Economics Planet - Daily Blog Posts about Economics

May 20, 2012

Fiscal stimulus

My colleague UCSD Professor Valerie Ramey has an interesting new paper looking at the effects of higher government spending on GDP.

Ramey (2012) approaches the question from a forecasting perspective. Suppose a certain event (examples of which are detailed below) causes you to revise your forecast of how high government spending is going to be over the next few years. How would this news cause you to change your forecast of how high private GDP (that is, all the components of GDP other than government spending) is going to be? If your prediction of private GDP goes up, that is evidence consistent with a fiscal multiplier greater than one-- added government spending not only contributes directly to GDP from the accounting identity, but also helps boost private spending as well. If private GDP goes down, that suggests a multiplier less than one.

The forecasting models she looks at are vector autoregressions, in which one tries to predict a set of variables such as the log of real government spending per capita, log of real private GDP per capita, the marginal tax rate, and the 3-month T-bill rate based on what all of those 4 variables have been doing over the last year. In her simplest exercise, Valerie looked at how the forecasts of each of the 4 variables would change if real spending this quarter comes in higher than you would have expected according to the model. The top panels in the figure below are based on forecasting relations using data all the way back to 1939. The graph in the left panel shows how the model's k-quarter-ahead forecast of real government spending would change in response to news of higher government spending at time 0, plotted as a function of k, how far into the future you're looking. (It will help economist readers, but perhaps not anyone else, if I were to describe this as an impulse-response function based on a Cholesky factorization with government spending ordered first as in Blanchard and Perotti (2002)). Given the positive serial correlation in government spending, if you learn spending is about 0.4% higher this quarter, you'd expect further spending increases over the next year, with the graph normalized such that the news causes you to expect 1% higher real spending per person 4 quarters following the original information.

The right top panel shows how the model's forecast of future real GDP per person excluding government spending would change in response to the news. The model predicts that private spending will be 0.7% lower after a year. This negative effect is statistically significant.

Revision in forecast k quarters into the future following an unanticipated increase in real government spending per person at time 0. Left panels: change in forecast (in percentage points) of real government spending per person. Right panels: change in forecast (in percentage points) of real GDP less real government spending per person. Estimates based on sample period indicated at top, 95% confidence regions in gray. Source: Ramey (2012).

In part this inference is based on what happened during World War II. Between 1941 and 1944, real government spending increased by $75 billion (in 1937 dollars), but real GDP only rose by $60 billion. That's consistent with the patterns above, in which private spending falls in response to higher government spending. One might argue that there were other special factors such as rationing that reduced private GDP at the time. The second row in the figure above leaves out the World War II data, and just bases the inference on what we saw over 1947-2008. The effects are similar to those found using the full sample. The last panel of the above figure leaves out both World War II and Korea. With less data, the inferences are less reliable, but the overall estimates remain quite similar to those for the full sample.

Valerie's paper explores a number of other possible ways one could form the forecasting question. One concern is whether government spending rose in response to some other events that might have had direct effects on the economy, in which case the revision in forecasts might represent the consequences of those events instead of an effect of government spending itself. The next graph uses an alternative idea proposed by Perlotti (2011) of specifying the news variable in terms of defense spending alone rather than overall government spending (this is now a 5-variable VAR with defense spending ordered first). Results are essentially the same as for the first exercise.

Revision in forecast k quarters into the future following an unanticipated increase in defense spending at time 0. Left panels: change in forecast (in percentage points) of real government spending per person. Right panels: change in forecast (in percentage points) of real GDP less real government spending per person. Estimates based on sample period indicated at top, 95% confidence regions in gray. Source: Ramey (2012).

In yet another approach, Valerie used a news series constructed in Ramey (2011) that is based on reading of Business Week and other historical sources to construct a series of changes in the expected present discounted value of government spending caused by military events. Although the effects on private GDP are not measured as precisely using this indicator, the overall inference confirms the view that higher government spending raises GDP by less than the spending itself.

Revision in forecast k quarters into the future following an unanticipated increase in future defense spending as measured by Ramey (2011). Left panels: change in forecast (in percentage points) of real government spending per person. Right panels: change in forecast (in percentage points) of real GDP less real government spending per person. Estimates based on sample period indicated at top, 95% confidence regions in gray. Source: Ramey (2012).

Ramey (2012) concludes:

Using a variety of identification methods and samples, I find that in most cases private spending falls significantly in response to an increase in government spending. These results imply that the average GDP multiplier lies below unity.

May 19, 2012

The Capital Spectator

Book Bits | 5.19.2012

● The 7 Most Important Equations for Your Retirement: The Fascinating People and Ideas Behind Planning Your Retirement Income
By Moshe Milevsky
Summary via publisher, Wiley

Physics, Chemistry, Astronomy, Biology; every field has its intellectual giants who made breakthrough discoveries that changed the course of history. What about the topic of retirement planning? Is it a science? Or is retirement income planning just a collection of rules-of-thumb, financial products and sales pitches? In The 7 Most Important Equations for Your Retirement...And the Stories Behind Them Moshe Milevsky argues that twenty first century retirement income planning is indeed a science and has its foundations in the work of great sages who made conceptual and controversial breakthroughs over the last eight centuries. In the book Milevsky highlights the work of seven scholars—summarized by seven equations—who shaped all modern retirement calculations. He tells the stories of Leonardo Fibonnaci the Italian businessman; Benjamin Gompertz the gentleman actuary; Edmund Halley the astronomer; Irving Fisher the stock jock; Paul Samuelson the economic guru; Solomon Heubner the insurance and marketing visionary, and Andrey Kolmogorov the Russian mathematical genius—all giants in their respective fields who collectively laid the foundations for modern retirement income planning.

Econbrowser

BLS: Wisconsin Private and Total Nonfarm Payroll Employment Decline in April

Official figures indicate employment declines in April, according to the BLS. Private payrolls are 4700 below January 2011 Levels

Figure 1: Actual Wisconsin private nonfarm payroll employment (blue), QCEW and DWD data based estimate of WI NFP (see post) minus government employment (red) and Walker path (green). Source: BLS (April release), author’s calculations.

Note that the numbers released by Governor Walker's Administration (pertaining to data through 2011M12) have no bearing on the direction of change in employment recorded in April.

Wisconsin nonfarm payroll employment is 12,800 below 2011M01 levels. Even if one used QCEW based numbers for private employment (see this post), and assumed the private employment changes since 2011M12, then April’s level would be 41.1 thousand below the Walker target.

May 18, 2012

The Capital Spectator

Passive Asset Allocation Strategies Are Still Tough To Beat

Brett Arends of SmartMoney skewers the simple stock/bond balanced fund strategy, and rightly so. There's no reason to rely on a basic equity/fixed income mix in a world where a wider array of asset classes are available through low-cost ETFs.

macroblog

Is inflation targeting really dead?

Harvard's Jeffrey Frankel (hat tip, Mark Thoma) is the latest econ-blogger to cast an admiring gaze in the direction of nominal gross domestic product (GDP) targeting. Frankel's post is titled "The Death of Inflation Targeting," and the demise apparently includes the notion of "flexible targeting." The obituary is somewhat ironic in that at least some of us believe that the U.S. central bank has recently taken a big step in the direction of institutionalizing flexible inflation targeting. Frankel, nonetheless, makes a case for nominal GDP targeting:

"One candidate to succeed IT [inflation targeting] as the preferred nominal monetary-policy anchor has lately received some enthusiastic support in the economic blogosphere: nominal GDP targeting. The idea is not new. It had been a candidate to succeed money-supply targeting in the 1980's, since it did not share the latter's vulnerability to so-called velocity shocks.

"Nominal GDP targeting was not adopted then, but now it is back. Its fans point out that, unlike IT, it would not cause excessive tightening in response to adverse supply shocks. Nominal GDP targeting stabilizes demand—the most that can be asked of monetary policy. An adverse supply shock is automatically divided equally between inflation and real GDP, which is pretty much what a central bank with discretion would do anyway."

That's certainly true, but a nominal GDP target is consistent with a stable inflation or price-level objective only if potential GDP growth is itself stable. Perhaps the argument is that plausible variations in potential GDP are not large enough or persistent enough to be of much concern. But that notion just begs the core question of whether the current output gap is big or small. At least for me, uncertainty about where GDP is relative to its potential remains the key to whether policy should be more or less aggressive.

In another recent blog item (also with a pointer from Mark Thoma), Simon Wren-Lewis offers the opinion that acknowledging uncertainty about size of the output gap actually argues in favor of being "less cautious" about taking an aggressive policy course. The basic idea is familiar. It is a simple matter to raise rates should the Fed overestimate the magnitude of the output gap. But with the short-term policy rates already at zero, it is not so easy to go in the opposite direction should we underestimate the gap.

No argument there. As I pointed out in a May 3 macroblog item, Atlanta Fed President Dennis Lockhart has said the same thing. But, as I argued in that post, this point of view is only half the story. Though I agree that the costs are asymmetric to the downside with respect to the FOMC's employment and growth mandate, they look to me to be asymmetric to the upside with respect to the price stability mandate. And I view with some suspicion the claim that we know how to easily manage policy that turns out to be too aggressive after the fact.

My issues are not merely academic. In an important paper published a decade ago, Anasthsios Orphanides made this assertion:

"Despite the best of intentions, the activist management of the economy during the 1960s and 1970s did not deliver the desired macroeconomic outcomes. Following a brief period of success in achieving reasonable price stability with full employment, starting with the end of 1965 and continuing through the 1970s, the small upward drift in prices that so concerned Burns several years earlier gave way to the Great Inﬂation. Amazingly, during much of this period, specifically from February 1970 to January 1977, Arthur Burns, who so opposed policies fostering inﬂation, served as Chairman of the Federal Reserve. How then is this macroeconomic policy failure to be explained? And how can such failures be avoided in the future?...

"The likely policy lapse leading to the Great Inﬂation …can be simply identified. It was due to the overconfidence with which policymakers believed they could ascertain in real-time the current state of the economy relative to its potential. The willingness to recognize the limitations of our knowledge and lower our stabilization objectives accordingly would be essential if we are to avert such policy disasters in the future."

With this historical observation in hand, it seems a short leap to turn Wren-Lewis's thought experiment on its head. Arguably, the last several years have demonstrated that nonconventional policy actions have been quite successful at short-circuiting the disinflationary spirals that pose the central downside risk when interest rates are near zero. (If you can tolerate a little math, a good exposition of both theory and evidence is provided by Roger Farmer.)

On the opposite side of the ledger, we know little about the conditions that would cause the Fed to lose credibility with respect to its commitment to its inflation goals, and very little about the triggers that would cause inflation expectations to become unanchored. Thus, I think it not difficult to construct a plausible argument about the risks of being wrong about the output gap that is exact opposite of the Wren-Lewis conclusion.

I end up about where I did in my previous post. Flexible inflation targeting, implemented in such a way that the 2 percent long-run inflation target rate exerts an observable gravitational pull over the medium term, feels about right to me. Despite what Frankel seems to believe, I think that idea is far from dead.

By Dave Altig, executive vice president and research director at the Atlanta Fed

macroblog

A take on labor force participation and the unemployment rate

By now, if you've been paying attention to the coverage following the April employment report, you know the following:

The March to April decline in the unemployment rate from 8.2 percent to 8.1 percent was arithmetically driven by yet another decline in the labor force participation rate (LFPR).
The decline in the LFPR, now at its lowest level since the early 1980s, is itself being influenced by a confounding mix of demographic change and other behavioral changes that nobody seems to understand—a point emphasized by a gaggle of blogs and bloggers such as Brad DeLong, Carpe Diem, Conversable Economist, Free Exchange, and Rortybomb, to name a few.

