The answer to the question depends on how it is measured. The post in statista, The Countries Spending the Most on Education by Martin Armstrong (9/12/2018) reports spending as a share of gross domestic product for primary, second and post-secondary non-tertiary education as well as tertiary education. By this measure Norway spends the most. But, if the measure used is expenditure per student as a share of GDP per capita, the high spender is (south) Korea (Norway is fifth). Our graph here is a scatter plot of the two measures by country.
The data is from OECD.Stat. Go to Education and Training, Education at a Glance, Financial resources invested in education, Education finance indicators, and finally Expenditure per student as share of GDP per capita. Under indicator at the top of the spreadsheet the measure can be changed. Definitions of measures can be found in the OECD Handbook for Internationally Comparative Education Statistics (page 99).
The path of Florence has been extremely unusual. As Philip Klotzbach, an Atlantic hurricane expert, tweeted on Friday, “33 named storms (since 1851) have been within 100 miles of Florence’s current position. None of these storms made US landfall. The closest approach was Hurricane George (1950) — the highlighted track [in white].”
Florence, tragically, has made a beeline toward the Carolinas. And it clearly was steered away from the historical (or “climatological”) path by a major high-pressure system blocking its typical path — north and away from land.
The Our World in Data article Plastic Pollution by Hannah Ritchie and Max Roser (Sept 2018) is a detailed summary of plastics with 20 charts. For example, one of the charts is a time series of plastic production (downloaded and posted here) showing that, in 2015, the world produced 381 million tons of plastic. In the same year, only 20% of the plastic was recycled (second chart in the article). There is information on plastic waste generation.
Packaging, for example, has a very short ‘in-use’ lifetime (typically around 6 months or less). This is in contrast to building and construction, where plastic use has a mean lifetime of 35 years.7 Packaging is therefore the dominant generator of plastic waste, responsible for almost half of the global total.
Who produces the most plastic waste?
… we see the per capita rate of plastic waste generation, measured in kilograms per person per day. Here we see differences of around an order of magnitude: daily per capita plastic waste across the highest countries – Kuwait, Guyana, Germany, Netherlands, Ireland, the United States – is more than ten times higher than across many countries such as India, Tanzania, Mozambique and Bangladesh.
As always with Our World in Data, the data associated with each graph is downloadable.
The New York Times interactive article How Much Hotter Is Your Hometown Than When You Were Born? By Nadja Popovich, Blacki Migliozzi, Rumsey Taylor, Josh Williams and Derek Watkins, allows the reader to input a birth year and hometown and provides a graph with historical 90+ degree days and predictions for the future. For example a person born in 1970 in NYC would get the graph copied here. In 1970 the expectation was six 90+ degree days, today it is 11, and by 2050 it will be 24 with a likely range of 15 to 30.
THE NEW YORK AREA is likely to feel this extra heat even if countries take action to lower their emissions by the end of the century, according to an analysis conducted for The New York Times by the Climate Impact Lab, a group of climate scientists, economists and data analysts from the Rhodium Group, the University of Chicago, Rutgers University and the University of California, Berkeley. If countries continue emitting at historically high rates, the future could look even hotter.
The future projection shown here assumes countries will curb greenhouse gas emissions roughly in line with the world’s original Paris Agreement pledges (although most countries do not appear on track to meet those pledges).
There are related human health impacts:
Worldwide, high temperatures have been found to increase the risk of illness and death, especially among older people, infants and people with chronic medical conditions. Lower-income populations, which more often lack access to air conditioning and other adaptive technologies, are also more likely to suffer the impacts of extreme heat. In America, so are people of color.
The article has other graphs and quantitative information which can be used in QL based course.
And 51% of teens say they often or sometimes find their parent or caregiver to be distracted by their own cellphone when they are trying to have a conversation with them.
As they look at their own lives and those of their peers, most teens see things that worry them. Roughly nine-in-ten teens view spending too much time online as a problem facing people their age, including 60% who say it is a major problem.
The article has eight charts, one of which is reposted here. There is a complete methodology section, which is perfect for a stats class, with enough information to use the data for statistical tests.
The Guardian article Arctic’s strongest sea ice breaks up for first time on record by Jonathan Watts (8/21/18) includes an animated graph of Arctic sea ice volume by year. We produce a similar graph using monthly average ice volume from PIOMAS (source cited for the data in the article). The graph clearly displays the change of ice throughout the year and the loss of ice throughout the years.
Freakish Arctic temperatures have alarmed climate scientists since the beginning of the year. During the sunless winter, a heatwave raised concerns that the polar vortex may be eroding.
This includes the Gulf Stream, which is at its weakest level in 1,600 years due to melting Greenland ice and ocean warming. With lower circulation of water and air, weather systems tend to linger longer.
A dormant hot front has been blamed for record temperatures in Lapland and forest fires in Siberia, much of Scandinavia and elsewhere in the Arctic circle.
The data from PIOMA includes monthly and daily ice volumes. The R script and csv file that produced the graph here can be downloaded.
