New Data: Pretax Income Growth

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.

A Trillion Ton Iceberg

Map of Larsen C, overlaid with NASA MODIS thermal image from July 12 2017, showing the iceberg has calved

If you have been following this blog you might know that we are talking about Larsen C. Project MIDAS reports: Larsen C calves trillion ton iceberg. What does this mean?

The iceberg weighs more than a trillion tonnes (1,000,000,000,000 metric tonnes), but it was already floating before it calved away so has no immediate impact on sea level. The calving of this iceberg leaves the Larsen C Ice Shelf reduced in area by more than 12%, and the landscape of the Antarctic Peninsula changed forever.

The article also notes:

Whilst this new iceberg will not immediately raise sea levels, if the shelf loses much more of its area, it could result in glaciers that flow off the land behind speeding up their passage towards the ocean. This non-floating ice would have an eventual impact on sea levels, but only at a very modest rate.

There is now some interesting modeling of where the iceberg will go and how long it will take to melt.  There should be a good differential equations project in here. Please read the full article, which includes a nice picture of the full break in the ice. Quiz question: What melting ice will have a significant impact on sea level?

Arctic Ice and Global Warming

An article from this past February, Rapid warming and disintegrating polar ice set the stage for ‘societal collapse’ – Carbon pollution is destabilizing both the Arctic and Antarctic, provides a nice overview of issues of warming and ice. For instance, there is the albedo feedback loop:

Climate models have long predicted that if we keep using the atmosphere as an open sewer for carbon pollution, the ice cap would eventually enter into a death spiral because of Arctic amplification — a vicious cycle where higher temperatures melt reflective white ice and snow, which is replaced by the dark land or blue sea, which both absorb more solar energy, leading to more melting.

The graph here is historical January Arctic ice extent and the data can be downloaded from the National Snow and Ice Data Center Sea Ice Data and Analysis Tools page.  Go to Sea ice analysis data spreadsheets and then to monthly data by year. As you’ll see there is other data there worth exploring. There are projects using Arctic ice data on both the calculus and statistics pages on this blog. If you are a real ice junkie take a look at the interactive sea ice graph and keep track of the current ice extent.  Finally, as a reader of this blog you know that you can make your own global temperature maps like the one in the article from reading April Second Warmest on Record.



Who eats more fast food the poor or wealthy?

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.

Fact Checking Coal Mining Jobs

The Washington Post’s Fact Checker article Pruitt’s claim that ‘almost 50,000 jobs’ have been gained in coal includes links to the Bureau of Labor Statistics data for each of their three screen shots of spreadsheets that back up their statements. The article notes:

In the last four months of the Obama administration, September to January, there was a gain of 1,400 jobs. In the first four months of the Trump administration, there has been a gain of 1,000 jobs.

While not part of the article, the graph here from the BLS is seasonally adjusted coal mining jobs since 1985.  Interestingly there was a drastic decline in coal mining jobs from 1985 until about 2000.

Worldwide LGBTQ Rights Scorecard with Data and Methodology

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.

Glacier Pairs

NASA’s Global Ice Viewer for Glaciers provides stunning pairs of images of glaciers taken many years apart at the same location. The viewer starts with a map of the world with links for seven locations. Each link brings you to glacier pair images from that location with information about the images. For example, here is a pair of images from Bear Glacier in the Alaskan Range. The top image was taken on July 20, 1909 and the second on Aug 5, 2005. Here is what the site says about glaciers:

Glaciers are sentinels of climate change. Ice that took centuries to develop can vanish in just a few years. A glacier doesn’t melt slowly and steadily like an ice cube on a table. Once glacial ice begins to break down, the interaction of meltwater with the glacier’s structure can cause increasingly fast melting and retreat.

Widespread loss of glaciers would likely alter climate patterns in complex ways. Glaciers have white surfaces that reflect the sun’s rays.  This helps keep our current climate mild. When glaciers melt, darker surfaces are exposed, which absorb heat.  This raises temperatures even more.

Employment Report for the Class of 2017

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 Feedback Loop: The Alaska Tundra

A recent NASA report Alaska tundra source of early-winter carbon emissions provides another example of a feedback loop. Global warming has slowed the refreezing of the Alaska tundra allowing for increased CO2 releases.

A new paper led by Roisin Commane, an atmospheric researcher at Harvard University in Cambridge, Massachusetts, finds the amount of carbon dioxide emitted from northern tundra areas between October and December each year has increased 70 percent since 1975.

“In the past, refreezing of soils may have taken a month or so, but with warmer temperatures in recent years, there are locations in Alaska where tundra soils now take more than three months to freeze completely,” said Commane. “We are seeing emissions of carbon dioxide from soils continue all the way through this early winter period.”

How much carbon is stored in the frozen soils. According to the report

The soils that encircle the high northern reaches of the Arctic (above 60 degrees North latitude) hold vast amounts of carbon in the form of undecayed organic matter from dead vegetation. This vast store, accumulated over thousands of years, contains enough carbon to double the current amount of carbon dioxide in Earth’s atmosphere.

April Second Warmest on Record

A NASA report notes that April 2017 was second-warmest April on record.

April 2017 was the second-warmest April in 137 years of modern record-keeping, according to a monthly analysis of global temperatures by scientists at NASA’s Goddard Institute for Space Studies (GISS) in New York.

Last month (meaning April) was 0.88 degrees Celsius warmer than the mean April temperature from 1951-1980. The two top April temperature anomalies have occurred during the past two years.

If you like the cool map here that was in the NASA report you can make your own for various time period at Global Maps from GHCN v3 Data. This is a great app and can be used for discussions about means vs distributions in stats or QL classes, for example. The article also has links to data sources.