With respect to the first observation, in a previous post my colleague Julie Hotchkiss described how to use our Jobs Calculator to get a ballpark sense of what the unemployment rate would have been had the LFPR not changed. If you follow those procedures and assume that the LFPR had stayed at the March level of 63.8 percent instead of falling to 63.6 percent, the unemployment rate would have risen to 8.4 percent instead of falling to 8.1 percent.

It is clear that interpreting this sort of counterfactual experiment depends critically on how you think about the decline in the LFPR. The aforementioned post at Rortybomb cites two Federal Reserve studies—from the Chicago Fed and the Kansas City Fed—that attempt to disentangle the change in the LFPR that can be explained by trends in the age and composition of the labor force. These changes are presumably permanent and have little to do with questions of whether the labor market is performing up to snuff.

The following chart, which throws our own estimates into the mix, illustrates the evolution of the actual LFPR along with an estimate of the LFPR adjusted for demographic changes:

enlarge

As the header on the chart indicates, our estimates suggest that roughly 40 percent of the change in the LFPR since 2000 can be accounted for by changes in age and composition of the population—in essentially the same range as the Chicago and Kansas City Fed studies. (If you are interested in the technical details you can find a description of the methodology used to generate the chart above, based on work by the University of Chicago's Rob Shimer.

In other words, 0.9 percentage points of the decline in the LFPR since the beginning of the past recession can be explained by demographic trends (as the baby boomers age, the labor force will grow more slowly than the total population [ages 16 and up]). Subtracting the demographic trends still leaves 1.5 percentage points to be explained, a number right in line with Brad DeLong's back-of-the-envelope calculation of "cyclical" LFPR change.

As DeLong's comments make clear, the interpretation of the nondemographic piece of the LFPR change requires, well, interpretation. And the consequences of connecting the dots between changes in the unemployment rate and broader labor market performance are enormous.

In the recently released Summary of Economic Projections following the last meeting of the Federal Reserve's Federal Open Market Committee, the midpoint of the projections for the unemployment rate at the end of 2013 is 7.5 percent. Turning again to our Jobs Calculator, we can get a sense of what sort of job creation over the next 20 months will be required given different values of the LFPR. For these estimates, I consider three alternatives: The LFPR stays at its April level, the LFPR reverts to our current estimate of the demographically adjusted level (that is, increases by 1.5 percentage points), and an intermediate case in which the LFPR increases by 0.7 percentage points—the lower end of DeLong's estimate of "people who really ought to be in the labor force right now, but who are not."

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DeLong asks:

"Are [people who really ought to be in the labor force right now, but who are not] now part of the 'structurally' non-employed who we will never see back at work, barring a high-pressure economy of a kind we see at most once in a generation?"

As you can see, the answer to that question matters a lot to how we should think about progress on the unemployment rate going forward.

By Dave Altig, executive vice president and research director at the Atlanta Fed

New Economist

Weekend reading/viewing: Explaining the rise of child labour in Victorian Britain

In Victorian Britain child labour was cheap and plentiful. Few today know much about the era; but they should. Next week Jane Humphries, a Professor of Economic History at Oxford University, is due to give a seminar at the Paris School of Economics about her work on the topic. She published a book on Childhood and Child Labour in the British Industrial Revolution in 2010, and her excellent 2010 Tawney lecture summarises that larger work. The lecture is forthcoming in...

May 17, 2012

New Economist

Economist job of the week: International economist at Standard Life (2 posts)

Edinburgh-based fund manager Standard Life Investments is looking for two economists. According to their vacancy posting: The aim is to have two or more economists, one to focus on OECD economies, especially the UK and Europe, and one to focus on emerging markets, especially China and Asia. These would coordinate international economics coverage with the North Amercian economist based in Montreal. As a result, a considerable degree of travel is probable for one or both positions. The economists would be...

May 14, 2012

About Economics

The Economics of Being an Economist...

People outside of academia may notice that professors move from one school to another from time to time, but what they probably don't realize is how competitive (and organized) the ...

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May 11, 2012

macroblog

Labor force nonparticipants: So what are they doing?

As Dave Altig, Atlanta Fed research director, pointed out earlier this week in this blog post, there is a great deal of interest these days in the labor force participation rate—particularly its level and the direction it's going. The question that seems to be on everyone's mind is how many of the nonparticipants in the labor force can we expect to return to the market. The answer to this question has immediate implications for the unemployment rate (especially if all these nonparticipants were to return to unemployment rolls), and longer-term implications for economic growth—our economy needs workers to fuel production.

The analyses that I can find to date are all primarily focused on a statistical detangling of demographic versus behavioral changes, structural versus cyclical changes, and employment trend versus employment gap debates. But all of this discussion begs the question that my colleague, Melinda Pitts, and I have been investigating: What are these labor force nonparticipants doing? Perhaps an answer to that question will help us get a better handle on which nonparticipants are likely to return to the labor force in the near future.

The Current Population Survey (CPS), administered by the U.S. Bureau of Labor Statistics (BLS), asks labor force nonparticipants about their reason for absence (details of the CPS questionnaire are available from the NBER). The reason given by nonparticipants that gets most of the attention is "discouraged over job prospects." In April 2012, these people accounted for only 1.1 percent of all nonparticipants (41 percent of the marginally attached—those who want a job, are available to work, and searched in the previous year). The vast majority of nonparticipants are absent because of retirement, disability, going to school, caring for household members, or other reasons.

Using the latest survey data we have available (November 2011), we find that most nonparticipants are retired (48 percent); the share who are in school, disabled, or taking care of household members are 18 percent, 16 percent, and 15 percent, respectively; and the share in the category termed "Other" comes in at about 2 percent.

For purposes of better understanding the decline in labor force participation, however, we look at the reasons for absence given by people who leave the labor force. Those who have left the labor force are arguably more likely to return (depending on the reason, of course) than those who have never been in the labor force. A feature of the CPS allows us to track certain individuals from one year to the next, so we are able to identify people who leave the labor force. Chart 1 illustrates how individuals who are not in the labor force—but who were employed or unemployed the previous year—are distributed across the reasons for nonparticipation. The raw data are not seasonally adjusted, of course, so we plot the numbers as a 12-month moving average—this approach does not affect the overall observed trends in the data. In addition, we restrict our analysis here to those between the ages of 25 and 54, since retirement overwhelmingly dominates the nonparticipation decisions of older workers, and schooling dominates the nonparticipation decisions of younger workers.

(enlarge)

Chart 1 illustrates what the labor force participation rates have been telling us. For every reason given for absence, except perhaps "Retired," the number of people leaving the labor force has increased during or after the recession of 2008. The most dramatic increases are seen among those people giving "School" and "Other" as a reason. However, since we are in search of changes in reasons that might be out of the ordinary, especially any significant upward shifts in nonparticipants giving a particular reason during and after the recession, we also look at how these folks leaving the labor force are distributed across the different reasons. This information will tell us whether the number of people giving one particular reason increased disproportionately compared with the other reasons.

Chart 2 plots the shares of all of those leaving the labor force (ages 25–54) giving each reason for their absence. Since the beginning of the recession, there has been a significant shift toward the reasons of "School" and "Other" among nonparticipants who have left the labor force within the previous year. The share levels attained by the reasons of "School" and "Other" are historically unprecedented by the end of the data series. These shifts also appear to have come mostly from a decline in the share of people leaving the workforce to take care of household members (HHcare). This is evidenced through the dramatic drop in the share giving the "HHcare" reason at the same time.

(enlarge)

It is difficult to interpret the implications of the rise in share of "Other" as a reason for nonparticipation among those leaving the labor force, although this category may be capturing some of the discouraged workers. The implication for the rise in "School" is unmistakable, however. With reasonable expectations, these individuals should re-enter the labor force with enhanced—or at least better-aligned—skills that will be able to make a positive contribution to overall economic growth.

By Julie Hotchkiss, research economist and policy adviser in the Atlanta Fed's research department

May 10, 2012

Aplia Econ Blog: News for Economics Students

Who Pays for Online Discounts?

The other day I woke up, looked in the mirror, and decided it was time for a haircut. Rather than picking up the phone and calling the salon where I’ve gone in the past, I checked my email, typed “haircut” into the search bar, and was rewarded with a half a dozen email offers from San Francisco salons selling haircut vouchers at 25%, 50% or even 75% off the regular price. All but one offer had since expired, but I was nonetheless amazed that businesses were offering such steep discounts with such regularity.

Traditional coupons seem to have withstood the test of time as a profitable marketing scheme, but the success of any discounting strategy depends on how coupons are used by both new and existing customers. A primary reason firms offer coupons is to attract new customers, in hopes that after experiencing the quality of the product or service, those who were initially only willing to pay the coupon price will later return and pay the full price. Regardless of whether coupons are offered online or delivered in the mail, however, some will invariably be used by existing customers who would have otherwise paid the full price. Thus, although coupons may generate additional revenues from new customers, they also may reduce revenues from existing ones. While I have been drawn to many new hair salons by their coupon offerings, the widespread availability of these offers has made me far less likely to become a repeat customer. Even if I particularly like a cut at one salon, I’m generally willing to take my chances on the next deal to save 50%.

Fortunately for firms, some customers willingly pay the full price to avoid the hassle coupons entail. This enables firms to separate consumers into two groups based on their willingness to pay, a technique known as price discrimination. If a salon can use coupons to gain customers who are willing to pay less (but instead willing to expend the effort required to use a coupon), and still charge a higher price to customers who are willing to pay more (but won’t bother with coupons), then it can increase the volume of its sales without having to lower the price on all sales. Thus, while it may seem that firms should want their coupons to be as easy to use and widely accessible as possible, the lower the “cost” of acquiring and using coupons, the less ability the firm has to continue charging the full price to some customers.

Admittedly, there are other factors to this new online market for coupons that may counteract the difficulties presented above; some people buy coupons but never cash in on the services, some of the “discounts” may actually reflect artificially inflated original prices, and not everyone is willing to take a chance on a new salon for each new haircut. In the long run, the market will likely determine whether or not this particular variation of discounting survives, but until then, I’ll continue to take advantage of half-off haircuts.

Discussion Questions

1. How might the analysis above be different for different types of goods and services? Is there a difference between offering deals on things people buy impulsively versus things that people buy regularly?

2. How does the fact that people actually have to purchase many of these deals in advance of using them (as opposed to simply clipping a coupon that you may or may not end up using) affect the market? How might this benefit or hurt the firms offering deals?

3. How strong is the psychological component of coupons? That is, how might consumers respond differently to a regularly priced car wash for $30 versus a coupon offering a $60 car wash at 50% off? What does economics have to say about these different price schemes and how they should affect the market outcome?

4. Suppose there are only two hair salons, how could you use game theory to model their payoffs when they each must decide to offer a coupon or not?

May 04, 2012

macroblog

Symmetric goals, asymmetric risks

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Mark Thoma has been hanging out with my boss or at least was at the same conference, where Thoma had a chance to try out his reporter chops:

"I just got out of a press conference with Dennis Lockhart (Atlanta Fed president) and Charles Evans (Chicago Fed president). I can't say there was any real news, but I did manage to ask a question... I asked whether the 2% inflation target was truly symmetric."

Thoma got an answer, though seemingly not one that left him totally convinced:

"Both insisted that the target is symmetric. However, Lockhart said that we know much more about the effects of inflation than deflation, and that preventing deflation was therefore job number one (which doesn't really answer the question). He didn't explain what he is so afraid of if inflation goes up...

"Evans, while explicitly agreeing the target was symmetric, made comments that indicated that it may not be. He said the Fed has not done a very good job of communicating its tolerance around the 2 percent target, both up and down, and they need to improve. But if the target is really symmetric, simply saying that (along with the tolerable range) is all that is required. Talking separately about tolerance for over and under-shooting isn't needed."

