How Do firms Evaluate New Hires? The Curious Case of NFL Quarterbacks

As we discuss in Chapter 16, the demand for labor depends on the marginal product of labor. In our basic model of a competitive labor market we assume that all workers have the same ability, skills, and training. Firms can hire as many workers as they would like at the market equilibrium wage. Because, by assumption, all workers have the same abilities, firms don’t have to worry about whether one person might be less able or willing to perform the assigned work than another person.

In reality, we know that most firms face more complicated hiring decisions. Even for a job, such as being a cashier in supermarket, that most people can be quickly trained to do, workers differ in how well they carry out their tasks and whether they can be relied on to regularly show up for work and to treat customers politely.

When hiring workers, firms face a problem of asymmetric information: Workers know more about whether they intend to work hard than firms know. Even for applicants who have a work history, a firm may have difficulty discovering how well or how poorly the applicant performed his or her duties in earlier jobs. In responding to inquiries from other firms about a job applicant, firms are rarely willing to do more than confirm that a person has worked at the firm because they are afraid that reporting anything negative about the person—even if true—might expose the firm to a law suit. In Section 16.5, we discuss the field of personnel economics, which includes the study of how firms design compensation policies that attempt to ensure that workers have an incentive to work hard.

When hiring someone entering the labor market, such as a new college graduate, firms have a particular problem in gauging the likely performance of a worker who may have no job history. In this case, there may not be a problem of asymmetric information because the worker may also be uncertain as to how well he or she will be able to perform the job, particularly if the worker has not previously held a full-time job in that field. When hiring new college graduates, firms may rely on an applicant’s college grades, the reputation of the applicant’s college, and the applicant’s scores on standardized test. Some firms have also developed their own tests to measure an applicant’s cognitive skills, knowledge relevant to the position applied for, and even psychological temperament. Some technology firms and investment banks ask applicants to complete demanding problems that may be unrelated to either technology or banking but can provide insight into whether the applicant has the cognitive ability and temperament to quickly complete complicated tasks.

Teams in the National Football League (NFL) face an interesting problem when hiring new players, particularly those playing the position of quarterback. College football players hoping to play professional football enter the NFL draft in which each of the 32 teams select players in eight rounds, with the selections being in reverse order of the teams’ records during the previous football season. There is often a substantial gap between an athlete’s ability to be successful playing college football and his ability to be successful in the NFL. As a result, many players who are stars in college are unable to succeed as professionals.

The position of quarterback is usually thought to be the most difficult to succeed at. Many highly-regarded college quarterbacks fail to do well in the NFL. Teams typically settle on one player as their starting quarterback who will play most of the time. But teams also have one or two backups. Sometimes the backups are older, former starters on other teams, but often they are players chosen in the draft of college players. It’s very difficult to judge how well a quarterback is likely to perform except by seeing him play in a game. Players who perform well in practice often don’t play well in games. As a result, a backup quarterback may be drafted and, if the starting quarterback on his team remains healthy and is effective, earn a nice salary from year to year without actually playing in many games. If a team’s starting quarterback is injured or is ineffective, the backup quarterback may play in several games during a season.

If the backup shows himself to be an effective player, the team may decide to retain him as the starter—with a substantial increase in salary. But given the difficulty of playing the position of quarterback, a more likely outcome is that the backup plays poorly and the team decides to draft another backup quarterback the following year.

The result is an odd situation: The more that a backup quarterback plays in games, often the less likely he is to keep his job. And the less that a backup quarterback plays, the more likely he is to keep his job. Or as one NFL head coach put it: “Backups who don’t play a lot tend to have long NFL careers, while those who are exposed [by actually] playing … have shorter careers.”

This outcome is an extreme example of the difficulty firms sometimes have in measuring how well new hires are likely to perform in their jobs.

Source for quote: Sportswriter David Lombardi on Twitter, quoting San Francisco 49ers’ head coach Kyle Shanahan, December 14, 2020.

How the Effects of the Covid-19 Recession Differed Across Business Sectors and Income Groups

The recession that resulted from the Covid-19 pandemic affected most sectors of the U.S. economy, but some sectors of the economy fared better than others. As a broad generalization, we can say that online retailers, such as Amazon; delivery firms, such as FedEx and DoorDash; many manufacturers, including GM, Tesla, and other automobile firms; and firms, such as Zoom, that facilitate online meetings and lessons, have done well. Again, generalizing broadly, firms that supply a service, particularly if doing so requires in-person contact, have done poorly. Examples are restaurants, movie theaters, hotels, hair salons, and gyms.

