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Tag Archives: QL

What are the symptoms of inequality?

The Guardian article, Trump’s ‘cruel’ measures pushing US inequality to dangerous level – UN warns by Ed Pilkington (6/1/18) lists some symptoms two of which are:

Americans now live shorter and sicker lives than citizens of other rich democracies;

The US incarceration rate remains the highest in the world;

The article lacks some data, which we provide here. It is true that incarceration rates in the U.S. are shockingly the highest in the world, see the chart here from the Prison Policy Initiative’s States of Incarceration: The Global Context 2018. But, this isn’t much different than in 2016 as compared to Prison Policy Initiative’s States of Incarceration: The Global Context 2016 report. Both PPI pages have links to the data sets at the bottom as well as other graphs.

As for U.S. life expectancy Our World in Data has an interactive life expectancy graph.  The last year for this graph is 2015, but by that time the U.S. already had a lower life expectancy than other wealthy countries. This graph allows us to choose other countries, has a map version, and a link to download the data.

In short, the symptoms of inequality stated in the Guardian article are not new (at least the two we focused on here), although they may be getting worse. The article is worth wording as well as the PPI report. Also explore the Our World in Data life expectancy graph. There is data and context to connect all this to stats or QL courses.

 

 

Do people in poverty work?

The EPI article, 50 years after the Poor People’s Campaign poverty persists because of a stingy safety net and a dysfunctional labor market by Elise Gould and Jessica Schieder (5/24/2018), answers the question with a graph (reposted here) and this:

The bottom bar shows us that, among those working-age individuals who are otherwise employable, 63 percent are working and 45.5 percent are working full time. An additional 37.2 percent are not working, but this share includes 1.6 million people living below the poverty line who are actively seeking a job. The data make it clear that millions of people who are active participants in the labor market are unable to make ends meet, either due to insufficient hours or low wages.

How old is Arctic sea ice?

From the NYT: In the Arctic, the Old Ice Is Disappearing

The NYT article In the Arctic, the Old Ice Is Disappearing by Jeremy White and kendra Pierre-Louis (5/14/2018) notes

In the Arctic Ocean, some ice stays frozen year-round, lasting for many years before melting. But this winter, the region hit a record low for ice older than five years.

In fact, in March of 1984 5+ year old ice made up about 70% of all ice and now it makes up only a few percent. There is also less ice overall.

If you really want to explore changes in the age of  Arctic ice go to the NSIDC Satellite Observations of Arctic Change interactive graph.  You can choose a year from 1985 through 2916, see a map of the ice, a bar chart of ice by month by age, and have the graph animate through the months of the year. The differences over the years is extreme. You can get related data from the EASE-Grid Sea Ice Age, Version 3 page, although you will have to register.

What is the Great Gatsby curve?

From The 9.9 Percent Is the New American Aristocracy: The class divide is already toxic, and is fast becoming unbridgeable – You’re probably part of the problem by Matthew Stewart (June 2018) in The Atlantic. (Figure 2)

The Great Gatsby curve represents the correlation between income inequality and intergenerational income elasticity. In short, the greater the income inequality in a country the greater the relationship between a child’s income and their parent’s income.

The Atlantic article, The 9.9 Percent Is the New American Aristocracy: The class divide is already toxic, and is fast becoming unbridgeable – You’re probably part of the problem by Matthew Stewart (June 2018)  is an excellent example of weaving important quantitative information (great for a QL course), including the Great Gatsby curve, to tell an important story.

Rising immobility and rising inequality aren’t like two pieces of driftwood that happen to have shown up on the beach at the same time, he noted. They wash up together on every shore. Across countries, the higher the inequality, the higher the IGE (see Figure 2). It’s as if human societies have a natural tendency to separate, and then, once the classes are far enough apart, to crystallize.

The post What is The Great Gatsby Curve? by David Vandivier (6/11/2013) has an animated gif that explains the curve well. To update or recreate the chart, you can get country gini values from the CIA World Factbook.  Intergenerational income elasticity can be found in figure 1 of a the paper Inequality from generation to generation:the United States in Comparison by Miles Corak (2012).  Intergenerational Social Mobility in OECD Countries January 2010 OECD Journal: Economic Studies 2010(1):6-6 Orsetta Causa  and Åsa Johansson is another source.  If you find more recent data let us know.

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What are the sources and uses of U.S. engergy?

Every year the EIA (U.S. Energy Information Agency) updates their energy flow and consumption diagrams. They are now available for 2017 energy use and the graph here is primary energy consumption by source and sector. For example, petroleum fulfills 37% or our energy use, 72% of petroleum is for transportation, and petroleum represents 92% of our transportation energy uses. Fossil fuels (petroleum, natural gas, and coal)  generated 78.1 quadrillion BTUs, which is 80% of our energy production, in 2017.  Links to this graph and the energy flow diagrams (total, petroleum, natural gas, coal, & electricity) are found at the bottom of the right side bar on the EIA Monthly Energy Review.

Past diagrams, dating back to 1996, are available at the Energy Flow Archives.  In 2016 fossil fuels generated 78.5 quadrillion BTUs, which was 81% of our energy production.  In 2008 (the first year this diagram appears) it was 83.4 quadrillion BTUs and 84%.

What is the state and future of snowpack out west?

Climate.gov has your answer with the article Winter so far has people out west asking, Where’s the snow?   (Feb 15, 2018) by Tom DiLiberto.

Farther south in Arizona, snows across the Rockies and in the Upper Colorado River Basin have been extremely low so far this year. Snow water equivalents—the amount of liquid water that would result  if  the snow melted in an instant—are between 0 and 30% of the median for this time of year for a broad region.  In fact, the “best” areas for snow this season lie along the Front Range in Colorado and are only just around normal.