Evans' and Lockhart's statements stand on their own, but we've collected some information via the Atlanta Fed's Business Inflation Expectations survey that helps me think about the Thoma question. The chart below plots the answers, collected in the February and April surveys, to the following query: "Projecting ahead, to the best of your ability, please assign a percent likelihood to the following changes to unit costs per year over the next five to 10 years."

Distribution of Respondent Expectations for Unit Costs

The question focuses on unit labor costs in order to elicit responses about what businesses may actually be planning for, as opposed to their guesses about a more abstract concept of overall inflation. The question focuses on expectations five to ten years into the future because the inflation goal of the Federal Open Market Committee (FOMC) is explicitly a long-run objective.

The obvious pattern in these survey responses is their asymmetry to the upside. The most probable outcome, according to the respondents, is that long-term costs will rise in a range that includes the FOMC's long-run inflation objective. But they also put an almost 50 percent probability on annual outcomes higher than 3 percent. Less than 20 percent probability is placed on costs rising at rates of less than 1 percent.

This picture is one of asymmetric risks to the inflation outlook, and as such it is an important element in thinking through policy choices. Symmetry in the sense of having an equal distaste for misses on either side of an objective does not necessarily imply symmetry with respect to the risks of meeting that objective.

To begin with, the Fed does have a dual mandate. Disinflation or, in the extreme, deflation has the potential to be problematic for growth and employment when interest rates are very low. The reason, if you buy the analysis, is that, with no room for rates to move lower, a decline in inflation raises the real cost of borrowing. A higher cost of borrowing restrains spending, creating an additional drag on economic activity in already tough circumstances.

The reverse argument has, of course, been made for temporarily tolerating inflation that is somewhat higher than the long-run objective. But even if you aren't quite sold on the wisdom of that approach—and I'll get to that in a bit—it is clear that, with policy rates near zero, misses to the downside on inflation bring risks to the Fed's growth mandate that are not implied by misses to the upside. Hence President Lockhart's comment regarding the importance of preventing deflation.

So why not respond to this asymmetric risk to growth by taking a chance on higher inflation? That question takes us back to the chart above. Taken at face value, the probabilities reported by our survey respondents suggest that the FOMC has been pretty successful in convincing folks that very low rates of inflation or deflation will not be allowed to set in. Perhaps this conviction is not surprising given the relatively aggressive responses of the committee to the disinflation scares of 2003 and 2010.

But the response to asymmetric risks to growth at low inflation rates may have had the effect of inducing asymmetric risks to the upside with respect to Fed's price stability mandate. Again taken at face value, the results of our business inflation expectations survey definitely imply a one-sided bet by businesses on how the FOMC might miss on its inflation objective. That could well explain why one would be so concerned if inflation rises. Just as there are asymmetric risks associated with below-objective inflation when it comes to the Fed's growth and employment mandate, there are asymmetric risks associated with above-objective inflation when it comes to the price stability mandate.

By Dave Altig, executive vice president and research director at the Atlanta Fed

April 27, 2012

About Economics

And the Baby Nobel Goes To...

The John Bates Clark Medal, awarded each year by the American Economic Association to the most promising economist under the age of forty, is sometimes referred to as the "Baby ...

Read Full Post

April 23, 2012

About Economics

The Best Game Theory Lesson Ever...

Most people who have studied economics know that the Prisoners' Dilemma is one of the classic problems in game theory. But what happens when the theory is ...

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April 13, 2012

Aplia Econ Blog: News for Economics Students

Hunting for a Cheaper Easter Egg

Easter egg decorating was more expensive than usual in Europe this year. According to the Wall Street Journal, the annual spike in egg prices—due to their use in Easter food and decorating—was much higher than usual. Compared to the same time last year, prices were up by more than 75% across the European Union and had more than doubled in Poland, Bulgaria, and the Czech Republic.

What’s different about this year? The beginning of 2012 marked the deadline for implementation of a European Union regulation, first issued in 1999, mandating larger cage sizes for hens. Because egg producers have to buy new cages and then use more space to house the same number of hens and produce the same numbers of eggs, the average cost of producing eggs has increased (assuming that having a larger cage doesn’t increase the number of eggs each hen lays). Consequently, some producers have exited the industry, and the remaining producers require higher prices to produce the same number of eggs.

The graph models the consequences of these demand and supply shifts in the egg market, with D1 indicating the normal, non-Easter demand and S1 indicating the supply without a regulation on cages sizes. “D Easter” reflects the increased demand due to Easter, and “S Reg” reflects the shift in supply due to the regulation on cage size.

Note:
P1: price of eggs outside of Easter season without a regulation on cage sizes
P2: price of eggs during Easter season without a regulation (i.e. what prices would have been during previous Easters)
P3: price of eggs this Easter season—that is, with a regulation on cage sizes
P4: price of eggs outside of Easter season with a regulation on cage sizes

The increased demand for eggs at Easter shifts the demand curve to the right, increasing the price of eggs from P1 to P2. The regulation shifts the supply curve up and to the left, causing a further increase in price (about 75% in this model) to P3. As Easter demand recedes and the demand curve shifts back to normal, the supply curve remains shifted, keeping prices, at P4, above what they were in previous years and, according to this model, above even what they were in previous Easters. The exact price and quantity changes will depend on the size of the demand and supply shifts and the elasticities of the demand and supply curves.

While the hens and those concerned with their welfare indubitably appreciate the improvement of their cages, which now have perches and more bedding, better conditions for chickens means higher egg prices for humans. As always, there’s no free lunch, even (perhaps especially) when it includes eggs.

Discussion questions:

1. How much extra would you pay for an egg produced by hens who got to live in better cages?

2. Assuming that all parts of Europe experience equal shifts in supply (which may or may not be true), what do the larger increases in price in Poland, Bulgaria, and the Czech Republic suggest about elasticity of demand for eggs in those countries relative to Europe as a whole? What other explanations are there for the higher price increase in those countries?

3. California Proposition 2, passed in 2008, requires egg producers in California to provide more room for hens starting in 2015. However, the proposition does not require that California retailers only sell eggs produced in California. What do you think will happen to egg prices in California, egg production in California, and egg production in neighboring states?

April 12, 2012

About Economics

Some Economics Tweeters For You...

There are a lot of economists out there in the Twitterverse, but it can sometimes be difficult to find and follow them in an organized way. Luckily, onlinecollege.org has ...

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March 31, 2012

About Economics

New Articles This Week...

Here are this week's new articles for you to check out:

The Arguments Against Free Trade - Why do people argue against free trade? Are their arguments justified?
Introduction to Natural Monopoly - What are natural monopolies? How, if at all, should they be regulated?

Enjoy!

March 29, 2012

Aplia Econ Blog: News for Economics Students

Kicking through the Ceiling

In the sports world, it has become cliché for people to say that every second counts. However, we expect that phrase to apply on the field, not to the teams trying to get there. Recently, a friend of mine wanted to register his team for a local kickball league. The registration was online only, starting at noon. He had a problem with his credit card that slowed him down, and by 12:02, all the spots in the league were taken and his team was shut out.

Rather than seeing his misfortune as a sign of kickball’s growing popularity, or the quick typing skills of other kickball managers, the first thing that came to my mind was that the market for league entries must be distorted. The league uses public fields that also need to accommodate other sports and high schools, so time on the fields is limited. Since the kickball league will only have a fixed number of hours on the fields, and since the season needs to accommodate a set number of games, it’s fair to think about the supply of league space as fixed, or perfectly inelastic. Most importantly, even at very high market prices, there is no way to add additional teams to the league.

If limited space were the only constraint on the market, then we could find the equilibrium for the market at the intersection of supply and demand, and thus know the equilibrium price where there are exactly as many teams willing to pay as there are spaces in the league. However, since the league filled up so fast, and teams (like my friend’s) that are willing to pay more than the $500 entry fee are unable to join, it appears that there is an artificial price ceiling in this market. Since the league is publicly run, it is likely that someone decided on a “fair” price to charge, so that entries in the league would be open to people of varying incomes. Unfortunately, price ceilings create shortages, that is, they force some people who desperately want the good to go without it. When goods do not sell for the unrestricted equilibrium price, people who value the entries the most do not necessarily receive them. Thus, the shortage caused by a binding price ceiling will end up lowering society’s total welfare.

Can economic theory suggest a solution that would still offer entries at the “fair” price, but also make sure the entries go to the teams that value them the most? Suppose that the league entries were still given out the same way, but once initially purchased by a team manager, an entry could be resold to a team that did not sign up fast enough, if both parties agree. Teams that got entries and value them at least as much as the equilibrium price will hold on to their entries, but teams that were too slow to purchase them initially will be able to buy them from teams who value them less than the equilibrium price. In terms of the final price and quantity of entries, making the league entries tradable will achieve the same result as removing the price ceiling altogether: the market price for the tradable entries will rise to the unrestricted equilibrium price, and the teams that value them the most will end up in the league. However, setting a lower price initially allows some teams the opportunity to buy that otherwise wouldn’t be able to get them. Society’s total welfare is maximized by either making the entries tradable or removing the price ceiling, the only difference is who earns the surplus. As you can see, there are different ways to maximize society’s welfare. Some can be more complicated than others, but they can accommodate different concerns about fairness.

DISCUSSION QUESTIONS:

1) Currently, the market mechanism used to allocate kickball league entries is first-come-first-served. What behaviors does this sort of mechanism encourage?

2) If league entries are tradable and originally given on a first-come-first-served basis, who would attempt to get the initial entries? Is there a chance people who do not want to have a kickball team might apply for a slot? Under a tradable permit system, does the way the entries are initially allocated affect who receives the most welfare?

3) The Coase Theorem is a public economics result that applies to markets where governments want to reduce pollution. It says that the most efficient way to reduce pollution to any desired level is to give firms in an industry permits to pollute the desired amount, and then allow the firms to trade the permits. Consider the similarities between a fixed number of kickball league entries and a fixed amount of pollution by an industry. What results would you expect to see in the market for pollution permits? What effect would creating that market have on society’s welfare?

March 19, 2012

About Economics

If You Want to Learn About the Federal Reserve, Why Not Go to the Source?

Starting tomorrow, Federal Reserve Chairman Ben Bernanke will deliver a series of lectures entitled "The Federal Reserve and the Financial Crisis" as part of an undergraduate course at George Washington ...

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March 15, 2012

About Economics

Adventures in Online Education, Game Theory Edition...

In case you haven't already heard, there is an online game theory course offered by two Stanford University professors starting March 19th. One of the best things about the ...

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March 12, 2012

Aplia Econ Blog: News for Economics Students

Tricky Tax Timing

After my husband signed a contract in October 2011 to work for a Maine hospital, they asked him if he preferred to get his signing bonus immediately or some time in 2012. The seemingly obvious answer was to get the money as soon as possible. As an economist’s spouse, my husband knows that a dollar today is worth more than a dollar tomorrow. However, after completing our 2011 taxes, we discovered that the answer to this question is not as simple as it seems.

While any economist will tell you that the present value of a dollar today is worth more than receiving that dollar in the future, the reverse of this is also true—spending a dollar tomorrow is cheaper in present value than spending a dollar today. To see this, consider the formula for computing future and present values:

Future Value = Present Value x (1 + Interest Rate)^{Number of Periods}
Present Value = Future Value / (1 + Interest Rate)^{Number of Periods}

If we take the money in 2011, we have to pay taxes on it by April 15, 2012; on the other hand, if we take the money in 2012, the tax payment would be delayed by a year, but we’d also get the bonus later.

