Tag Archives: charts and graphs

What were the leading causes of death in 2020?

The CDC’s report, Provisional Mortality Data – United States 2020 (3/31/2021) provides the chart presented here.  COVID-19 was the third leading cause of death, although there were only deaths attributed to COVID-19 for nine months of the year. There is also this:

During January–December 2020, the estimated 2020 age-adjusted death rate increased for the first time since 2017, with an increase of 15.9% compared with 2019, from 715.2 to 828.7 deaths per 100,000 population. COVID-19 was the underlying or a contributing cause of 377,883 deaths (91.5 deaths per 100,000). COVID-19 death rates were highest among males, older adults, and AI/AN and Hispanic persons. The highest numbers of overall deaths and COVID-19 deaths occurred during April and December. COVID-19 was the third leading underlying cause of death in 2020, replacing suicide as one of the top 10 leading causes of death (6).

The findings in this report are subject to at least four limitations. First, data are provisional, and numbers and rates might change as additional information is received. Second, timeliness of death certificate submission can vary by jurisdiction. As a result, the national distribution of deaths might be affected by the distribution of deaths from jurisdictions reporting later, which might differ from those in the United States overall. Third, certain categories of race (i.e., AI/AN and Asian) and Hispanic ethnicity reported on death certificates might have been misclassified (7), possibly resulting in underestimates of death rates for some groups. Finally, the cause of death for certain persons might have been misclassified. Limited availability of testing for SARS-CoV-2, the virus that causes COVID-19, at the beginning of the COVID-19 pandemic might have resulted in an underestimation of COVID-19–associated deaths.

There is a table with data of total and covid deaths by age, sex, and race/ethnicity, as  well as another chart.

How’s the labor market for college grads?

The Federal Reserve Bank of New York’s page The Labor Market for Recent College Graduates has a number of graphs related to employment for recent and not so recent grads. For example, their graph here is the percent that are underemployed defined as

The underemployment rate is defined as the share of graduates working in jobs that typically do not require a college degree. A job is classified as a college job if 50 percent or more of the people working in that job indicate that at least a bachelor’s degree is necessary; otherwise, the job is classified as a non-college job. Rates are seasonally adjusted and smoothed with a three-month moving average. College graduates are those aged 22 to 65 with a bachelor’s degree or higher; recent college graduates are those aged 22 to 27 with a bachelor’s degree or higher.

There are graphs for unemployment, underemployed job types, wages and a table of outcomes by major. In all cases the data can be downloaded.

What is the connection between life expectancy and education?

The PNAS paper Life expectancy in adulthood is falling for those without a BA degree, but as educational gaps have widened, racial gaps have narrowed by Anne Case and Angus Deaton (3/16/2021) provides an answer. From the abstract:

We construct a time series, from 1990 to 2018, of a summary of each year’s mortality rates and expected years lived from 25 to 75 at the fixed mortality rates of that year. Our measure excludes those over 75 who have done relatively well over the last three decades and focuses on the years when deaths rose rapidly through drug overdoses, suicides, and alcoholic liver disease and when the decline in mortality from cardiovascular disease slowed and reversed. The BA/no-BA gap in our measure widened steadily from 1990 to 2018. Beyond 2010, as those with a BA continued to see increases in our period measure of expected life, those without saw declines.

By 2018, intraracial college divides were larger than interracial divides conditional on college; by our measure, those with a college diploma are more alike one another irrespective of race than they are like those of the same race who do not have a BA.

The appendix has 7 graphs including the one copied here. A few observations: Hispanics with a BA have a greater life expectancy by sex. In fact, a Hispanic female with no BA has a life expectancy similar to that of a White Male with a BA. The gap in life expectancy between Black and White by sex is about the same by BA/no BA (about 1 year in all 4 cases) but the gap between those with a BA and those without is larger.  For example, a Black male with a BA lives almost 3 years longer than a White male without a BA. This is about 2 years for women.  The no BA group has seen decreasing life expectancy, in general, since about 2010, while the BA group has continued with an increasing life expectancy.

I didn’t find the data in the appendix but there is an email to contact an author and they may provide it if you ask.

How has unemployment changed over the last year?

The U.S. Bureau of Labor Statistics has an interactive graph of unemployment for cities from Jan 2020 to Jan 2021.

Unemployment rates were higher in January 2021 than a year earlier in 376 of the 389 metro areas, lower in 9 areas, and unchanged in 4 areas. The largest over-the-year unemployment rate increase occurred in Kahului-Wailuku-Lahaina, Hawaii. Rates rose over the year by at least 5.0 percentage points in an additional 11 areas.

Unemployment rates were 10.0 percent or higher in 21 metro areas in January 2021. This was greater than the 4 areas with unemployment rates of at least 10.0 percent in January 2020 but much less than the 339 areas in April 2020, at the onset of the COVID-19 pandemic.

The data is available on the page and provides unemployment rates for metropolitan areas from Jan 2020 to Jan 2021.

What is the distribution of global income?

The Our World in Data article How much economic growth is necessary to reduce global poverty by Max Roser (2/15/2021) includes the graph copied here. Note that all countries incomes are adjusted for price differences so it is fair comparison from county to country. It is easy to forget how much wealthier the U.S. is compared to almost all other countries.

The reason why such substantial economic growth is necessary for reducing global poverty is that the average income in many countries in the world is very low: 82% of the world population live in countries where the mean income is less than $20 per day.

