## Teaching With the Data of Economic Mobility

My latest piece for the New York Times Learning Network was inspired by some amazing data visualizations from The Upshot.

These animations show trends in economic mobility gathered from a landmark study of 20 million Americans. In my lesson, students use the Upshot’s customizable tools to collect and analyze data from the study to determine which groups of Americans have the best chance of improving their economic standing.

Here’s the introduction:

America is often referred to as the land of opportunity. But are all opportunities created equal? Do all Americans have the same chance of achieving the American dream?

A groundbreaking study of United States census data examined how the economic status of 20 million Americans changed from childhood to adulthood, and while the data has a lot to tell us about economic opportunity in the United States, it is likely to raise more questions than it answers.

In this lesson, students use tools created by The New York Times to explore data from the study on economic mobility. They will analyze and categorize economic outcomes, compare and contrast statistics for different demographic groups, and pose and explore their own questions about what this data has to say about economic opportunity.

Does everyone in America have the same chance at success? Let’s see what the data says.

The full lesson is freely available here.

## Exploring Compound Interest

My latest piece for the New York Times Learning Network is a math lesson exploring personal savings and the power of compound interest.  The piece was inspired by a new program in Illinois that creates an automatic payroll-deduction savings program for all state residents.

In addition to exploring the basic ideas of savings and compounding, students are invited to analyze the merits of this state-run program.

The automatic retirement savings program mentioned in the article is described as a zero-fiscal-cost program because it does not require any government funding to run. This is because the savers themselves pay the costs, in the form of fees to financial institutions, amounting to 0.75 percent of their total savings each year.

Have students compute the costs associated with maintaining the account for each of the typical savers they profiled in the previous activity. One way to do this is to compute 0.75 percent of the total value of the savings account each year, before interest is computed. This is an estimate of the amount that would be paid in fees that year, and thus should be subtracted from the amount in savings.

The entire piece is freely available here.  Hopefully students will get a sense of the power and value of long-term savings, and maybe ask a few good questions about the the true price of zero-fiscal-cost programs.

## Who Has Done a Billion Dollars-Worth of Work?

Sheryl Sandberg, the COO of Facebook, recently became one of the world’s youngest female billionaires. The Bloomberg article about this featured a curious quote from David Kirkpatrick, author of The Facebook Effect.

“Did she do a billion dollars-worth of work? I don’t know. She had the good fortune to land in the right place where her talents could really be applauded.” (link)

Critics rightly took issue with the gender-bias inherent in this remark. You’d be hard-pressed to find a high-profile business publication questioning whether a rich man really earned his wealth, I suspect.

But beyond this particular offense, the implication that anyone has done “a billion dollars-worth of work” is rather absurd. That this absurdity isn’t recognized speaks to both a general problem of numeracy and to a specific problem of contextualizing large numbers.

What would a billion-dollars worth of work look like? In some ways it’s an ill-defined question, but we can explore some simple cases to get a sense of the answer.

Consider someone working for the current federal minimum wage of \$7.25 an hour. A convenient rule-of-thumb approximation is that one’s hourly wage, doubled, is one’s yearly salary in thousands of dollars. (This assumes someone works 40 hours per week for 50 weeks per year, and so, a total of 2,000 hours per year.)

A full-time minimum wage worker therefore earns about \$15,000 per year. At that rate, 7 years of work would be worth around \$100,000, and so 70 years of work would be worth around \$1 million. Since one billion is equal to 1,000 million, we see that \$1 billion is equivalent to around 70,000 years of minimum wage-work.

Would any billionaire claim to have worked an equivalent of 70,000 years at minimum-wage? I doubt it. Not publicly, at least.

Some other benchmarks may help establish further context. For example, the average teacher salary in New York state is around \$45,000 per year.  Roughly speaking, that’s \$100,000 every two years, and so \$1 million every 20 years. Thus, \$1 billion dollars is worth around 20,000 years of teacher-work.

What about highly-paid professionals? Surgeons typically earn around \$250,000 per year. A quick calculation shows that a billion dollars is worth about 4,000 years of surgeon-work.

While the article suggests otherwise, to me, the answer to the question “Has anyone done a billion dolllars-worth of work?” is pretty clearly “No”.

An interesting follow-up question might be, “Has anyone created a billion dollars worth of value?”

## Math Lesson: Fiscal Cliff

My latest contribution to the New York Times Learning Network is a math lesson that explores the mathematics of the so-called Fiscal Cliff.

Look Before You Leap!  Understanding the Mathematics of the Fiscal Cliff

In this lesson, students explore the quantitative consequences of the expiration of policies like the Bush Era Tax Cuts and the Payroll Tax by calculating income tax differences for individuals across income levels, and putting those numbers in context.

Students are also directed to consider the situation from the perspective of the government by approximating tax-revenue increases and investigating the consequences of discretionary spending cuts.

## Math Lesson: Economic Recovery

My latest contribution to the New York Times Learning Network is a math lesson built around investigating the indicators analysts use to classify and predict economic recovery.