Further complicating this was our state of residence. In 2011 we were residents of Pennsylvania, so accepting the signing bonus in 2011 meant that we had to pay taxes in Maine as nonresidents (which came out to about 5.5% of the bonus and required that I file taxes in that state when I normally don’t have to). We also had to pay taxes in Pennsylvania as residents (roughly 3%, but you can deduct the Maine payment so you aren’t fully double taxed), and in local taxes to Williamsport, PA (at another 2%). However, if we had waited to accept the payment in the middle of 2012, we would only pay taxes on this income in Maine as residents next April (roughly 8-10% depending on our joint income next year) and not need to file in Maine in 2011.

We were making less money in 2011 than we will in 2012, so we are in a lower federal tax bracket for our April 2012 filing. Our dilemma is whether or not that break plus the value of getting the money today is enough to compensate us for the additional tax liabilities of receiving the money in 2011. Our inability to foresee that taking the money in 2011 would result in a complicated tax situation might have lead us to make a suboptimal decision. Thus, before quickly jumping on the present value bandwagon and taking the money immediately, it’s important to make sure you have complete information about all future consequences of present-day choices.

Discussion Questions:

1. Another factor that led us to take the money immediately is that we plan to use it to pay down some of my husband’s high-interest rate medical school loans. Does this make our decision more or less rational if the interest rate on his loans is above what we could earn in an interest-bearing account? What if the rate on his loan is below what we could earn in interest?

2. Suppose that we received the signing bonus from a hospital in New Hampshire instead, where there is no state income tax for residents or nonresidents. How does this affect our optimal decision?

3. Suppose we were adopting several kids and I was planning to quit my job to become a full-time, stay-at-home mom, putting us in a lower tax bracket in 2012. How might this affect our decision?

March 06, 2012

About Economics

S**t Happens, the Economics Version...

There's an old joke that all religions can be explained using the "s**t happens" saying. Economist and comedian Yoram Bauman has brought this idea to the economic sphere with ...

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February 29, 2012

About Economics

New Articles This Week...

Here are this week's new articles for you to check out:

The Federal Reserve System - what is the Federal Reserve system and what exactly does the Fed do?
Economist Ben Bernanke - Who is Ben Bernanke and what does he do?
Economic Equilibrium - How do supply and demand come together to reach an equilibrium price and quantity?
Calculating Economic Equlibrium - How can a supply and demand equilibrium be found mathematically?
The Prisoners' Dilemma - What is the prisoners' dilemma and what is the equilibrium outcome of the game?
The Costs of Inflation - What are the economic costs of inflation?
The Efficiency-Wage Theory - What is the efficiency-wage theory and how does it affect labor markets?

Aplia Econ Blog: News for Economics Students

Land or Dishes? Dishes, please!

On a recent visit to the Los Altos History Museum with my daughters, I found myself hoping that one day they will appreciate my favorite exhibit: a replica of the wheel-of-fortune used by the Los Altos Land Company in the 1930s. During the Great Depression, the company had a difficult time selling plots in the sparsely-populated apricot orchards that later became Los Altos. That land is now part of the Silicon Valley and among the 100 wealthiest communities in the country, but nobody could have predicted that success back then. To offload the property, the struggling company conducted a promotional contest that took place in San Francisco movie theaters. Participants could spin the wheel to win free stuff, including a choice between a set of dishes and a plot of land in Los Altos. A plot of land was about the price of a set of dishes back in the day and according to the exhibit, most people chose the dishes.

What determined this choice? Economic utility theory tells us that the choice between land and dishes is determined by the marginal rate of substitution between them. However, without more information about the consumer, we cannot deduce the winner’s utility function from owning one more set of dishes or one more plot of land by simply knowing that the prices are equal.
Asset pricing theory—an economic theory that attempts to understand the prices of uncertain payments—can give us more insight into the matter. Land and dishes are assets with different properties. Although both could be viewed as durable goods, the set of dishes qualifies as a consumer good, whereas land is largely treated as investment. This allows us to view this scenario as the choice between consumption and investment (or saving); a decision between the two is determined by the relative prices of the two goods, the utility of the consumption good, future returns on investment, and the rate of future discounting (or the degree of impatience). Even without assumptions about the impatience and preferences for fancy dishes, the seemingly naïve choice of the dishes was fully rational given that investment in farm land did not promise great returns at the time.

Now suppose that the lottery winners knew that in 40 years the area’s booming economy would lead to skyrocketing land prices. As a rational economist, if I was a winner at such an event, would I choose dishes or land? My first reaction is: “Of course in this case, I would pick the land!” On the second thought, however, I realize that there is a very good chance that I still would choose the dishes. In troubled times like the Great Depression, both the perception of risk and the demand for liquidity increased, making the dishes a clear winner. Because a set of dishes could be considered a durable good, it could serve as an asset functioning as a store of value. Also, it is easier and less costly to sell or exchange dishes than a plot of land, thus making dishes a more liquid asset than land. Thus simply knowing in 1931 that the Los Altos land would appreciate in a few decades does not imply that it could be immediately converted into cash when needed. In tough economic times, survival today is often more important than planning for the future. Therefore, I would likely choose in favor of current consumption despite the high expected return on investment.

Discussion questions:

1. What piece of information about the dishes and the plot of land is critical in my decision-making?

2. Suppose that land is as liquid as the dishes. How would this affect the choice between the land and the dishes?

3. Would the same economic reasoning apply if it were a dinner instead of the dinnerware?

4. What would be your choice today if you were presented with a similar set of alternatives? Justify why this choice is the same as or different from most people’s choice for dishes in the 1930s.

February 27, 2012

About Economics

What's an Oscar Worth? Economists Can Tell You...

It's probably not shocking that, even when thinking about entertainment and the arts, economists are always thinking at least a little about the numbers behind the art. The Oscars ...

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February 13, 2012

Aplia Econ Blog: News for Economics Students

Househunters Meets Econ

A famous quip suggests that if you could teach a parrot to say "supply and demand," he could replace 90% of the world's economists. However, what economics is really about is analyzing the decisions people make in the face of scarcity and uncertainty. While the supply-and-demand model does have a wide variety of applications, it also comes with a laundry list of assumptions that give the model its power and tractability. One of these assumptions is complete information about the prices and quality of various goods. But one thing is for sure—in the housing market, there is certainly imperfect information about current and future market conditions.

For example, last week I found myself in an interesting predicament while house hunting. I was faced with two options: I could make an offer on a house currently on the market (going forward we’ll call this House A), with the belief that it was priced-to-sell and thus would likely be unavailable in the coming weeks. Or I could wait a month for more houses to come on the market, thus foregoing House A and incurring additional costs, like the effort to find new houses on the market and travel time to look at houses. This situation is exactly the type of scenario studied by search theory economists.

Search theory is a branch of economics that models markets with search frictions. In this case, “frictions” are the unknowns about what kinds of houses will come on the market in the coming months. You are faced with the choice of either accepting the good you’ve found today (House A) or throwing that choice away and paying a cost to find another choice tomorrow (House B). With some standard assumptions regarding utility and the distribution of houses on the market, the optimal way to shop is to use a reservation strategy; this means that you continue to shop until you find a house that makes you equally or more happy than that of the “reservation house”. This is the house that makes you exactly indifferent between continuing to search and buying it.

Thus, you can imagine my excitement surrounding house hunting. Not only is it fun to peruse the web and schedule showings, but house hunting is a great example of where supply and demand falls short, making room for more appropriate economic models like search theory. In most cases, consumers don’t know with certainty where goods can be found, how much they cost, and if they’re available; rather, they must spend time and money searching for these goods. When people ask me what kind of economics I like to study, my typical response is “the economics of shopping.” Search theory is simply the economic tool I use to describe it.

Discussion Questions:

1. How would you expect the time you have to search for houses to factor into the characteristics of your reservation house? In other words, do you think your level of pickiness will change if you had 3 months left to search versus 12 months?

2. Suppose that you didn’t have to worry about losing a housing option if you decide to search another day. Search theorists called this having “recall” over previous draws. How do you think this affects your “reservation house” if you’re searching over an infinite time period. How about a finite time period such as 12 months?

3. How has the internet alleviated search frictions in the housing market?

4. What other markets might be better explained using search theory as opposed to the standard theory of supply and demand? Why?

January 31, 2012

Aplia Econ Blog: News for Economics Students

Getting Incentives Right

In September 2010, a natural gas pipeline owned by Pacific Gas & Electric company (PG&E) ruptured in a subdivision of San Bruno, California, starting a fire that killed eight people, destroyed 53 homes, and damaged 120 more. Because San Bruno is only a few miles from the Aplia offices, I took particular notice when I saw the following headline in the San Francisco Chronicle: “PG&E incentive system blamed for leak oversights.”

According to the Chronicle article, PG&E had delegated leak survey crews to find leaks on its pipelines but had been paying bonuses to “supervisors whose leak survey crews found fewer leaks and kept repair costs down.” Two years before the fire, PG&E ended the policy of bonuses and, worried about the consequences of the bonus system, began to redo leak surveys. The subsequent surveys revealed “many more” leaks than had originally been reported.

This result doesn’t come as a surprise if we think about a supply curve for undiscovered leaks -- leaks that exist but are not discovered by the survey crew. While it may at first take some mental gymnastics to think about undiscovered leaks being “supplied,” when you contemplate how the bonus system would effect the effort and diligence of leak survey crews, you can easily imagine the usual upward sloping supply curve like the one on the graph:

As with any upward sloping supply curve, the more that is paid for undiscovered leaks, the more “undiscovered” leaks will be supplied. Equivalently, fewer “discovered” leaks will be supplied.

(Note: The analysis doesn’t rely on the definition of the quantity supplied used here. A similar analysis based on the supply of discovered leaks, which may be easier to think about, leads to the same upward sloping supply curve. The difference is that the bonus system makes the payment for discovering a leak negative, and to account for this, the supply curve has to start at a price below zero (that is, even at a price of zero, there would be leaks discovered, so the price at which no leaks would be discovered would be below zero.)

This analysis doesn’t mean that incentive-based pay is a bad idea. It would make sense to pay to anyone responsible for preventing leaks a bonus based on fewer discovered leaks (assuming that the people in charge of discovering are different from the people in charge of preventing). What this analysis does mean, though, is that you have to match the incentives to the result you want. The price of mismatching incentives is, in some cases, very high.

Discussion questions:

1. Think of an incentive system that would have resulted in fewer leaks going undiscovered. What would be some of the drawbacks of this incentive system, particularly in terms of cost?

2. In the United States, students take standardized tests at various points in their academic career, and in some states, their teachers face dismissal if their students perform poorly on the exams. In those states there have also been reports of teachers fixing their students answers on the tests, and complaints that teachers focus on “teaching to the test,” to the exclusion of skills that cannot be tested. How are the incentives faced by teachers like the incentives faced by PG&E’s leak survey crews? How are they different?

3. What other situations do you know of where an incentive system produces undesirable results? Would the results improve more by changing the incentives or by removing the incentive system altogether?

January 19, 2012

Aplia Econ Blog: News for Economics Students

Low Carbs, High Fat…High Prices?

"One box will cost you $740, but if you don’t like it, you could try your luck with the Russian smuggler down the street." There are plenty of goods that might be sold based on discussions like that, but would you ever expect to hear that said about butter? Residents in Norway are currently facing a market like that, according to recent reports.

A recent diet craze emphasizing high fat and low carbs has caused a change in Norwegian consumer preferences. Fads and trends will change the equilibrium price and quantity observed in a market by shifting the demand curve. In this case, the popular new diet increased the demand for butter (shifting the demand curve to the right), while leaving the supply curve unchanged. The standard supply and demand model says a rightward shift of the demand curve leads to an increase in the equilibrium price and quantity consumed. Both of those were observed in real life, as well.

