Wednesday, January 28, 2015

Majorities in France, Germany, and Britain Hold Favorable Views of Muslims

According to a 2014 Pew Research Center survey, Muslims are viewed favorably by 72 percent of the French public, 58 percent of Germans, and 64 percent of the British public.  France, Germany and the UK have the largest Muslim populations among European Union member countries, with 4.7, 4.8, and 3 million Muslims respectively.

Other European countries, especially in southern Europe, have more mixed views of Muslims.  Negative views are highest in Italy, where only 28 percent of respondents said they have favorable views of Muslims.


In each of the seven countries surveyed by Pew, public opinion regarding Muslims was linked to ideology and age.  Respondents on the political right were more likely to give Muslims in their country an unfavorable rating, as were older respondents (age 50 and over).

The Muslim share of Europe's total population has grown from four percent in 1990, to 6 percent in 2010, and is projected to reach eight percent in 2030.

Read more:
http://www.pewresearch.org/fact-tank/2015/01/15/5-facts-about-the-muslim-population-in-europe/#more-266325
http://www.pewglobal.org/2014/05/12/chapter-4-views-of-roma-muslims-jews/#mixed-views-of-muslim-minorities

Tuesday, January 27, 2015

Marijuana Fast Becoming Legitimate Business

A new interactive feature by The Economist maps the progress of the decriminalization of cannabis in the United States.  Four states and the District of Columbia now allow recreational use of marijuana, and 21 additional states make it available for medicinal use.  The percentage of the public in favor of legalization has grown from 12 percent in 1969 to 52 percent in 2014.


Other figures highlighted by The Economist:

  • Colorado (the first state to sell recreational marijuana in January 2014) sold around $600 million-worth of recreational and medical weed over the first 11 months, raising $68 million in taxes for the state. 
  • But thanks largely to taxes and restrictive licensing rules, it costs about 50 percent more to get high legally in Colorado than it does from buying cannabis off the street (though legal weed will probably be stronger). 
  • Americans spend an estimated $40 billion getting high each year, about 20% of what they spend on cigarettes and alcohol. 
  • California is likely to legalize cannabis in 2016; federal legalisation may be five to ten years away. 






Read more:
http://www.economist.com/blogs/graphicdetail/2015/01/daily-chart-11

TeachingwithData.org resources:
Respondants' perception of the Nation's progress in coping with illegal drugs (http://www.teachingwithdata.org/resource/2926)
Characteristics of Teen Substance Users: A Data-Driven Learning Guide (http://www.teachingwithdata.org/resource/3435)

Monday, January 26, 2015

Deepening Global Inequality Or Dodgy Statistics?

A recent report by the anti-poverty advocacy organization Oxfam has made headlines around the world.  The report, "Wealth: Having It All and Wanting More," shows that global wealth is increasingly concentrated in the hands of a small wealthy elite.  According to Oxfam, the wealthiest one percent will soon own more than the rest of the world’s population: the share of the world's wealth owned by the richest one percent increased from 44 percent in 2009 to 48 percent in 2014.  Oxfam expects the wealthiest one percent to own more than 50% of the world’s wealth by 2016.

The Oxfam report is based on data from Credit Suisse's annual Global Wealth Databook, and the Forbes billionaire list.


Some commentators have pointed out that Oxfam's methodology is flawed however, and their results potentially misleading.  Felix Salmon, for example, has repeatedly taken Oxfam to task for their methodology.  One issue, Salmon explained, is that it is statistically unwise to take a chart like the one provided by Credit Suisse (above) and extrapolate from it as Oxfam did (see chart below): "The lines on the Oxfam charts are thin, which gives the impression that the numbers are precise. But of course the numbers are anything but precise: the error bars on all these data points are huge, which means that the variation over the years could easily just be statistical noise." (For an illustration of statistical noise, see our recent post, "The Difference That Statistical Noise Makes: US Job Reports": http://teachingwithdata.blogspot.com/2015/01/the-difference-that-statistical-noise.html)


