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Tag Archives: inequality

How has the economic situation of college students changed?

The Pew article A Rising Share of Undergraduates Are From Poor Families, Especially at Less Selective Colleges, by Richard Fry and Anthony Cilluffo (5/22/19) summarizes the change in the economic background of students from 1996 to 2016.

As of the 2015-16 academic year (the most recent data available), about 20 million students were enrolled in undergraduate education, up from 16.7 million in 1995-96.1 Of those enrolled in 2015-16, 47% were nonwhite and 31% were in poverty, up from 29% and 21%, respectively, 20 years earlier.2

The rising proportion of undergraduates in poverty does not mirror wider trends in society. The official poverty rate for adults age 18 to 64 (12%) was similar in 1996 and 2016, suggesting that access to college for students from lower-income backgrounds has increased since 1996.

As the graph copied here shows:

The growth in the share of dependent students from families in poverty has been uneven across postsecondary education. Their growing presence has been most dramatic among less selective institutions.

The article has a eight charts, a methodology section, and links to the data sources.

Are there correlations between one or more deceased parents and race, gender, or socio-economic status?

The Census Bureau report Parental Mortality is Linked to a Variety of Socio-economic and Demographic Factors by Zachary Scherer (5/6/19) provides charts of deceased parent(s) by sex, race (chart copied here), and socio-economic status.

For example, among those ages 45 to 49, 26% have lost their mother, while 45% have lost their father. Along these same lines, 7 in 10 of those ages 60 to 64 have a deceased mother, while about 87% have lost their father.

For example, among those ages 35 to 44, 43% of those living below the FPL have lost one or both parents, compared to 28% for those living in households with an income-to-poverty ratio of at least 400% of the FPL.

Parental loss, which varies by race and socio-economic status, is often accompanied by psychological and material consequences. These statistics demonstrate the way these new SIPP data can help assess how socio-economic and demographic characteristics are associated with parental mortality in the United States.

There are two other charts and a link to the SIPP data source.

 

Why is Black maternal mortality higher?

Kevin Drum provides an excellent example of quantitative reasoning in his (5/6/19) post How Can We Reduce Black Maternal Mortality? The story begins with his chart here that shows maternal mortality increasing in general, but it has increased faster and is much higher for Black mothers as compared to White. Drum begins by addressing the toxic stress hypothesis, in other words, the differences are do to the stress caused by societal and systemic racism which leads to physiological issues.  But,

One reason for this is the “Hispanic paradox”: Hispanics certainly encounter systemic racism too, but the maternal mortality rate for Hispanic mothers is about the same as for white mothers.

The article has a graph of “allostatic load” which looks to quantify long-term stress.

The differences in allostatic load are tiny—about the equivalent of one IQ point on an intelligence test—and Hispanics have a higher allostatic load than either blacks or whites but the lowest maternal mortality rate.

Another chart looks at self-reported stress by race for poor individuals, but

Poor blacks report less stress and higher levels of optimism than both poor whites and poor Hispanics. Put all this together and the toxic stress/weathering hypotheses look shaky. The racial differences are modest and don’t seem to correlate well with maternal mortality anyway. The problem is that every other hypothesis seems wrong too. Researchers have looked at poverty, education, drinking, smoking, and genetic causes. None of them appear to be the answer.

There are two more charts as part of Drum’s article. The article is worth reading, he cites his data, and is it perfect for a QL based course. His general conclusion at this point:

This is shocking: we still have almost no idea of what’s going on even though this has been a well-known problem for more than two decades.

 

 

 

How do we humanize data?

When comparing countries or even within countries, we can talk about GDP, Gini coefficients, poverty rates, etc., but sometimes (all the time?) it is hard to know what this really means for people. If a picture is worth a 1000 words then Dollar Street has 30,000,000 words as they “visited 264 families in 50 countries and collected 30,000 photos” resulting in an excellent example of humanizing data.

The pictures here represent the bathroom for a family in India earning $4621 per month and for a family in Egypt earning $775 per month (which is which?).  Dollar Street has large sets  of pictures like this from around the work for over 100 different categories such as armchairs, beds, computers, earrings,  floors, hands, ovens, and plates.  Difference can be explored by picture within countries or the world. These sets of pictures provide both a sense of what poverty is really like around the world, while at the same time there are much more similarities than one might expect even with seemingly large gaps in income. Take the time to visit Dollar Street.

What are the differences in the college aspirations of teens?

Pew reports results of a detailed survey in their article Most U.S. Teens See Anxiety and Depression as a Major Problem Among Their Peers — For boys and girls, day-to-day experiences and future aspirations vary in key ways by Juliana Menasce Horowitz and Nikki Graf (2/20/19). Here, we highlight college aspirations:

Girls are more likely than boys to say they plan to attend a four-year college (68% vs. 51%, respectively), and they’re also more likely to say they worry a lot about getting into the school of their choice (37% vs. 26%). Current patterns in college enrollment among 18- to 20-year-olds who are no longer in high school reflect these gender dynamics. In 2017, 64% of women in this age group who were no longer in high school were enrolled in college (including two- and four-year colleges), compared with 55% of their male counterparts.

There are also differences by parental education and economic class:

Among teens with at least one parent with a bachelor’s degree or higher, as well as those in households with annual incomes of $75,000 or more, about seven-in-ten say they plan to attend a four-year college after high school. By comparison, about half of teens whose parents don’t have a bachelor’s degree or with household incomes below $75,000 say the same.