Nonetheless, changes in tastes rarely result in price fluctuations of this magnitude, so how do economists explain why the cost of butter went so high? We see increases (and decreases) in demand every day, but prices rarely swing so wildly. A closer look at the details sheds some light on the source: the government is preventing the free market from doing its work. As the Swedish Dairy Association (Svensk Mjölk) noted, Norway has “very restrictive trading policies, borderline protectionist.” That means that the Norwegian government’s policies make it very difficult (or even impossible) for foreign goods to enter the domestic market.

Though the government does this in an effort to protect Norwegian producers, protective policies like those block markets from working efficiently. When a “shock” to supply or demand occurs (in this case, an increase in demand), protectionist policies prevent foreign producers from entering the market to capture new profits. Because the trend hit quickly, and the production time for agricultural goods isn’t exactly short (you can’t just go out and rent an extra 200 cows overnight), the Norwegian market for butter appears to be relatively inelastic in the short run (that is, even a small percentage change in the quantity supplied is associated with a large percentage change in price). If this trend in preferences continues, prices will remain high until producers have time to react by expanding their farms to accommodate more livestock, hire more workers, and install more processing equipment.

Does that mean that Norwegian consumers are going to continue facing these brutal prices for the coming months? Only time will tell, but if prices persist, it would be a testament to a population stubborn enough (or wealthy enough) to stick to the latest trendy diet, and a government dedicated to hard-line international policies, even at the cost of its own citizens’ welfare.

DISCUSSION QUESTIONS:

1) A change in preferences isn’t the only way that the demand for a good can change. What are some other factors that could cause the demand for butter to increase?

2) Rather than demand returning to where it was (the end of interest in the fad diet), the equilibrium price of butter could also decrease if supply shifts. Which direction would the supply curve need to shift for that to happen? What would happen to the equilibrium quantity? What are some ways that the supply could shift in that direction?

3) Suppose the Norwegian government feels pressure to help lower butter prices. Propose a policy that would help lower prices in the market. Is there a policy that the government could use to generate revenue for itself while lowering the price of butter?

January 17, 2012

Aplia Econ Blog: News for Economics Students

A Penny Saved Is...

Which scenario would you prefer: (a) losing $30, or (b) losing $30, then losing $90, then regaining the original lost $30? While in most circumstances the first option is the unquestionably preferable, I recently found myself in a situation favoring the latter.

As a member of the Marin Sun Farms “meat club CSA” (community supported agriculture), I order a custom package of meats from a local farm that is delivered (frozen) once a month to a pick-up location near my home. While this arrangement offers me an excellent supply of local meat at a discounted price, the difficulty is remembering the monthly pick-up time. As disclaimed on the Marin Sun Farms website, “Packages not picked up promptly will be forfeited.”

This past Sunday I was sifting through emails when I discovered buried amongst online coupon offerings, eStatements, and a “Hello!” from mom, a reminder email sent the previous Thursday: “Pick up your CSA box today!” My heart sank as I pictured my box of grass-fed beef, lamb, and chicken slowly defrosting, decomposing, and ultimately being discarded. It had been a small shipment, only $30 worth, but nonetheless, I cringed at the waste.

Monday morning I awoke to another minor financial misfortune: a $90 parking ticket proclaiming my violation of section VC22500E – DRIVEWAY BLOCKING. D’oh! I knew when I parked that the rear of my car extended a few inches beyond the curb and into the neighboring driveway, but after half an hour searching for a spot I decided to take my chances (always thinking in economic terms, I figured that the expected cost of a ticket—equal to the true cost times the probability of actually receiving a ticket—was outweighed by the benefit from no longer looking for parking).

Chagrined by my back-to-back oversights, I called the number of the CSA pick-up location, just in case. To my surprise and relief, the woman in charge had managed to store my meat—not their usual policy—and I picked it up later that day.

By Monday night I had experienced the aforementioned $30 (perceived) loss, $90 loss, and $30 (perceived) gain, yet I felt better than I had felt on Sunday night when then I perceived only the $30 loss of meat. This may have had something to do with the order of events (after internalizing the loss of the ticket in the morning, the gain of $30 remained more salient at the end of the day), but I think it had more to do with how I perceived the true value of each loss. To a meat-loving economist, a discarded order constitutes a clear waste of resources—$30 of value—gone. The $90 parking ticket, on the other hand, represents a transfer of resources from me to the city of San Francisco, which ostensibly will put the money to use in the creation or maintenance of the public services I enjoy.

In introductory economics, we make a similar distinction between the deadweight loss and government revenue generated by taxes. Deadweight loss reflects the decrease in benefits to society (producers and consumers) resulting from fewer total transactions taking place. Economists view this loss to consumers and producers as different from the revenues a tax generates. Although both come at the direct expense of consumers and producers, the latter provides governments with the means to furnish public goods and services which indirectly benefit consumers, while the former—like rotten meat—is just no good.

Discussion Questions:

1. Why else might the $90 parking ticket be less painful than losing the meat shipment? Think about the “value” I got from time saved by parking illegally.

2. How does risk aversion factor into the decision of whether it’s worth taking the chance of doing something illegal? Consider a person who frequently speeds and occasionally gets speeding tickets. Ignoring the potential effects on others, might this too be a rational decision?

3. Consider other instances in which financial losses of the same dollar value might be felt in different ways (e.g. forgetting to take a $20 bill out of your pocket before washing it versus accidentally leaving an extra $20 as a tip on a restaurant bill?)

December 13, 2011

Aplia Econ Blog: News for Economics Students

The illogicality of plastic ear-swabs (or why some savings decisions make more sense than others)

While perusing the aisles of Safeway the other day, I pondered the rationality of my grocery selections. I bought the Q-tips brand ear swabs instead of kind with plastic stems which would have saved me about $0.70, but reluctantly opted not to buy my favorite kind of chocolate because it was selling for $3.99 rather than the frequent sale price of $2.99 a bar. I selected the grape tomatoes at $1.99 a carton instead of my preferred cherry tomatoes at $3.99 a carton, but bought a fancy bottle of salad dressing for $4.59 in spite of a myriad of cheaper alternatives.

Why did I spend the extra money on Q-tips when I could have used it to buy chocolate instead? Why forgo the expensive tomatoes but not the pricier salad dressing? The answers lie largely in the economic concept of elasticity. Price elasticity of demand describes how much a change in the price of a good affects the quantity demanded for that good. If a good has very elastic demand, then a small change in the price will have a large effect on how much of that good is demanded. Conversely, the price of a good with inelastic demand can rise substantially without having much effect on the quantity demanded. For example, my choice to stop buying chocolate bars in response to an increase in price suggests my demand for them is relatively elastic.

Cross-price elasticity of demand refers to how much a change in the price of one good affects the demand for another good. The switch from cherry to grape reflects a positive cross-price elasticity of demand, because my demand for grape tomatoes increased when the price of cherry tomatoes rose. This illustrates one determinant of elasticity: the availability of viable alternatives or substitutability. Although I do prefer cherry tomatoes to grape, it is a slight preference, so when cherry tomatoes are not on sale, I substitute grape tomatoes for cherry and save $2.

Another determinant of a good’s price elasticity is the percentage of one’s overall budget that a good requires. I eat a lot of chocolate; therefore, only buying it when it goes on sale adds up to far more savings over time than choosing to buy the generic Q-tips, which I only buy every six months or so. Because I find off-brand Q-tips mildly frustrating (the cotton doesn’t seem to stay properly attached), choosing the off-brand to save $1.40 a year would probably be one of the least worthwhile money-saving sacrifices I could make.

A third determinant of price elasticity is necessity. While food in general is perhaps the most necessary good I buy, my actual need for chocolate is (somewhat) less pressing. Reluctantly, I postponed my chocolate purchase in hopes that next time it would be on sale.

While normal people do not consider the elasticity of their demand for various grocery items, their actions are inevitably guided to some degree by the prices of alternatives, the weight of the expenditure in their overall budget, and the necessity of the good. But why stop at the checkout line? While it might be most natural to illustrate the elements of elasticity with groceries, economists believe the same decision-making behaviors apply when people buy any good or service. So, next time you’re considering whether to sacrifice or splurge on anything from cupcakes to cell phone plans, remember that some savings make more of an impact on your budget than others.

Discussion Questions:

1. Instead of talking about one type of tomatoes versus another, how does my demand for Roma tomatoes compare to my demand for tomatoes in general? How does a narrow or broad definition of a good relate to its elasticity?

2. The income elasticity of demand refers to the change in the quantity demanded that results from a change in the buyer’s income, rather than the price of the good. Suppose I got a raise. How would a dramatic increase in my income affect my demand elasticity for an expensive treat, like steak? Would the effect be the same on all goods? What about my demand for ramen noodles?

3. If the producers of a good have conducted research that suggests demand for their good is highly elastic, how might this affect their pricing decisions?

4. Recently, there have been “sin taxes” proposed in some states on a number of goods, including artificial tanning, tattoos, and sugary sodas. Economists call these Pigouvian taxes. They are taxes placed on goods that the government believes are socially unappealing. Suppose the demand for artificial tanning is very elastic, while the demand for sugary soda is not. Compare the effects of two equal sized taxes on the equilibrium market price, the equilibrium quantity consumed, and the tax revenue raised.

December 02, 2011

Aplia Econ Blog: News for Economics Students

Not-so-sunken costs?

I recently had to decide between going to a concert for which I’d already bought a ticket and attending a dinner party with friends. Initially I was compelled to “get my money’s worth” by going to the concert (it was too late to sell the ticket to someone else), in spite of the fact that I would have preferred to go to the dinner (if I hadn’t bought the ticket). According to the economic theory of sunk costs, however, choosing to go to the concert under these circumstances would have been irrational.

Once a good or service has been paid for, the future costs and benefits of actually making use of the purchase should be compared to the future costs and benefits of alternative options—the cost of the purchase, paid in the past, is “sunk” and should not factor into the decision. Suppose that neither the dinner nor the concert would cost me any additional money, but I predicted the enjoyment I would get from the dinner would exceed the potential enjoyment from the concert. Because the expected future benefit minus the (nonexistent) future cost of the dinner exceeded that of the concert, I chose to go to the dinner.

Choosing to ignore sunk costs, however, is not always easy, in part because it can be difficult to distinguish situations in which the cost is truly sunk from those in which it shouldn’t be written off entirely.

Suppose instead I had been asked to bring a bottle of wine to the dinner. In that case, the fact that I had already bought the ticket meant that I was choosing between a concert that would cost no additional money, and a dinner that would cost me the price of a bottle of wine (say $15). Though I would have had a definite preference for going to the dinner and paying $15 for wine over going to the concert and paying $20 for a ticket, it could have been the case that I preferred going to the concert (at no additional cost) to going to dinner (and spending additional money). Although the $20 I’d spent on the ticket was gone either way, it had made one of the options free without affecting the cost of the other option. This would be particularly meaningful if I had a monthly budget for semi-luxuries like concerts and wine, and having spent $20 on the concert, I couldn’t justify spending $15 on a bottle of wine.

The moral of the story is that while you should never consider the “sunk cost” in itself when making decisions, it is relevant to consider how the sunken payment may have altered your current set of options.

Discussion questions:

1. Think about how this kind of analysis would be important to a company that has already invested considerable capital in a project, but later finds a different project that would have been better to invest in. When deciding whether to abandon the first project to invest in the second, how should the money already invested in the first project affect or not affect the decision?

2. Can you think of examples of sunk costs in your life that you might be tempted to not ignore because it can be difficult psychologically to not use things you’ve purchased?

Aplia Econ Blog: News for Economics Students

Oz Economics: Will your silver shoes carry you over the desert?

