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Tag Archives: inequality

Who misses school the most?

The EPI article,  Student absenteeism – Who misses school and how missing school matters for performance by Emma García and Elaine Weiss (9/25/18) provides a detailed account of absenteeism based on race and gender.  For example, their chart here is the percent of students that missed three or more days in the month prior to the 2015 NAEP mathematics assessment. There are noticeable differences. For instance, the percentage of Black, White, and Asian (non ELL) that missed three or more days in the month is 23%, 18.3%, and 8.8% respectively.

Why does this matter?

In general, the more frequently children missed school, the worse their performance. Relative to students who didn’t miss any school, those who missed some school (1–2 school days) accrued, on average, an educationally small, though statistically significant, disadvantage of about 0.10 standard deviations (SD) in math scores (Figure D and Appendix Table 1, first row). Students who missed more school experienced much larger declines in performance. Those who missed 3–4 days or 5–10 days scored, respectively, 0.29 and 0.39 standard deviations below students who missed no school. As expected, the harm to performance was much greater for students who were absent half or more of the month. Students who missed more than 10 days of school scored nearly two-thirds (0.64) of a standard deviation below students who did not miss any school. All of the gaps are statistically significant, and together they identify a structural source of academic disadvantage.

These results “… identify the distinct association between absenteeism and performance, net of other factors that are known to influence performance?”  The article has 12 graphs or charts, with data available for each, including one that reports p-values.

How have wages grown since 1980?

Source: EPI

Wage growth has varied depending on numerous factors such as gender, race, income level, and education. The EPI article,  America’s slow-motion wage crisis-Four decades of slow and unequal growth by John Schmitt, Elise Gould, and Josh Bivens (9/13/18) summarizes the findings with 30 graphs or tables (data included). For example, the cumulative percent change in inflation-adjusted hourly wages for workers in the 10th, 50th, and 90th percentile is given in the graph here (downloaded from the article).

The first key trend since 1979 is the historically slow growth in real wages. In 2017, middle-wage workers earned just 16.8 percent more than their counterparts almost four decades earlier. This corresponds to an annualized inflation-adjusted growth rate over the 38-year period of just 0.4 percent per year. The real wage increase for low-wage workers (those at the 10th percentile) was even slower: 8.9 percent over 38 years, or a 0.2 percent annualized growth rate.

This slow growth is particularly disappointing for two reasons. First, as we will see in the next section, U.S. workers today are generally older (and hence potentially more experienced) and substantially better educated than workers were at the end of the 1970s.10 Second, for workers at the bottom and the middle, most of the increase in real wages over the entire period took place in the short window between 1996 and the early 2000s. For the large majority of workers over the last four decades, wages were essentially flat or falling apart from a few short bursts of growth.

Quiz Questions: What was the cumulative change in hourly wages from 1979 to 2017 for

  1. What was the cumulative change in hourly wages from 1979 to 2017 for workers with an advanced degree?
  2. What was the cumulative change in hourly wages from 1979 to 2017 for workers with less than a high school diploma?
  3. Which ethnic group had the greatest change?
  4. What was the cumulative change in hourly wages from 1979 to 2017 for Women in the 50th percentile?
  5. What was the cumulative change in hourly wages from 1979 to 2017 for Men in the 50th percentile?

The article and/or corresponding data is ready for use in a stats or QL course in the 90th percentile.

Answers: (1) 30.0% (2) -9.6% (3) Asian American/Pacific Islander non-Hispanic 23.3% (4) 33.8% (5) 8.1%.

What percent of doctors are female?

OECD has the answer in their post Women make up most of the health sector workers but they are under-represented in high-skilled jobs (3/2017) along with a nice graphic.

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.

Where are women less likely than men (ages 30-70) to die of a major disease?

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.

How many people are there and how many can the earth support?

The article in The Conversation 7.5 billion and counting: How many humans can the Earth support? by Andrew D. Hwang (7/9/18) provides some details.  The graph here, copied from the article provides population number and future estimates. 