The article has a number of other charts and a detailed methodology section (perfect for a stats  course).

 

How many people in the world don’t have electricity?

Our World in Data’s latest visualization is a bar chart from 1990 to 2016 of the number of people with and without electricity.  In 2016, out of about 7.5 billion people nearly 1 billion lived without electricity or about 12%. In 1990, 1.5 billion people were without electricity, a decrease of 1/2 a billion, but also a decrease from 35% to 2016’s 12%. Their graph is interactive and users can choose individual countries, download the graph, and download the data.

Related Post (12/14/17): How many people don’t have access to electricity?

How much money do parents spend on children?

Parental Financial Investments in Children per Quarter by Household Income Percentile Rank (2014 dollars).

 

The graph here from the American Sociological Review paper Income inequality and Class Divides in Parental Investments by Schneider, Hastigs, and LaBriola (5/21/18) summarizes changes in spending on children by income.

The past 40 years have witnessed historic increases in income inequality in the United States (Piketty and Saez 2003). Over the same period, existing class divides—by household income and by parents’ educational attainment—in how much money parents spend on children and how much time parents spend in childcare have widened considerably (Altintas 2016Kornrich and Furstenberg 2013Ramey and Ramey 2010). These increasingly evident class divides in parental investments of time and money spark concern, because parental investment is an important factor in the intergenerational perpetuation of advantage (Downey, von Hippel, and Broh 2004Potter and Roksa 2013Waldfogel and Washbrook 2011). If affluent families are increasingly able to transmit their advantages to children, that bodes poorly for an open opportunity structure.

Of course,

We would expect rising income inequality to increase class gaps in parental financial investments in children mechanically if rising income inequality simply means the affluent have more to spend. But, rising income inequality might also widen class gaps in investments in children if it reshapes parents’ preferences for these practices differentially by class.

It is also possible that income inequality is not related to class gaps in parental investment. Indeed, recent work suggests a narrowing of gaps in early achievement by family income, and a narrowing or arrested divergence in some gaps in parenting practices, even as income inequality has continued to rise, raising questions about this often assumed empirical relationship (Kalil et al. 2016Reardon 2011Reardon and Portilla 2016).

We empirically investigate these questions.

The paper has interesting charts and data, and worth reading for their conclusions. Also, the supplemental materials include some mathematical modeling.

What are the top charts of 2018?

EPI puts forth its top twelve charts of 2018 in the post Top charts of 2018 Twelve charts that show how policy could reduce inequality—but is making it worse instead (12/20/2018). For example,  chart 10 (copied here) compares 11 economic and social indicators between white and African american families from 1968 to 2018.

Not nearly far enough. The chart shows that, while African Americans are in many ways better off in absolute terms than they were in 1968, they are still disadvantaged in important ways relative to whites. African Americans today are much better educated than they were in 1968—but young African Americans are still half as likely as young whites to have a college degree. Black college graduation rates have doubled—but black workers still earn only 82.5 cents for every dollar earned by white workers. And—as consequences of decades of discrimination—African American families continue to lag far behind white families in homeownership rates and household wealth. The data reinforce that our nation still has a long way to go in a quest for economic and racial justice.

There are 11 other economic related charts. Each chart has a link to data and can  be downloaded.

How does the digital divide impact secondary education for different groups?

The Pew Research Center article Nearly one-in-five teens can’t always finish their homework because of the digital divide by Monica Anderson and Andrew Perrin (10/26/18) provides insights on how lacking access to the internet impacts the ability to complete homework.  Their chart (copied here) gives the percent of school-age children by race and income without high-speed internet.  A second chart provides the results of survey about how this impacts homework. In particular,

One-quarter of black teens say they are at least sometimes unable to complete their homework due to a lack of digital access, including 13% who say this happens to them often. Just 4% of white teens and 6% of Hispanic teens say this often happens to them. (There were not enough Asian respondents in this survey sample to be broken out into a separate analysis.)

The article includes a link at the bottom for results and methodology. This includes sample sizes making this article particularly useful for statistics courses.

What is the relationship between rates of suspension by race and free and reduced lunch?

Propublica’s article, Miseducation – Is There Racial Inequality at Your School? by  Lena V. Groeger, Annie Waldman and David Eads, (10/16/18), provides data by state on the percent of nonwhite students, the percent of students who get free/reduced-price lunch, high school graduation rate, the number of times White students are likely to be in an AP class as compared to Black students, and the number of times Black students are likely to be suspended as compared to White students. The comparison is also available for Hispanic students.

The graph here was created with their data and compares the percent of students on free and reduced lunch with the number of times Black students are likely to be suspended  compared to White students (state data isn’t available for HI, ID, MT, NH, NM, OR, UT, or WY).  The red lines uses all the data where as the blue line removes the outliers of DC and ND. The blue regression line has a p-value of 0.012 and R-squared of 0.15.  This suggests that wealthier states, as measured by free and reduced lunch programs, have a greater disparity is suspensions between black and white students. The impact of outliers is instructive here and there are other scatter plots worth graphing from the article. There are also statistics projects waiting to be created with this data.

The article also has an interactive map or racial disparities by districts, but the map can be misleading based on missing data from districts. Can you see how?  This makes the map itself useful for QL courses.  R Script that created this graph. Companion csv file.