowen, “This is one big reason why you can believe income inequality is high and/or rising, and not see it as the most significant normative issue.”

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