The following figure uses data from the Federal Reserve Economic Data (FRED) website (fred.stlouisfed.org) on employment in several business sectors—note that the sectors shown in the figure do not account for all employment in the U.S. economy. For ease of comparison, total employment in each sector in February 2020 has been set equal to 100.

Employment in each sector dropped sharply between February and April as the pandemic began to spread throughout the United States, leading governors and mayors to order many businesses and schools closed. Even in areas where most businesses remained open, many people became reluctant to shop in stores, eat in restaurants, or exercise in gyms. From April to November, there were substantial employment gains in each sector, with employment in all goods-producing industries and employment in manufacturing (a subcategory of goods-producing industries) in November being just 5 percent less than in February. Employment in professional and business services (firms in this sector include legal, accounting, engineering, legal, consulting, and business software firms), rose to about the same level, but employment in all service industries was still 7 percent below its February level and employment in restaurants and bars was 17 percent below its February level.

Raj Chetty of Harvard University and colleagues have created the Opportunity Insights website that brings together data on a number of economic indicators that reflect employment, income, spending, and production in geographic areas down to the county or, for some cities, the ZIP code level. The Opportunity Insights website can be found HERE.

In a paper using these data, Chetty and colleagues find that during the pandemic “spending fell primarily because high-income households started spending much less.… Spending reductions were concentrated in services that require in-person physical interaction, such as hotels and restaurants …. These findings suggest that high-income households reduced spending primarily because of health concerns rather than a reduction in income or wealth, perhaps because they were able to self-isolate more easily than lower-income individuals (e.g., by substituting to remote work).”

As a result, “Small business revenues in the highest-income and highest-rent ZIP codes (e.g., the Upper East Side of Manhattan) fell by more than 65% between March and mid-April, compared with 30% in the least affluent ZIP codes. These reductions in revenue resulted in a much higher rate of small business closure in affluent areas within a given county than in less affluent areas.” As the revenues of small businesses declined, the businesses laid off workers and sometimes reduced the wages of workers they continued to employ. The employees of these small businesses, were typically lower- wage workers. The authors conclude from the data that: “Employment for high- wage workers also rebounded much more quickly: employment levels for workers in the top wage quartile [the top 20 percent of wages] were almost back to pre-COVID levels by the end of May, but remained 20% below baseline for low-wage workers even as of October 2020.”

The paper, which goes into much greater detail than the brief summary just given, can be found HERE.

Census Bureau Releases Results from the American Community Survey

Each year the U.S. Census Bureau conducts the American Community Survey (ACS) by surveying 3.5 million households on a wide range of questions including their income, their employment, their ethnicity, their marital status, how large their house or apartment is, and how many cars they own. The ACS is the most reliable source of data on these issues and is widely used by economists, business managers, and government policy makers. The data for 2019 and for the five-year period 2015-2019 were released on December 10. You can learn more about the survey and explore the data on the ACS website.

The ACS provides data on increases in income over time by different ethnic groups. This news article discusses the result that between 2005 and 2019, the incomes of Asian American grew the fastest, followed by the incomes of Hispanics, the incomes of non-Hispanic whites, and the incomes of African Americans.

Does Automation Lead to Permanent Job Losses?

This post on the Federal Reserve Bank of St. Louis’s Page One blog discusses how the belief that automation can lead to permanent job losses is an example of the “lump of labor” fallacy. Click HERE to read the article.

The post refers to the circular-flow diagram, which we discuss in Chapter 2 and in Chapter 18 in the textbook. We discuss the effects of automation and robots on the labor market in Chapter 16.

9/04/20 Podcast – Authors Glenn Hubbard & Tony O’Brien Welcome Jadrian Wooten of Penn State.

Listen as authors Glenn Hubbard and Tony O’Brien have a wide-ranging discussion with Jadrian Wooten, an economics professor at Penn State University. Jadrian discusses some pedagogical approaches in his online classes, his use of a standing desk in zoom teaching his large classes, as well as the unclear impact of missing college football to the local college economies.

Over the next several weeks, we will be gearing up this podcast to become an essential listen during your week. Whether your interest is teaching or policy, you will learn from this discussion.

Just search Hubbard O’Brien Economics on Apple iTunes and subscribe! Episodes are usually available the next day on Apple iTunes or any other podcast app.

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