For centuries, gold and silver served as money, but not anymore. Silver went out of circulation in the late 19th century. Gold was effectively banned from circulation in the United States by the Gold Reserve Act of 1934. The last attempt to revive silver in the US as a form of money was made by the Populist Party. In particular, W. J. Bryan, a three-time presidential candidate on the cusp of the 19th and 20th centuries, argued that adherence to the gold standard tightened the money supply and consequently limited access to credit. He claimed that this hurt the entire economy, especially the Midwestern farmers suffering from the deep and prolonged recession of 1890s.

The Populists’ solution was bimetallism – the use of gold and “free silver” – to increase the money supply, which in turn would help the farmers. Their efforts failed, but the debates of those days are immortalized in L. Frank Baum’s The Wonderful Wizard of Oz. According to Henry Littlefield’s famous interpretation of Baum’s fantasy, Dorothy’s silver shoes symbolize the silver money that had to be added to the gold – the yellow brick road – in order for Dorothy’s quest to succeed.*

Money is a special asset. It exists in multiple forms and serves different functions. For example, commodity money such as silver or gold has an intrinsic value, whereas fiat money or paper money has value only as a result of government decree or law. To be used as money, any asset (commodity or fiat) must fulfill the following requirements: It must serve as a medium of exchange, a unit of account, a store of value, and a standard of deferred payment. In the modern world, it’s much easier to use paper money than silver or gold coins or bars. However, silver and gold outperform paper money when it comes to the store of value function, because inflation can potentially turn paper money into useless pieces of paper.

Gold is widely used for inflation hedging, which means that when fiat money loses value due to inflation, gold retains its value. The importance of gold as a store of value is underscored by historical price data that shows spikes in the periods of greatest macroeconomic uncertainty. Although not used as a universal medium of exchange, gold still remains an important asset. Its price among pivotal financial market indicators is on a par with Dow Jones and other major stock price indices and Treasury bills.

What about silver? In the past hundred years, it has never come close to gold in importance. The use of silver for investment was negligible until 2008. It was mostly used for industrial applications and for jewelry. Recently, the role of silver has been changing as it becomes an increasingly attractive investment. In 2010, its use as an investment commodity increased to 17 percent of total production. This resulted in an increase in the demand for silver, and consequently, a higher price.

Since then, silver has been appreciating steadily relative to gold. In September 2010, the price of silver was about $20 per ounce, whereas the price of gold was approximately $1,250 per ounce. This yields the gold-silver price ratio of 62.5, which is close to the average for the past two decades. One year later, in September 2011, an ounce of silver was traded for $30 and an ounce of gold for $1,800. Thus the gold-silver price ratio fell to 45. This suggests that silver has begun to function as a store of value and is creeping up on gold. Moreover, current technology significantly increases the liquidity of both gold and silver as assets. Not only is it possible to open an online storage account without leaving your desk, but it is also possible to trade silver and gold shares online without knowing where the metals are physically located.

The quest for a safer investment didn’t just increase the demand for gold, it also dragged silver back into the spotlight. It restored, even if temporarily, silver’s position as a store of value. Even if modern investors don't believe that a pair of silver shoes alone will carry them over the desert of economic instability, they are certainly interested in giving them a try.

* Dorothy’s ruby slippers in the MGM classic movie make no sense economically. Ruby replaced silver for the film because the red ‘popped’ more in the new Technicolor technology.

DISCUSSION QUESTIONS

1. How would expanding the money supply have helped poor farmers at the turn of the 20th century?

2. Would you be willing to accept a gold or silver bar as a means of payment today? Do you think your favorite store at the mall would? Based on your answers, would you say precious metals serve as an effective type of money in our modern society?

3. If two similar investments (like gold and silver) show very different rates of return over the same time period, do you think the investment market is in equilibrium?

November 03, 2011

Aplia Econ Blog: News for Economics Students

NFL Concessions Meet Economic Tradeoffs

While at the Patriots-Steelers NFL game earlier this season, I made a classic economics observation: tradeoffs are everywhere. It was partway into the second quarter, and dinner time was approaching. Because our seats were up in the highest section possible, this meant short lines at the concession stands, but the quality of food available was poor. The economist in me couldn’t help but see the natural connection to consumer theory, specifically indifference curves.

Indifference curves express how much utility, or happiness, comes from various combinations of goods. Any two points along the same indifference curve must represent two combinations of goods that make you equally happy. Additionally, points on different indifference curves represent different levels of happiness. In terms of the shape of indifference curves, economists make standard assumptions, such as more is better and averages are preferred to extremes. However, consider what the indifference curve mappings would look like if the goods being represented are quality of food and queue length (that is, the length of time you expect to wait in line .)

In this case, a long queue length is undesirable (economists call this a “bad”).That means that if you’re going to tolerate a longer line, the food quality must improve for you to be equally well off; this translates graphically into the increasing shape of the curve above. Also, for any given queue length, a higher quality of food makes you better off, so the level of happiness represented by IC2 must be higher than that of IC1. Finally, because averages are still better than extremes, the bowedness of the curves must be in the northwest direction. This is illustrated on the following graph:

A and C are two possible consumption bundles, while B represents the average of this bundle. Because B is preferred to A and C and you know that consumers are happier with better food and shorter lines (the southeast direction), the curve must be bowed in this way.

As you can see from the graph above, the choice as to whether or not it makes sense to travel throughout the stadium for better food is a simple consumer choice problem. My optimal decision rests on the relative happiness I get from higher food quality versus not waiting in line. What did I choose? A simple burger with french fries in exchange for a short line, so I could watch Brady and the Patriots blow it!

Discussion Questions:

1. What if instead of modeling “queue length” on the vertical axis, you want to show the indifference curves between “food quality” and the “amount of the game you watch from your seat.” How would the shape of the indifference curves change? Which direction represents a higher level of happiness from one curve to another?

2. Suppose that the value of watching the game diminishes because your team is crushing the opposition. How would this change the shape of your indifference curves between queue length and food quality?

3. What if averages are no longer better than extremes? How would that alter the shape of the indifference curves shown above?

August 30, 2011

Aplia Econ Blog: News for Economics Students

Education Regulation

Across the country, students are returning to higher education institutions for the start of another semester, except it seems that the federal government may not want some of those students in classes. This summer, the Department of Education announced new rules that will limit federal loans and grants available to for-profit colleges in order to change the way these institutions do business. These rules base funding on how educational programs meet performance goals, and they have already had an effect; new student enrollments have fallen by nearly 50% at the University of Phoenix, the largest for-profit college.

While both the analysis of the performance policy and larger questions on the economic value of subsidizing education are relevant for economic debate, perhaps it is worth taking a step back and considering something much simpler: holding the initial funding constant, there was a market where buyers (potential students) were happy to trade with sellers (for-profit colleges), but the government chose to intervene and prevent trades that the market otherwise would have facilitated. Most of the time, economists endorse laissez-faire policies, literally “hands off.” This is because government intervention often distorts the market equilibrium and leads to lost surplus, thus lowering welfare for society as a whole. However, sometimes economic theory endorses government intervention, because some policies can correct for market failures and actually raise social welfare.

What are some examples of government intervention that can be economically beneficial?

1. Tragedy of the commons - When public resources are freely available to everyone, they can become overused and permanently damaged. When a government requires fishing licenses to fish in public streams, the intervention limits usage and preserves the environment by preventing over-fishing.

2. Externalities - In some cases, while the private costs and benefits apply to individuals, the consumption of goods can have far-reaching effects on society as a whole. By imposing fines for pollution, the government can make private firms internalize the cost to society of damage done by production.

3. Asymmetric Information - When sellers know more about their product than consumers can reasonably find out, they could exploit that information to rip consumers off. Governments can step in to level the playing field. Consider when a county’s boards of weights and measures routinely calibrate supermarket scales.

4. Incomplete Information - Economists typically assume complete information when analyzing markets. Welfare loss can occur if consumers do not know exactly what is for sale, or if a good fits their needs.

Returning to the case of new rules on for-profit education, perhaps the government has justified its intervention by suggesting that some students don’t know what they are getting themselves into, and are buying a product they don’t need or can’t use. In some cases, the federal government, along with four individual states, is taking things one step further. In August, they filed a lawsuit against another for-profit education company (Education Management Corporation), charging them with fraud. Based on information from whistleblowers, the government is charging:

"The company had a ‘boiler-room style sales culture’ in which recruiters were instructed to use high-pressure sales techniques and inflated claims about career placement to increase student enrollment, regardless of applicants’ qualifications. Recruiters were encouraged to enroll even applicants who were unable to write coherently, who appeared to be under the influence of drugs, or who sought to enroll in an online program but had no computer."

While the fraud case is early in the legal process, the metaphorical jury is still out on the government’s new policies. Regardless of the legal outcome, it’s important to consider the costs and benefits to the parties involved when the government considers intervention.

DISCUSSION QUESTIONS:

1) Will the government regulations related to for-profit education cause a pareto optimal change? Who is better off under these regulations? Is anyone made worse off by these new laws?

2) What should the government consider when debating laissez-faire policies versus intervention?

3) Do you think the government regulation of for-profit colleges is appropriate? Is this a positive or normative question?

4 ) How would the market react if students had to pay for their education entirely out of their own pocket, rather than receiving some government aid? Would you expect the same level of regulation to be introduced?

Aplia Econ Blog: News for Economics Students

Crossing the Bridge: Do the Wealthy Live Longer?

A recent study on longevity provides intriguing data on life expectancy (LE) in the United States. Despite the U.S. having the highest health expenditure per capita, life expectancy in the US trails that in most other developed countries.

Life expectancy is a measure of a nation's or community's health that summarizes current mortality statistics by answering the following question: Assuming all current conditions remain unchanged, how long could children born this year be expected to live on average? In 2007, the US ranked 37th in the world in terms of LE at birth, with 75.6 years for men and 80.8 years for women. Across US counties, however, LE ranged from 65.9 to 81.1 years for men and 73.5 to 86.0 years for women. To assess the extent of these disparities, the authors used a benchmark based on ten countries with highest LE in the world. Then they ranked each US county based on how many years it is behind or ahead of the benchmark. For example, if county A has LE of 75 years and it took the benchmark countries years ten years to go from LE of 75 years to the current average of 80 years, then county A is ten years behind the benchmark.

The analysis determined that very few of the US counties are ahead of the benchmark, and most are behind. Some counties are decades behind, ranking close to less developed countries such as Peru and El Salvador. What is perhaps most surprising is that large disparities exist even between neighboring US counties. Take for example two California counties, both in the San Francisco Bay Area: Santa Clara, home to Stanford University, and Alameda, home to UC Berkeley. In 2007, based on LE for men, Santa Clara county was almost a decade ahead of the international benchmark and Alameda county was at least five years behind. An allegory comes to mind: By crossing the Bay Bridge, we jump 15 years back in time! For women, the time travel would be shorter, a decade.

The authors of the cited study are health researchers primarily interested in demographic factors and life style choices that create medically preventable deaths caused by obesity, smoking, and alcohol. Economists have a different interest in these statistics: the link between wealth and health. In 1975, demographer Samuel Preston first reported a positive relationship between GDP per capita and LE. The graphical representation of this relationship is now called the Preston curve. Two properties of the Preston curve are of special interest to policy makers: (1) Life expectancy at birth rises quickly at low levels of per capita income but flattens at high levels of income; (2) The Preston curve shifts upward over time, which is largely explained by improvements in health care technology. The shape of the Preston curve resembles that of a production function, suggesting that health, measured by LE at birth, is a product of a healthcare system where the only input of interest is per capita income.