Also problematic, Salmon argues, is Oxfam's reliance on Credit Suisse's method of adding up wealth:
"If you look at the tables in the Credit Suisse databook, China has zero people in the bottom 10% of the world population: everybody in China is in the top 90% of global wealth, and the vast majority of Chinese are in the top half of global wealth. India is on the list, though: if you're looking for the poorest 10% of the world’s population, you'll find 16.4% of them in India, and another 4.4% in Bangladesh. Pakistan has 2.6% of the world’s bottom 10%, while Nigeria has 3.9%. 
But there’s one unlikely country which has a whopping 7.5% of the poorest of the poor — second only to India. That country? The United States. 
How is it that the US can have 7.5% of the bottom decile, when it has only 0.21% of the second decile and 0.16% of the third? The answer: we’re talking about net worth, here: assets minus debts. And if you add up the net worth of the world’s bottom decile, it comes to minus a trillion dollars. The poorest people in the world, using the Credit Suisse methodology, aren't in India or Pakistan or Bangladesh: they're people like Jérôme Kerviel, who has a negative net worth of something in the region of $6 billion."


He continues: "The result is that if you take the bottom 30% of the world's population — the poorest 2 billion people in the world — their total aggregate net worth is not low, it's not zero, it's negative. To the tune of roughly half a trillion dollars. My niece, who just got her first 50 cents in pocket money, has more money than the poorest 2 billion people in the world combined."

These criticisms are not meant to imply that there isn't an enormous amount of wealth inequality in the world.  However there are two important takeaways:

  1. "It's very easy, and rather misleading, to construct any statistic along the lines of 'the top X people have the same amount of wealth as the bottom Y people'."
  2. "When you're talking about poor people, aggregating wealth is a silly and ultimately pointless exercise. Some poor people have modest savings; some poor people are deeply in debt; some poor people have nothing at all. (Also, some rich people are deeply in debt, which helps to throw off the statistics.) By lumping them all together and aggregating all those positive and negative ledger balances, you arrive at a number which is inevitably going to be low, but which is also largely meaningless [...] Wealth, and net worth, are useful metrics when you're talking about the rich. But they tend to conceal more than they reveal when you're talking about the poor."


Read more:
http://www.huffingtonpost.com/2015/01/19/world-wealth-oxfam_n_6499798.html
http://www.oxfam.org/en/research/wealth-having-it-all-and-wanting-more
http://fusion.net/story/39185/oxfams-misleading-wealth-statistics/

TeachingwithData.org resources:
The Difference That Statistical Noise Makes: US Job Reports (http://teachingwithdata.blogspot.com/2015/01/the-difference-that-statistical-noise.html)
Wealth Inequality in America (http://www.teachingwithdata.org/resource/3922
Wealth and Health of Nations (http://www.teachingwithdata.org/resource/2984)

Tuesday, January 20, 2015

Most American Communities Remain Intensely Segregated, Even As The Country Becomes More Diverse

Through a series of maps, Eric Fisher has painted a sobering picture of residential segregation in more than one hundred US metropolitan areas.  The maps are based on population data from the 2010 US Census Bureau reports.  Each dot represents 25 residents, color-coded by race: red is White, blue is Black, green is Asian, orange is Hispanic, yellow is Other.

Fisher credits Bill Rankin's map of Chicago: http://www.radicalcartography.net/index.html?chicagodots as the inspiration for his project.

Chicago, IL

Detroit, MI

Los Angeles, CA

Miami, FL

St. Louis, MO


As noted in Vox, "[w]hat's really striking about these maps [...] is that even though the color patterns change — some cities have many more Asians or Latinos than St. Louis — the basic impression of highly segregated lives does not."

You can see all of Fisher's maps by browsing through his flickr album "Race and Ethnicity 2010": https://www.flickr.com/photos/walkingsf/sets/72157626354149574/

Read more:
https://www.flickr.com/photos/walkingsf/sets/72157626354149574
http://www.radicalcartography.net/index.html?chicagodots
http://www.vox.com/2015/1/20/7547159/real-state-of-the-union-maps-and-charts

TeachingwithData.org resources:
Parable of the Polygons (http://www.teachingwithdata.org/resource/3932)
White/Black Racial Segregation in U.S. Cities (http://www.teachingwithdata.org/resource/3163)
Investigating Exploring Race and Ethnicity Using Census 2000 Data (http://www.teachingwithdata.org/resource/3176)

Monday, January 19, 2015

America In Black And White

As we celebrate Martin Luther King Day, the Pew Research Center paints a sobering picture of racial disparities in the United States.  Five decades after Dr. King's address at the March on Washington, substantial social, economic, educational barriers to racial equality subsist.  These inequalities are reflected in Whites' and Blacks' perceptions.