On average, in 2017, black women workers were paid only 66 cents on the dollar relative to non-Hispanic white men, even after controlling for education, years of experience, and geographic location. A previous blog post dispels many of the myths behind why this pay gap exists, including the idea that the gap would be closed by black women getting more education or choosing higher paying jobs. In fact, black women earn less than white men at every level of education and even when they work in the same occupation. But even if changing jobs were an effective way to close the pay gap black women face—and it isn’t—more than half would need to change jobs in order to achieve occupational equity.
Along with the graph copied here, there is a time series from 2000 to 2016 of the Duncan Segregation Index:
the “Duncan Segregation Index” (DSI) for black women and white men, overall and by education, based on individual occupation data from the American Community Survey (ACS). This is a common measure of occupational segregation, which, in this case identifies what percentage of working black women (or white men) would need to change jobs in order for black women and white men to be fully integrated across occupations.
The current overall health workforce is mostly composed of women. Nonetheless, female health workers remain underrepresented in highly skilled occupations, such as in surgery. As of 2015, just under half of all doctors are women across OECD countries on average. The variation across countries is significant: in Japan and Korea only around 20% of doctors are women, in Latvia and Estonia this proportion is over 70%.
It is worth noting that the U.S. is well below the OECD average with only 34.1% of its doctors female in 2015, although the current posted data set has the U.S. at 35.06% for 2015 (35.52% for 2016).
Time series data for OECD countries is available at the OECD.stat Health Care Resources page. Data for the U.S. dates back to 1993 (19.59%) through 2016. For this specific data set click physicians by age and gender on the left side bar. Within the chart click variable, measure, and year, to change the scope of the data in the spreadsheet. The data can be downloaded in multiple formats.
In 2015, total recoverable water in storage in the aquifer was about 2.91 billion acre-feet, which is an overall decline of about 273.2 million acre-feet, or 9 percent, since predevelopment. Average area-weighted water-level change in the aquifer was a decline of 15.8 feet from predevelopment to 2015 and a decline of 0.6 feet from 2013 to 2015.
A little geography:
The High Plains aquifer, also known as the Ogallala aquifer, underlies about 112 million acres, or 175,000 square miles, in parts of eight states, including: Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas and Wyoming. The USGS, at the request of the U.S. Congress and in cooperation with numerous state, local, and federal entities, has published reports on water-level changes in the High Plains aquifer since 1988 in response to substantial water-level declines in large areas of the aquifer.
In my view, Plains farmers cannot afford to continue pushing land and water resources beyond their limits – especially in light of climate change’s cumulative impact on the Central Plains. For example, a recent study posits that as droughts bake the land, lack of moisture in the soil actually spikes temperatures. And as the air heats up, it further desiccates the soil.
This vicious cycle will accelerate the rate of depletion. And once the Ogallala is emptied, it could take 6,000 years to recharge naturally. In the words of Brent Rogers, a director of Kansas Groundwater Management District 4, there are “too many straws in too small of a cup.”
The USGS post and the Conversation article are useful for a QL based course. The full USGS report has links to water-level data sources starting on page 7.
The Our World in Data post, Why do women live longer than men? by Esteban Ortiz-Ospina and Diana Beltekian (8/14/18) answers the question with the graph copied here.
As the next chart shows, in most countries for all the primary causes of death the mortality rates are higher for men. More detailed data shows that this is true at all ages; yet paradoxically, while women have lower mortality rates throughout their life, they also often have higher rates of physical illness, more disability days, more doctor visits, and hospital stays than men do. It seems women do not live longer than men only because they age more slowly, but also because they are more robust when they get sick at any age. This is an interesting point that still needs more research.
Interestingly, it seems that except for Bhutan it is only countries in Africa where women are more likely to die of a major disease. The article is an excellent example of telling a story with data while also posing questions.
The evidence shows that differences in chromosomes and hormones between men and women affect longevity. For example, males tend to have more fat surrounding the organs (they have more ‘visceral fat’) whereas women tend to have more fat sitting directly under the skin (‘subcutaneous fat’). This difference is determined both by estrogen and the presence of the second X chromosome in females; and it matters for longevity because fat surrounding the organs predicts cardiovascular disease.
But biological differences can only be part of the story – otherwise we’d not see such large differences across countries and over time. What else could be going on?
The article has three other graphs beyond this one. One compares life expectancy by country for women and men, one for life expectancy for men and women in the U.S. (and three other countries that can be selected) since 1790, and one for the difference in life expectancy at age 45 since 1790 for selected countries. All graph can be downloaded and the data is available for each.
The answer to the question depends on how it is measured. The post in statista, The Countries Spending the Most on Education by Martin Armstrong (9/12/2018) reports spending as a share of gross domestic product for primary, second and post-secondary non-tertiary education as well as tertiary education. By this measure Norway spends the most. But, if the measure used is expenditure per student as a share of GDP per capita, the high spender is (south) Korea (Norway is fifth). Our graph here is a scatter plot of the two measures by country.