Some factors that produce health from wealth operate on individual level. Higher income leads to better nutrition, which in turns creates better health outcomes, especially in children. Some operate on the community level (sanitation and other public health measures), and some on the national level (health care system coverage and production of medical knowledge). However, the causality in the Preston curve is unclear, and an alternative explanation is possible: The Preston curve may reflect an impact of health on income. That is, healthier people are able to work more and thus earn more, which enables them to take a better care of their children. Healthier children spend more time studying and thus become more productive workers, etc. This may explain the steeper slope of the Preston curve for the less developed countries where mortality is likely to affect productive members of labor force, while in developed countries, mortality largely affects retirees.

Regardless of the interpretation, the Preston curve remains an empirical observation that holds across countries and suggests that the link between health and income is more important for developing countries than for developed ones. In the case of the United States, does it matter at all? Quite a bit, it turns out. This graph shows a strong relationship between average personal income and LE across California counties. Specifically, average income per capita in Santa Clara county is 16% higher than in Alameda county, $36.5K versus $31.5K. In 2007, LE in Santa Clara county was 80.6 for men and for 83.9 women while in Alameda county it was 77.7 for men and 82.3 for women. So, the Preston curve is relevant even at the county level. Holding all else constant, baby boys and girls born in a relatively wealthier county are expected to live longer.

Discussion questions:

1) Why are researchers from different disciplines interested in life expectancy statistics?

2) What factors might be responsible for the US ranking 37th in the world?

3) What factors could be responsible for the differences in LE in two neighboring US counties with similar demographics and health care systems?

July 19, 2011

Aplia Econ Blog: News for Economics Students

Raising the Roof... on the National Debt

For the past month, House Republicans and the White House have been in a bitter standoff over the national debt ceiling, the legal limit to borrowing that the U.S. government imposes on itself. The law establishing the ceiling has been in effect since 1917, but the ceiling has been raised many times over the past century. The current limit is set at $14.3 trillion. Government spending would have exceeded this limit on May 16th, but the U.S. Treasury has enacted emergency measures that will keep the government and its lenders funded until early August. Failing to increase the debt ceiling could lead to the U.S. being unable to fund military salaries, pay for programs like Medicare, or make interest payments to creditors. But increasing the debt ceiling won’t be easy either.

In the worst-case-scenario, an agreement to raise the debt ceiling would not be reached, and the U.S. government would risk defaulting on interest payments to lenders. The United States government has consistently served as a safe haven for lenders looking to store funds; historically it has never missed a payment. As a result of this reliability, the U.S. economy has been able to enjoy relatively low interest rates. If the U.S. were to consider defaulting on its loans, investing in the U.S. government would become riskier. To attract borrowers and accommodate for the increased risk of not being paid back, real interest rates would have to rise.

John Maynard Keynes wrote in The General Theory of Employment, Interest and Money that aggregate demand (composed of consumption, investment, and government expenditures) is the main determinant of an economy’s level of output. Investment spending, such as the purchase of a new home, is typically financed through borrowing. As real interest rates rise, borrowing becomes more expensive. Because investment is a component of aggregate demand, an abrupt decline in investment would theoretically shift the aggregate demand curve inward as in the graph below. With the US economy struggling to overcome the recession caused by the 2008-2009 financial crisis, a reduction in output and the corresponding fall in employment would certainly be viewed as an unfavorable outcome.

As part of the ongoing debt ceiling discussion, President Obama recently unveiled a plan to reduce deficits over the next twelve years that includes nearly $2 trillion in spending cuts and an increase in the debt ceiling. The government spends nearly $700 billion annually on national defense alone, and it also employs millions of people. As mentioned, government expenditure is a major component of aggregate demand. Economists would expect a reduction in government expenditures to shift aggregate demand inward in a similar manner as decreases to investment.

It is important to also consider the concept of “crowding out.” Whenever there is an increase in government spending, the resulting increase in incomes leads to increased spending and thus a higher demand for money. This increased demand for money causes increased interest rates. The opposite is also true. As government spending is reduced, so too are incomes, money demand, and interest rates. This reduction in interest rates makes borrowing cheaper, and thus stimulates greater private investment. Increased investment would therefore lessen the impact of a shock to aggregate demand from government spending cuts. Those in favor of spending cuts point to the increase in investment to suggest that the cuts won’t significantly decrease aggregate demand. Those opposed to the cuts note that interest rates are already very low so doubt that the cuts would spur much private investment.

It is relatively unlikely that US elected officials would be stubborn enough to permit a seemingly preventable crisis. Remember, the debt ceiling is a constraint that the government arbitrarily places on itself. No matter how the government chooses to proceed, the short-run economy is likely to see some negative consequences. Economists like to talk about optimal decision making, and recognize that sometimes even a “bad” option can be optimal if no other choice will lead to a better outcome. When it comes to the debt ceiling, let’s hope that our elected officials think like economists.

Discussion Questions:

1.) A number of nations around the world hold the U.S. dollar as their reserve currency. Others have periodically pegged their exchange rate to the U.S. dollar (China, for example). What would the implications of a U.S. credit default mean for foreign economies?

2.) Suppose that the debt ceiling remains unchanged. How might the US government prevent defaulting on its loans? How does this compare to the current plan suggested by President Obama?

3.) If you were President of the United States, how would you deal with the current level of debt? Would you increase taxes? If you were to cut programs, which ones would you cut? How might your view change if you were up for reelection?

June 10, 2011

Aplia Econ Blog: News for Economics Students

Correcting Faulty Defaults to Improve Society?

California recently cut $170 million from the amount the state must pay toward 2012’s retirement benefits, according to an article in the Mercury News. With the uncertainty regarding the future of pension plans and social security, private retirement savings are more important than ever. Despite this need, many people have difficulty making consumption sacrifices today to provide for their future selves.

Recent changes to many private firms’ retirement savings programs seem to reflect this need for personal savings. In the past, employees had to actively opt-in to company savings plans by changing their monthly contribution amount from the default of $0 to some positive amount. Traditional economic models of savings assume that people are perfectly rational and will choose the level of saving that maximizes their utility over the entire course of their lives, therefore people’s decisions of how much to save should not be affected by something as small as the effort required to “opt-in” to a plan. Companies have found, however, that merely changing the default option from “no savings” to “X% of paycheck automatically saved” causes a significant increase in employee savings. One firm found that after switching from standard to automatic enrollment in retirement savings plans, the participation rate for new hires was 35 percentage points higher after three months on the job (as compared to those hired before the automatic enrollment). The participation rate remained 25 points higher after two years.

Why would something as seemingly trivial as changing the default setting have such a large impact on the decision of how much to save? The field of behavioral economics acknowledges that people do not always act according to the model of perfect rationality, which requires weighing all costs and benefits (present and future) and accounting for all available information. Deciding how much to save for retirement is an important life decision, yet the “easy” choice of accepting the default option often prevails against the rational action of giving it more serious consideration. Traditional economic models do not explain the widespread tendency to stick with defaults regardless of their suitability, but behavioral economists can replicate this behavior in controlled research environments.

Applying this understanding of behavioral economics to the savings plan structure is an example of “libertarian paternalism,” a school of thought that strives to maintain freedoms (libertarian) while still guiding people towards the choice that society deems best (paternal). The new default setting does not interfere with employees’ rights to do what they please because employees can easily “opt-out” by making a short phone call and signing a form. At the same time, it benefits society by encouraging more people to save, since those who do not sufficiently save for their future needs pose a problem not only to their older selves (who may have to work past their desired retirement age), but also for the government (and thus taxpayers) who may then have to help provide for them as well.

Discussion Questions:

1) Do you think that a company changing the default behavior for a retirement program infringes on employee's rights?

2) How do you make decisions about long-term financial planning? Do you research and model your finances, base your decisions on suggestions (from an employer, family, or friends), or ignore it entirely?

3) Are you surprised that changing a default value has an effect on what people select?

4) Imagine you are a freshman in college choosing a meal plan. You don’t know what the other food options on campus will be like, nor what your schedule will be. What advantages does a “default” option provide in this situation?

April 22, 2011

Aplia Econ Blog: News for Economics Students

Revisiting the Reach of the BP Oil Spill

A year ago, cleanup efforts to recover from the Gulf oil spill were just beginning, but the effects of the spill were already finding their way into markets. While the debates and projections attempt to forecast how far the oil will spread, economists understand that the effects of the spill will reach further than the oil itself ever could. While many initial discussions focused on the local impact of the disaster, applications of the basic supply and demand model shed light on how a regional disaster can spread to national and global markets.

Soon after the disaster, the Associated Press reported that the price of shrimp started to climb in response to the spill. To begin to understand why, consider the direct effect of the spill on the supply of shrimp caught in the Gulf. The graph to the left reflects the market for Gulf-coast shrimp. As shown on the graph the oil spill reduces the supply of locally caught shrimp in the gulf as fishermen have been prevented from conducting much of their normal business. In response to the reduced supply, the equilibrium price rises, while the amount of shrimp sold falls.

Assume that shrimp caught in the Gulf and shrimp caught elsewhere are separate goods, though the markets for each are clearly related. Aside from the environmental problems associated with catching shrimp in the gulf, there may be variations in quality or style between shrimp caught in different locations. That said, shrimp are still shrimp, so even if consumers have a slight preference for one type or another, shrimp from other locations can be considered substitutes. When two goods are substitutes, an increase in the price of one of the goods causes an increase in demand for the other, all else held constant. When the market price for Gulf-caught shrimp rises (along with concerns that shrimp caught in the gulf may be contaminated) many buyers will look to purchase shrimp from other regions, like North Carolina, South Carolina, Georgia, and Texas. The second graph to the left illustrates how an increase in the price of a one good (Gulf shrimp) causes an increase in the demand for a substitute good (non-Gulf shrimp). The result here matches the reports by the AP: An increase in the equilibrium price and quantity of non-Gulf shrimp due to the effects of the oil spill.

Interestingly, the effects of the oil spill will also be felt by companies that have nothing to do with catching anything from the sea. For example, consider the market for tartar sauce. Many people like putting tartar sauce on their shrimp when they eat it, but have no desire to eat tartar sauce on its own. Economists would call tartar sauce a compliment to shrimp. When two goods are compliments, an increase in the price of one of the goods causes a decrease in demand for the other, all else held constant. On the final graph below, you can see the effect that higher equilibrium prices of shrimp have on the tartar sauce market. When the market price goes up, consumers will purchase less shrimp, and if less shrimp is consumed, consumers have less of a need for tartar sauce. This decreases the demand for tartar sauce, resulting in a decrease in the equilibrium price and quantity of tartar sauce.

There is still too much uncertainty about how much damage has been caused and the extent of the long-term effect on the environment for economists to reliably give exact figures on how these markets will change. However, the basic supply and demand confirms that the effects of this spill can be seen far beyond the Gulf region.

Discussion Questions:

1) How will the elasticity of supply and the elasticity of demand for non-Gulf shrimp affect the magnitude of the change in equilibrium price and quantity? How do economists describe the magnitude of a change in demand for one good in response to a change in the price of another?

2) What other markets do you expect to be affected by a change in the price of shrimp? What will happen to the equilibrium price and quantity in each of these markets? Are these goods compliments or substitutes?

3) Suppose that instead of an oil spill earlier this year, weather patterns had changed to make the shrimp season in the Gulf abnormally productive. If it were easier to catch shrimp in the gulf, what would you expect to happen to demand for shrimp caught in other regions? What about the demand for complementary goods like tartar sauce?

4) If you wanted to work on a shrimp fishing boat, all else held constant, which labor market do you think would be more favorable to join, one in the Gulf coast or one in South Carolina? Why?

5) Suppose the fishing industry is monopolistically competitive. Do you expect firms to enter or exit the market in the Gulf right now? In the long-run, assuming that fishing conditions return to their pre-spill levels, what can you say about the firms that will be in the market? Is it possible that any existing firms will be better off now than they were before the spill? If so, how?