Why does this matter?

For areas in the Upper Colorado River Water Basin along the southern Rockies which rely on snow melt for water resources later in the year, snow amounts this low bring fears. Particularly, is there going to be enough snowmelt to fill  Lakes Mead and Powell, which provide water to major cities like Tucson and Phoenix?

What is the cause? A second La Nina year in a row is part of the explanation, but (as their graph here shows)

As we continue to warm the planet due to emissions of greenhouse gases, mountain snowpack out west will likely continue to dwindle. Assuming we continue to increase global emissions of greenhouse gases (A2 scenario), the snow water equivalent of the snowpack in California by the end of the century will be 43% of what it was from 1971-2000. In Colorado, the snow water equivalent will be 26% less than that observed from 1971-2010.

A smaller and earlier-melting snowpack means less water to runoff into streams and tributaries in lower elevations. For places in the Sierra Nevada Mountains, Upper Colorado, and Upper Rio Grande River basins that rely heavily on a melting snowpack to provide the bulk of their annual runoff, climate change will have profound impacts on reservoir levels, water storage, and the people and ecosystems who rely on them.

There is enough quantitative information to use this article in a QL based course.

Is America’s nutritional divide due to food deserts?

In a recent article by Richard Florida, It’s Not the Food Deserts: It’s the Inequality, the case is made that food deserts aren’t the real problem.

Instead of within cities, the biggest geographic differences in the way Americans eat occur across regions. The map above plots the geography of healthy versus unhealthy eating across America’s 3,500-plus counties. Dark red indicates a lower health index based on grocery purchases, while light yellow represents a higher health index. While there is some variation within cities and metro areas, by far the biggest and most obvious differences are across broad regions of the country.

Ultimately, the fundamental difference in America’s food and nutrition has more to do with class than location. More than 90 percent of the difference in Americans’ nutritional inequality is the product of socioeconomic class, according to the study. And it’s not just that higher-income Americans have more money to spend on food. In fact, the cost of healthy food is not as prohibitively high as people tend to think. While healthy food costs a little bit more than unhealthy food, most of that is driven by the cost of fresh produce.

The article has useful graphs and summary statistics and can be used in  QL or statistics based course.

How has adult death rates changed by U.S. state?

The PRB (Population Reference Bureau) post, Declines in Adult Death Rates Lag in the U.S. South, answers the question with interactive graphs.

Adult death rates in many southern states are 30 percent or 40 percent higher than in states with the lowest death rates. The growing geographic disparity means that adults (ages 55+) in the worst-off southern states can expect to die three to four years earlier, on average, than their counterparts in states with the lowest death rates.

The graphs show death rates by state and state rankings for both females and males, from 1980 to 2015.  There is a clear trend.

In 2015, all of the states with the highest female death rates (ages 55+) were located in the South. In 1980, by comparison, the five states with the highest female death rates included Louisiana, New Jersey, New York, Ohio, and Pennsylvania.

The set of graphs are perfect for a QL course. The data, cited in the post, is from the CDC which could make for a regression based statistics project.

How does a small increase in average temperature increase the chance of extremes?

The Climate Central post, Small Change in Average -Big Change in Extremes, summarizes the idea well with the graph. As the mean shifts to the right, there is a significant increase in the chance of extreme temperature. The animated gif on the site is perfect in expressing the idea.

That’s what we are seeing across much of the country. Average summer temperature have risen a few degrees across the West and Southern Plains, leading to more days above 100°F in Austin, Dallas and El Paso all the way up to Oklahoma City, Salt Lake City, and Boise.  It’s worth noting that this trend has been recorded across the entire Northern Hemisphere, as shown in this WXshift animation.

You should check out the WXshift page they link to. This material is perfect for a stats course. It is also worth pointing out that the pictures here assumes the standard deviation stays the same, but there is evidence that it may be increasing. The effect is a flatter more stretched out density, with even greeter likelihood of extremes.

What do you know about the top 1%?

The Chicago Booth post, Never mind the 1 percent Let’s talk about the 0.01 percent, provides an insightful summary of income distribution at the top.

Mankiw noted that the 1 percent’s share of total income, excluding capital gains, rose from about 8 percent in 1973 to 17 percent in 2010, the latest figures available at the time. “Even more striking is the share earned by the top 0.01 percent. . . . This group’s share of total income rose from 0.5 percent in 1973 to 3.3 percent in 2010. These numbers are not easily ignored. Indeed, they in no small part motivated the Occupy movement, and they have led to calls from policymakers on the left to make the tax code more progressive.”

There is detailed exposition on who makes up the top and how they got there. For instance,

Technology, from the internet to media such as ESPN and Bloomberg terminals, has given elite athletes, entertainers, entrepreneurs, and financiers the ability to profit on a much larger, global scale, making the fruits of their labor more valuable than what previous superstars, such as, say, Pelé or Babe Ruth, brought in. Ruth’s peak salary of $80,000 would be worth about $1.1 million in 2016 dollars, around one-thirtieth of the $33 million the highest-paid Major League Baseball player, pitcher Clayton Kershaw of the Los Angeles Dodgers, made in salary alone in 2016.

And hedge-fund managers make multiples more than top athletes and entertainers. James Simons of Renaissance Technologies and Ray Dalio of Bridgewater Associates each made more than $1 billion in 2016, even though, as Institutional Investor’s Alpha reported, the top-25 hedge-fund earners took in the least as a group since 2005, largely because of the industry’s overall poor investment performance.

This is an excellent article about income and how it is distributed, with a number of graphs suitable for QL based courses.