Compared to White Americans, Black Americans are more likely to be dissatisfied with the way things are going in the country, more likely to say that Blacks are treated less fairly than Whites in most social arenas, and more likely to say that much more needs to be done to achieve racial equality.  They are also less optimistic about the state of race relations.

Read more:
http://www.pewresearch.org/fact-tank/

TeachingwithData.org resources:
White/Black Racial Segregation in U.S. Cities (http://www.teachingwithdata.org/resource/3163)
Race and Poverty in the United States (http://www.teachingwithdata.org/resource/3169
Exploring Race and Ethnicity Using Census 2000 Data (http://www.teachingwithdata.org/resource/3176
Race and Ethnic Inequality (http://www.teachingwithdata.org/resource/3101)
Gender, Education, Family, Poverty, and Race (http://www.teachingwithdata.org/resource/3128)
Race in America: Tracking 50 Years of Demographic Trends (http://www.teachingwithdata.org/resource/3863)
Attitudes about Racial Discrimination and Racial Inequality in the US: A Data-Driven Learning Guide (http://www.teachingwithdata.org/resource/3431)

ICPSR Student Paper Competition--Submission Deadline is January 31!



For details, please go to: http://www.icpsr.umich.edu/icpsrweb/ICPSR/support/announcements/2015/01/icpsrs-2015-student-research-paper

Wednesday, January 14, 2015

The Difference That Statistical Noise Makes: US Job Reports

Do your students struggle to understand the meaning of a margin of error in polls?  The New York Times' The Upshot created a helpful illustration, using US job reports as an example.  Every month,  the Bureau of Labor Statistics, which is part of the Labor Department, conducts a large survey of 144,000 employers in order to assess how many jobs there are in the United States.  This job report is an important tool to evaluate the health of the economy.

And yet, as this Upshot article explains, one should not put too much stock in these figures:
"Even with all those survey participants, there is sampling error; the employers responding to the survey might be different from the nation's employers as a whole. And the Labor Department's initial release, coming as it does so soon after the survey, is released before all the data is in, with researchers filling in the gaps with statistical estimates that might prove wrong. Only in later weeks and months is all the data available, and the bureau then revises its initial numbers."
In addition,
"The sampling error becomes magnified because those of us following the jobs report don't focus on the total number of jobs in the economy (more than 130 million). We focus on the relatively small change in the number of these jobs from month to month (typically a few hundred thousand, at most)."
To illustrate how statistical noise might affect our perception of job trends, the Upshot created a computer simulation that assumes that "the economy is adding exactly 150,000 jobs every month, and that the monthly estimates of job growth come with exactly as much statistical noise as the Labor Department says its estimates have."

In this scenario job growth is steady, which should look like the chart below on the left--a straight line.  However, sampling error introduces statistical noise that can make the chart look like the chart below on the right.


A second scenario posited accelerating job growth (true job growth starts at 150,000 jobs in January and accelerates by 15,000 a month through December).  As the chart below demonstrates, statistical noise can make it difficult to make out this pattern in the data.  "Month by month, you wouldn't have any way of knowing if it was a true acceleration, or just a false signal generated by sampling error."



Read more:
http://www.nytimes.com/2014/05/02/upshot/how-not-to-be-misled-by-the-jobs-report.html?abt=0002&abg=1
http://www.bls.gov/

TeachingwithData.org resources:
Dancing statistics: explaining the statistical concept of sampling & standard error through dance (http://www.teachingwithdata.org/resource/3910)
Bureau of Labor Statistics: Databases, Tables & Calculators by Subject (http://www.teachingwithdata.org/resource/3886)
Economy Track: Employment to Population Ratio (http://www.teachingwithdata.org/resource/2936)