For real populations, doubling time is not constant. Humans reached 1 billion around 1800, a doubling time of about 300 years; 2 billion in 1927, a doubling time of 127 years; and 4 billion in 1974, a doubling time of 47 years.

On the other hand, world numbers are projected to reach 8 billion around 2023, a doubling time of 49 years, and barring the unforeseen, expected to level off around 10 to 12 billion by 2100.

The article provides a link to download the data and discusses key points related to inequality. For example,

Wealthy countries consume out of proportion to their populations. As a fiscal analogy, we live as if our savings account balance were steady income.

According to the Worldwatch Institute, an environmental think tank, the Earth has 1.9 hectares of land per person for growing food and textiles for clothing, supplying wood and absorbing waste. The average American uses about 9.7 hectares.

These data alone suggest the Earth can support at most one-fifth of the present population, 1.5 billion people, at an American standard of living.

This article is useful for QL and Stats classes, as well as anyone that would like to use population data and/or discuss carrying capacity.

What is the poverty rate in OECD countries?

The OECD (Organisation for Economic Co-operation and Development) defines poverty as an income below half the median household income. The chart here was created using the most recent year of data from the OECD poverty rate page .  The U.S. leads the pack with a rate of 17.8%, with Israel right behind at 17.7%. At the bottom are Denmark and Finland with rates of 5.5% and 5.8% respectively.  It is important to note, as the OECD does,

However, two countries with the same poverty rates may differ in terms of the relative income-level of the poor.

The data is available for more than OECD countries on their page and there is an interactive graph, but the graph can’t be dowloaded. The data and R script that created the graph here are available: csv file, R script.

Citation for data:OECD (2018), Poverty rate (indicator). doi: 10.1787/0fe1315d-en (Accessed on 11 July 2018)

What is the story of suicides in the U.S.?

The article in the Conversation, Why is suicide on the rise in the US – but falling in most of Europe? by Steven Stack (6/28/18), tries to get at the story. The first chart (copied here), clearly shows that the suicide rate rose from 199-2015 overall and considerably more for the 45-54 age group (stats regression problem here).  There is a second chart showing changes in suicide rates in Western European countries:

However, suicide rates in other developed nations have generally fallen. According to the World Health Organization, suicide rates fell in 12 of 13 Western European between 2000 and 2012. Generally, this drop was 20 percent or more. For example, in Austria the suicide rate dropped from 16.4 to 11.5, or a decline of 29.7 percent.

The obvious question is why?

There has been little systematic research explaining the rise in American suicide compared to declining European rates. In my view as a researcher who studies the social risk of suicide, two social factors have contributed: the weakening of the social safety net and increasing income inequality.

The article has two more charts showing that the U.S. is low on Social Welfare Expenditures as a percent of GDP and is high on inequality. In all instances the data is available for download and there are links to the original sources.

What economic impacts does some college education have on men?

The article in the Conversation 22 percent of men without college don’t have jobs. Here’s why they’re being left behind. by Erin Wolcott (6/7/2018) makes two points:

But the unemployment rate doesn’t tell the full story because it only includes people actively looking for work. People who report not having looked for work in the previous four weeks are completely left out of this number. The employment rate, which is the share who are actually employed, captures the full picture.

And the numbers are stark. Back in the 1950s, there was no education-based gap in employment. About 90 percent of men aged 25-54 – regardless of whether they went to college – were employed.

The Great Recession was particularly painful for men without any college. By 2010, only 74 percent had a job, compared with 87 percent of those with a year or more of college.

By 2016, from the graph here copied from the article, the gap was 90% vs 78%. For the second point:

The gap extends to the wages of those who actually had jobs as well. As recently as 1980, real hourly wages for the two groups were nearly identical at about US$13. In 2015, men with at least a little college saw their wages soar 65 percent to over $22 an hour. Meanwhile, pay for those who never attended plunged by almost half to less than $8.

The article has another graph for wages. Both graphs are interactive and contain links to download the data. Read the article.

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.