There are three other graphs in the article, which is suitable for a QL based course. There isn’t data associated with these particular graphs but there are links at the top of the article with related economic data.

Is there a polar vortex climate change connection?

When the Arctic polar vortex is especially strong and stable (left globe), it encourages the polar jet stream, down in the troposphere, to shift northward. The coldest polar air stays in the Arctic. When the vortex weakens, shifts, or splits (right globe), the polar jet stream often becomes extremely wavy, allowing warm air to flood into the Arctic and polar air to sink down into the mid-latitudes. NOAA Climate.gov graphic, adapted from original by NOAA.gov.


The climate.gov article Understanding the Arctic polar vortex by Rebecca Lindsey (3/5/2021) is a complete primer on the polar vortex, jet stream, and what we know (and don’t) abut the connection to climate change.

According to NOAA stratosphere expert Amy Butler, people often confuse the polar vortex with the polar jet stream, but the two are in completely separate layers of the atmosphere. The polar jet stream occurs in the troposphere, at altitudes between 5-9 miles above the surface. It marks the boundary between surface air masses, separating warmer, mid-latitude air and colder, polar air. It’s the polar jet stream that plays such a big role in our day-to-day winter weather in the mid-latitudes, not the polar vortex.

Any relationships to climate change is unclear, for example:

The uncertainty due to a relatively short history of observations isn’t the only reason experts can’t dismiss the possibility that something could be up with polar vortex. Some climate model experiments do predict that continued warming will lead to a weakening of the polar vortex. “It’s true that when you run some high-resolution climate models, with a realistic stratosphere, and a realistic sea ice layer, and you reduce sea ice cover, these models predict that the polar vortex gets weaker,” Butler said. And some studies combining models and observations have shown a connection between low sea ice extent in the Barents and Kara Seas of the eastern Arctic, sudden stratospheric warming events, and cold winters in North America.

At the same time, other model simulations predict that warming and sea ice loss will lead to a stronger polar vortex. Part of the reason for the disagreement is that the impact of Arctic surface warming and sea ice loss on the atmospheric waves that can disrupt the polar vortex is very sensitive to exactly where and when the sea ice loss occurs, and that hasn’t been consistent across model simulations.

No data in this article but there are some useful graphs, such as the one copied here, and the article is just generally interesting.

Why did wages grow in 2020?

The EPI article, Wages grew in 2020 because the bottom fell out of the low-wage labor market, by Elise Gould and Jori Kandra (2/24/2021) provides insights into changes in the labor market this past year. Key find:

Wages grew largely because more than 80% of the 9.6 million net jobs lost in 2020 were jobs held by wage earners in the bottom 25% of the wage distribution. The exit of 7.9 million low-wage workers from the workforce, coupled with the addition of 1.5 million jobs in the top half of the wage distribution, skewed average wages upward.

There are seven graphs or tables in the article with the associated data. The last two graphs are of the same type as the one copied here but for the 2000 and 2008 recessions, respectively.

How much wind power was installed in 2020?

From the eia article The United States installed more wind turbine capacity in 2020 than in any other year by Richard Bowers and Owen Comstock (3/32021):

In both 2019 and 2020, project developers in the United States installed more wind power capacity than any other generating technology. According to data recently published by the U.S. Energy Information Administration (EIA) in its Preliminary Monthly Electric Generator Inventory, annual wind turbine capacity additions in the United States set a record in 2020, totaling 14.2 gigawatts (GW) and surpassing the previous record of 13.2 GW added in 2012. After this record year for wind turbine capacity additions, total wind turbine capacity in the United States is now 118 GW.

There are two other graphs in the article and an answer to the question of which state generates the most wind power. There are also links to the data.

What did the 116th Congress do more of?

From the Pew article, Though not especially productive in passing bills, the 116th Congress set new marks for social media use, by Aaron Smith and Sono Shah (1/25/2021):

Voting members of the 116th Congress collectively produced more than 2.2 million tweets and Facebook posts in 2019 and 2020. That means the median member of Congress produced more than 3,000 posts across their profiles on the two social media platforms during this span.

The 3000 sound like a lot but amounts to only about 8 posts a day and I have to imagine that some of it is done by aides.

There are two other charts in the article and a detailed methodology section. There is also a link to a related article which includes the number of laws passed from the 101st through 116th congress.

How should we measure COVID-19 deaths?

As we try to quantify the deaths by COVID-19 we need to measure it correctly. For example, deaths should be normalized to population size. Beyond that, we should really look at excessive mortality, that is mortality above what we might see without COVID-19. Some causes of deaths have decreased over the last year.  Our World in Data does just this on their Excess mortality during the Coronavirus pandemic (COVID-19) page. Not only do they  provide interactive graphs, such as the one copied here but also an explanation of the methodology.

A measure that is more comparable across countries is the P-score, which calculates excess mortality as the percentage difference between the number of deaths in 2020–2021 and the average number of deaths in the same period — week or month — over the years 2015–2019.

While the P-score is a useful measure, it too has limitations. For example, the five-year average death count might be a relatively crude measure of expected deaths because it does not account for trends in mortality or population size. To learn about other measures of excess mortality and their strengths and limitations, see our article with John Muellbauer and Janine Aron.

Note that the graph has times were for, say Italy, deaths were twice what would normally be expected, but at other times actually negative. As always Our World in Data provides the data and other graphics.