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Tag Archives: statistics

What is the history of manufacturing employment in the U.S.?

We can answer this question by using FRED. The accompanying graph was created with FRED’s graphing tool (see below for a quick tutorial on creating this graph), which creates an interactive graph that can be downloaded along with the data. The blue line represents total manufacturing jobs, which consistently decreases during a recession (gray bands). Manufacturing jobs peaked in 1979 at just below 20 million and now stand at about 12.5 million. The red line provides another perspective and represents the percent of manufacturing jobs relative to all employment.  In the 1940s manufacturing represented almost 40% of all employment. It has been decreasing ever since and today it is down to around 8.5%.

How to create the graph: Start by searching FRED for manufacturing employment. You should get this.  On the upper right click edit graph and then add line (second button on top). Search employment and click on All Employees: Total Nonfarm Payrolls.  Add the data series. Go to format (third button across the top)  and click right under y-axis position for LINE 2.  Now go to edit line 2 (first button across the top). Under customize data search manufacturing. Click All Employees: Manufacturing. In formula type b/a. Now click add next to All Employees: Manufacturing.  This does it. FRED offers a powerful tool.

What do you know about historical unemployment by race?

The data, from the U.S. Bureau of Labor Statistics, and a graph by FRED can enlighten you. FRED has Black, Hispanic, and White unemployment data since 1973.  Here we downloaded the graph since the end of the 2008 recession. At its peak (about March 2010) Black unemployment (16.8%) was about twice that of White (8.9%), while Hispanic unemployment was about 50% greater at 12.9%.  Currently, Dec 1017, the spread isn’t as bad but the relationships still exists with unemployment rates at 6.8% (Black), 4.9% (Hispanic), and 3.7% (White). The FRED graph is interactive and you can download the data.

What is the lead-crime hypothesis?

Kevin Drum provides an overview and update of the hypothesis in his detailed post An Updated Lead-Crime Roundup for 2018. In short,

The lead-crime hypothesis is pretty simple: lead poisoning degrades the development of childhood brains in ways that increase aggression, reduce impulse control, and impair the executive functions that allow people to understand the consequences of their actions. Because of this, infants who are exposed to high levels of lead are more likely to commit violent crimes later in life.

He notes further down in the article that

It’s important to emphasize that the lead-crime hypothesis doesn’t claim that lead is solely responsible for crime. It primarily explains only one thing: the huge rise in crime of the 70s and 80s and the equally huge—and completely unexpected—decline in crime of the 90s and aughts. The lead-crime hypothesis is the answer to the question mark in the stylized chart below:

The post has useful graphs for QL based courses, provides an overview of the hypothesis, and the Statistics Projects section of this blog has lead-crime data for projects.

How does a small increase in average temperature increase the chance of extremes?

The Climate Central post, Small Change in Average -Big Change in Extremes, summarizes the idea well with the graph. As the mean shifts to the right, there is a significant increase in the chance of extreme temperature. The animated gif on the site is perfect in expressing the idea.

That’s what we are seeing across much of the country. Average summer temperature have risen a few degrees across the West and Southern Plains, leading to more days above 100°F in Austin, Dallas and El Paso all the way up to Oklahoma City, Salt Lake City, and Boise.  It’s worth noting that this trend has been recorded across the entire Northern Hemisphere, as shown in this WXshift animation.

You should check out the WXshift page they link to. This material is perfect for a stats course. It is also worth pointing out that the pictures here assumes the standard deviation stays the same, but there is evidence that it may be increasing. The effect is a flatter more stretched out density, with even greeter likelihood of extremes.

How strong is the relationship between women’s education and fertility?

Our World in Data has an interactive graph of women’s educational attainment vs fertility, by country and colored by region, from 1950-2010.  The correlation between the average years of education for women and the countries fertility rate is clear.  A world bank article, Female Education and Childbearing: A Closer Look at the Data, from 2015 provides evidence that the relationship is causal.

Why does female education have a direct effect on fertility? The economic theory of fertility suggests an incentive effect: more educated women have higher opportunity costs of bearing children in terms of lost income. The household bargaining model suggests that more educated women are better able to support themselves and have more bargaining power, including on family size.

According to the ideation theory, more educated women may learn different ideas of desired family size through school, community, and exposure to global communication networks. Finally, more educated women know more about prenatal care and child health, and hence might have lower fertility because of greater confidence that their children will survive.

Of course, education isn’t the only factor contributing to fertility rates.  Data is provided by Our World in Data, along with the graph. The data can be used for tests of correlation, regression, and one can compare by county and region for specific years.

Life Expectancy by Health Expenditure with Comments on Differences by Race

Our World in Data has an interactive graph of life expectancy by health expenditure for a number of countries, with downloadable data. The U.S. spends more money per person on health care, by far, than the other countries represented, without corresponding gains in life expectancy. At the same time, there are large differences in life expectancy by race in the U.S.  For example, the 2013 CDC National Vital Statistics Report life tables has life expectancy at birth for Non-Hispanic Black males of 71.9 years, which would be at the bottom of the chart.  Hispanic females are at the top in the U.S. with a life expectancy at birth of 84.2 years; a 12.3 year difference (data on page 3 here).  At the same time, the money spent on health care is also not likely to be equally distributed. The CDC is a source of life expectancy data and if you ask them they might have excel files. For an example of using life expectancy data, here is a 2012 paper Period Life Tables: A Resource for Quantitative Literacy published in Numeracy and freely available.

Life Expectancy vs Income Per Person

With health care in the news, let’s take a look at the knowledge that can be gained by using Gapminder. For example, the graph here is life expectancy vs income per person for 2015, with the bubbles representing population size of the country. Can you guess the bubble for the U.S.? Go to the graph on Gapminder to find out.  As a bonus their is a play button so that the graph will scroll from 1800 to 2015. You will also find a number of tools to change the graph and create others. All the data used by the Gapminder graphs is located on their data page.

Who eats more fast food the poor or wealthy?

Data helps us understand the world as it really is as opposed to what we think is true. The article Do poor people eat more junk food than wealthier American? uses the Bureau of Labor Statistics longitudinal data, accessible in the article, to answer the question.

Because it’s considered relatively inexpensive, there’s an assumption that poor people eat more fast food than other socioeconomic groups – which has convinced some local governments to try to limit their access.

Read the article to learn more and take advantage of the data sources for statistics or QL courses.

Fact Checking Coal Mining Jobs

The Washington Post’s Fact Checker article Pruitt’s claim that ‘almost 50,000 jobs’ have been gained in coal includes links to the Bureau of Labor Statistics data for each of their three screen shots of spreadsheets that back up their statements. The article notes:

In the last four months of the Obama administration, September to January, there was a gain of 1,400 jobs. In the first four months of the Trump administration, there has been a gain of 1,000 jobs.

While not part of the article, the graph here from the BLS is seasonally adjusted coal mining jobs since 1985.  Interestingly there was a drastic decline in coal mining jobs from 1985 until about 2000.

Is Life Fair or Not?

Stats classes are always looking for interesting data. One place to look in YouGov. For example, they did a poll (Note: it is not clear how the sample was obtained but they do provide a sample size.) asking people if life is fair.  Here are the results by gender.

  1. Do you think life is fair or not fair?
% TOTAL Male Female
Life is fair 38 46 31
Life is not fair 46 40 51
Not sure 16 14 18

You are set for a statistical test comparing Male vs Female perception of life being fair or not. This now allows for a discussion of why women would respond differently than men. One extra bonus on the site is you can look at the same questions broken down by other categories including income. Go to the YouGov Results page to see the data they have.