A recent YouGov article, Is anyone ever “asking for it?” Americans seem to think so, provides the pie chart to the left. According to the data, 40% of adults believe that a women wearing revealing clothing is fully or somewhat responsible for unwanted sexual advances. Along with that, another 17% prefer not to say and 6% don’t know. Maybe a better way of reporting the results is that only 36% of adults say that the person is not at all or not very responsible. There is other data in the article as well as a link to the full survey results. This data that is sure to generate a conversation in stats or QL course.
Kevin Drum’s post, The Real Story Behind All Those Confederate Statues, provides the associated chart about the timing of confederate monument and statue building.
This illustrates something that even a lot of liberals don’t always get. Most of these monuments were not erected after the Civil War. In fact, all the way to 1890 there were very few statues or monuments dedicated to Confederate leaders. Most of them were built much later.
This is an excellent example of how data and a good graphic helps tell an important story.
Yes, these monuments were put up to honor Confederate leaders. But the timing of the monument building makes it pretty clear what the real motivation was: to physically symbolize white terror against blacks. They were mostly built during times when Southern whites were engaged in vicious campaigns of subjugation against blacks, and during those campaigns the message sent by a statue of Robert E. Lee in front of a courthouse was loud and clear.
Drum’s post, worth a quick read, links to the Southern Poverty Law Center report that contains this and other data and excellent graphics for a QL course. It is worth recalling the first statement of sustainability on our Defining Sustainability Page: The current state of people is not a morally acceptable endpoint of societal development.
The EPI has detailed report on CEO pay, CEO pay remains high relative to the pay of typical workers and high-wage earners. The article includes data, such as the ratio of CEO-to-worker pay that was used to create the graph here. Although the ratio has decreased since its peak of 347.5 in 2007, it was still a healthy 270.5 in 2016, which is over 10 times the 20 it was in 1965. From the report:
From 1978 to 2016, inflation-adjusted compensation, based on realized stock options, of the top CEOs increased 937 percent, a rise more than 70 percent greater than stock market growth and substantially greater than the painfully slow 11.2 percent growth in a typical worker’s annual compensation over the same period. CEO compensation, when measured using the value of stock options granted, grew more slowly from 1978 to 2016, rising 807 percent—a still-substantial increase relative to every benchmark available.
Over the last three decades, compensation, using realized stock options, for CEOs grew far faster than that of other highly paid workers, i.e., those earning more than 99.9 percent of wage earners. CEO compensation in 2015 (the latest year for data on top wage earners) was 5.33 times greater than wages of the top 0.1 percent of wage earners, a ratio 2.15 points higher than the 3.18 ratio that prevailed over the 1947–1979 period. This wage gain alone is equivalent to the wages of more than two very-high-wage earners.
As noted, the report which is worth reading, has data that can be used in the classroom and ample quantitative information for QL based classes.
How much has pretax income grown by earner percentiles? The graph here, from Chicago Booth Review’s article New Data: Inequality Runs Deeper than Previously Thought, provides the answer.
So Piketty, Saez, and Gabriel Zucman of University of California at Berkeley combined tax, survey, and national-accounts data to create distributional accounts that they say capture 100 percent of US income since 1913. The new accounts include transfer payments, employee fringe benefits, and capital income, which weren’t in previous data.
The data set reveals since 1980 a “sharp divergence in the growth experienced by the bottom 50 percent versus the rest of the economy,” the researchers write. The average pretax income of the bottom 50 percent of US adults has stagnated since 1980, while the share of income of US adults in the bottom half of the distribution collapsed from 20 percent in 1980 to 12 percent in 2014. In a mirror-image move, the top 1 percent commanded 12 percent of income in 1980 but 20 percent in 2014. The top 1 percent of US adults now earns on average 81 times more than the bottom 50 percent of adults; in 1981, they earned 27 times what the lower half earned.
If you click on the top right of the graph in the article and go to edit chart you can get a table of the data used for the chart. Great for use in a QL or stats course. Of course Piketty and Saez are know for creating the World Wealth and Income Database, which we have highlighted on this blog before.
Data helps us understand the world as it really is as opposed to what we think is true. The article Do poor people eat more junk food than wealthier American? uses the Bureau of Labor Statistics longitudinal data, accessible in the article, to answer the question.
Because it’s considered relatively inexpensive, there’s an assumption that poor people eat more fast food than other socioeconomic groups – which has convinced some local governments to try to limit their access.
Read the article to learn more and take advantage of the data sources for statistics or QL courses.
Most countries score an F on our LGBT human rights report card is a recent article in The Conversation.
Our research gives most countries in the world a failing grade in LGBTQ rights, reflecting widespread persecution of sexual minorities. Only one country in 10 actively protects the human rights of sexual minorities.
The article includes three charts with data that can be used in a statistics or QL based course. There is also a link to the related paper, Human Rights and the Global Barometer of Gay Rights (GBGR): A Multi-Year Analysis, that describes the methodology used in the rankings.
EPI released a must read report early this month titled the Class of 2017. This is a long report with 17 graphs of historical trends, with data, related to employment of recent college grads. For example, figure F provides unemployment rates for young college graduates by race and ethnicity (Black, Hispanic, and White). The graph provides historical trends and notes that young graduates of color have higher unemployment rates. Other highlights from the report:
The overall unemployment rates and idling rates of young graduates mask substantial racial and ethnic disparities in these measures.
Young graduates are burdened by substantial student loan balances.
The wage gap between male and female young high school graduates has narrowed since 2000, while the wage gap between male and female young college graduates has widened.
Wages have stagnated—or fallen—for most young graduates since 2000.
There is an abundance of information and data in this report that can be used in math or QL based courses.
A recent EPI report notes: Straight out of college, women make about $3 less per hour than men.
Right out of college, young men are paid more than their women peers—which is surprising given that these recent graduates have the same amount of education and a limited amount of time to gain differential experience.
What may be worth exploring is the historic difference in starting pay between women and men, which you can do since the data is available and can easily be placed into Excel.
If you are looking for data on wealth and income inequality visit the World Wealth and Income Database. You can create graphs and download the data. For example, the graph here is pre-tax share of income for the top 1% (20.2% in 2014) and bottom 50% (12.6% in 2014) of adults in the U.S. The trends since 1980 are roughly linear and so the data, which you can download in a number of formats, can be used for regression. Once you have the lines, they can be used in other places in the curriculum. Other categories exist including wealth instead of income and groups such as the top 10% or middle 40%.
Stats classes are always looking for interesting data. One place to look in YouGov. For example, they did a poll (Note: it is not clear how the sample was obtained but they do provide a sample size.) asking people if life is fair. Here are the results by gender.
- Do you think life is fair or not fair?
|Life is fair||38||46||31|
|Life is not fair||46||40||51|
You are set for a statistical test comparing Male vs Female perception of life being fair or not. This now allows for a discussion of why women would respond differently than men. One extra bonus on the site is you can look at the same questions broken down by other categories including income. Go to the YouGov Results page to see the data they have.