April 11, 2011

Aplia Econ Blog: News for Economics Students

Violent Torpedo of Falling Prices

We asked for the truth from Charlie Sheen, and we got it; well, with respect to his tour, that is. In mid-March, he tweeted that the first two concerts on his “Violent Torpedo of Truth Tour ” had sold out in a matter of minutes. Although Charlie Sheen was telling the truth about his first two concerts, the activity in the secondary market suggested that empty seats were still likely. Upon further analysis, it appears as if scalpers (not Sheen’s cadre of supporters) made up a large percentage of buyers at the box office, with high hopes that demand for tickets would soar in the secondary market.

Once there were no tickets left at the box office, interested customers who still wanted to see Charlie Sheen were forced to purchase resold tickets at prices higher than their original value. At first, some scalpers looking to make a quick profit posted tickets for Sheen’s performance online at heavy markups. Some scalpers sold their tickets and made a profit, while other scalpers held on to what they had or even bought more tickets in the secondary market with the expectation that ticket prices would continue to rise.

However, about a quarter of the tickets for Sheen’s first appearance were still available just days before the show. As the performance neared, there was a change in expectations, and thus behavior, by ticket holders. Since the scalpers were never interested in actually seeing Sheen perform, they were concerned that the window of opportunity to make a profit was closing. Prices were no longer expected to increase.

The graph to the left illustrates the initial supply (S1) and demand (D1) curves in the secondary market for Sheen tickets. The change in expectation by scalpers meant that ticket-buying scalpers left the market, causing a leftward shift in the demand curve (as seen in the shift from D1 to D2). As ticket prices fell and time was running out, those scalpers still holding on to tickets rushed to the market to get rid of their remaining tickets. This led to the increase in the amount of tickets supplied in the secondary market (as illustrated in the shift from S1 to S2). As a result, the price of resale tickets plummeted, and many scalpers ended up in the red. On stubhub.com, tickets for Sheen’s performance fell as low as $14. That is, they were selling for well below their original value at prices where many scalpers lost money on every ticket they sold. Clearly scalpers’ expectations about consumers willing to see Charlie Sheen were initially out of sync with reality. But with his wallet unaffected by fluctuations in the secondary ticket market, it is fair to say that Charlie Sheen is the one who ended up winning.

Discussion Questions:

Are ticket scalpers behaving optimally by agreeing to sell tickets for less than they paid? Explain the scalper’s profit-maximizing behavior.
Suppose beer is a complementary good to Charlie Sheen’s live show. If the price of booze went down significantly in Detroit the week before the show, what effect would this have on the demand for tickets to Sheen’s show?
Suppose a stand-up comedy show performed by Chris Rock would be a substitute good to Charlie Sheen’s live show. Suppose Rock already had one show scheduled in Detroit the night of Sheen’s show, but then Rock announced that he would add a second show that night. How would an increased supply of tickets to see Rock’s show affect the market for tickets to see Sheen?
Reviews of Sheen’s first show were reportedly quite bad, and Sheen was even booed. How would this news affect the market for Sheen tickets in other cities on his tour?

February 09, 2011

Aplia Econ Blog: News for Economics Students

Technology, jobs, and creative destruction

In response to the recent approval of an all-electronic toll-collection system for the Golden Gate Bridge, many San Franciscans have voiced concern over the loss of toll workers’ jobs. The belief that new technologies are necessarily detrimental to employment, however, reflects a common misunderstanding regarding the interplay between technological advance, progress, and the economy as a whole. Though the introduction of electronic tolls will harm the toll workers in the short run by putting them out of work, they also enable the government to re-allocate the money formerly spent on toll-worker wages. These savings will either pay for other public goods and services (thereby employing workers in other sectors) or be used to reduce the deficit (thereby reducing the burden on the taxpayer).

Many balk at the notion of cutting jobs for the sake of “efficiency.” Consider, however, whether people would choose to move in the opposite direction—sacrificing efficiency for the sake of increased employment. Instead of using dishwashers and washing machines, individuals could hire others to wash their dishes and clothes by hand—that, too, would create jobs. It is tempting to separate such individual spending decisions from those made by the government, but ultimately the saving from automated tollbooths is no different from that provided by any other time- and money-saving device.

The process of old products or services dying out in the wake of new technologies, known as creative destruction, has been transforming the world for centuries. Many typewriter manufacturers went out of business with the advent of computers, yet few people would argue today that we should have repressed such technological advances for the sake of workers. Just as the computer industry gave birth to myriad of new jobs, the new tolls themselves create jobs in technology development, manufacturing, maintenance, etc. Thus, instead of widespread unemployment, the result of such technological progress is economic growth. People use technology to produce goods more efficiently, and those goods then become available for everyone’s consumption.

Does this mean new technologies never cause employment problems? Of course not. Those whose skills are made obsolete by new technologies may indeed suffer a period of unemployment, although often the money saved is even put toward job-training programs and unemployment insurance to ease the pain of transition (to quote The Economist, “Protect workers, not jobs”). While this is an unfortunate side effect of technological growth, the difficulty imposed on the unlucky individuals is typically outweighed by the widespread benefits to society that technology creates.

Discussion Questions:

1. Can you think of other industries where creative destruction is present and thus encourages the creation of improved technology on an ongoing basis?

2. Before the Golden Gate Bridge was built in the 1930s, cars had to cross between San Francisco and Marin County (the two ends of the bridge) on ferries. How is building the bridge like an improvement in technology, and how did it impact employment?

3. How does creative destruction affect the quality decision producers must make? Why is it that some goods are made with the intention of lasting decades while others are only designed to last a few years?

4. In this case, the tolls will reduce the number of workers in the toll-collecting industry, but is this always the case with new technology? What is an industry where technology acts as a complement to labor, and how is this different from technology as a supplement to labor?

February 01, 2011

Aplia Econ Blog: News for Economics Students

Super Picks

My friend Kasie and I are both big football fans. We’ve tried to pick the winner in every NFL game since the start of the season, where each correct pick earns one point. With only the Super Bowl remaining, we’re a single point apart. Quite unfortunately, I happen to be trailing by that point, so my chance to avoid an offseason of taunting rests on picking the Super Bowl correctly and having Kasie pick incorrectly. Being an economist, I realized I could apply game theory to guide my strategy for making my final pick of the season in order to maximize my chance of beating Kasie. First, suppose my friend and I only care about if I tie or lose; losing by one point is exactly the same as losing by two. Also, as we have done all season long, our picks will be submitted before the football game starts and then revealed simultaneously.

For starters, assume that both the Green Bay Packers and Pittsburgh Steelers have a 50% chance to win, and further assume that when we make our picks, Kasie and I both know this.

The matrix on the right shows the utilities (or payoffs) associated with different combinations of picks. For example, if we both pick the Packers, I cannot gain any ground on Kasie for the season; therefore regardless of the outcome of the Super Bowl, I lose to her and get a utility of -2, while Kasie wins the picking game and receives a utility of 2. However, in the case where we do not pick the same team, no matter who I pick, I have a 50% chance of tying her for the season, and a 50% chance of losing by 2 points. Before the game starts, that gives me a utility of 1 unit, while Kasie gets a utility of -1 because she’ll have to sit through a now stressful game. Therefore, the utilities expressed in the payoff matrix when we play different actions represent ex ante payoffs—that is, they are our expected payoff before knowing the result of the game.

In this case, this is a complete information simultaneous game that has no equilibrium where either player can use a pure strategy. However, there is a mixed strategy Nash Equilibrium for this game where both players randomize their selection and pick either team with a 50% probability. When one player randomizes his or her selection by picking either team half of the time, the other player’s best response is to also pick each team half the time. If both players randomize this way, neither has an incentive to deviate from that strategy, and thus those strategies are an equilibrium.

There is at least one more complication, though, and it’s very important! I am a proud Pittsburgh Steelers fan, so I would prefer to root for my team knowing that I also have a chance to tie Kasie. It would not be as rewarding to root for the Steelers knowing that they must lose in order for me tie for the season. On the other hand, Kasie is a stinky New England Patriots fan, and she has no preference between the teams in the Super Bowl. Because I care about who I want to win, I now prefer not only to pick differently than Kasie but also to pick the Steelers so I can wholeheartedly root for them.

The matrix on the left now incorporates my rooting interest by updating my payoff if Kasie and I disagree, while leaving Kasie’s payoffs the same no matter who we both pick. Given the new payoff matrix, the mixed strategy Nash equilibrium is that I pick either team with equal probability, and Kasie picks the Packers 1/3 of the time, and the Steelers 2/3 of the time. If Kasie doesn't adjust her mixing, then I would always pick the Steelers since the expected payoff would be higher than any mixing strategy. My mixing strategy stays the same, however, because Kasie's payoffs are untouched.

Of course, Kasie is going to read this post as well, so now I’m going to need to account for the fact that she knows my preference for the Steelers. Looks like it’s time to update my strategy again!

Discussion Questions:

1. Consider the solution to the picking game when my rooting preferences are factored in. Despite my interest in the Steelers, why do I still only pick them half of the time? If I picked them more than half of the time, what would Kasie’s optimal response be?

2. What is your favorite sports team? Which is more important to you, seeing them win a championship or winning a competition with your friends? How do personal preferences of one player influence the decisions each player makes in this picking game?

3. Suppose one person picking has information about the game that the other does not. For example, if one person gets a tip on an injury to a star player that isn’t public knowledge, how would this new information change the informed picker’s strategy?

4. Some people actually prefer to bet against their favorite team, using the wager as a form of insurance. The logic being that “I won’t mind losing money if my favorite team wins, but if my favorite team loses; at least I’ll get some cash to make me feel better.” How would thinking like this change the way the picking game’s payoffs are described?

November 02, 2010

Aplia Econ Blog: News for Economics Students

A Biter's Market

Halloween always brings back a lot of wonderful memories for me. Like so many kids, trick-or-treating may have been my favorite few hours of the year. And while costumes and free candy are always appealing, there was also some thrill to the hunt. At least for me, just getting the candy was satisfying, regardless of if I liked a particular treat, or if I already had more candy than I would ever be allowed to eat! If an economist assumes that kids get some utility from “the hunt,” or at least that it is costless to kids to continue to go to houses for as long as they are allowed, then the result is that kids will get as much candy as is offered to them, regardless of how much or little they value it. If kids end up with a bunch of candy, many people are concerned about the health effects associated with eating it all. One interesting attempt to work with the fact that kids will get candy but reduce their consumption has come from a very interesting program where dentists offer to buy Halloween candy back from their patients. By offering a monetary incentive, these dentists are accepting that kids will gladly gather as much candy as possible, but perhaps their consumption can be changed if they are given more incentives than just a promise of “healthier teeth.”

Most economists would look at this program and talk about how its organizers are trying to incentivize kids by increasing the payoff of an otherwise less desirable choice. At the frontier of current research, economists are developing models to analyze the fairest and most efficient way to do this. Some economists may also suggest that this program’s goal is to influence a child’s preferences so that she will make different (healthier) decisions in the future. However, the ability of programs to change preferences is currently an open question in economic research.

Discussion Questions:

1. What other sorts of behaviors might dentists want to subsidize? What are some other examples of when a healthcare provider tries to encourage a healthy behavior?

2. Why might a program, like the one above, be unsuccessful at reducing candy consumption?

3. How much money would you need to be paid to sell a pound of Halloween candy? How much candy would you sell if you had five pounds? What about fifty pounds?

4. Do you think the amount of money a child already has will influence his or her decision to sell some candy? In what way?

5. Right now, anyone choosing to sell candy can pick which treats they sell. How do you think participation in the program would change if the pound of candy was selected randomly from all the candy collected trick-or-treating? What if the most dentally damaging candies were priced higher?

September 21, 2010

Aplia Econ Blog: News for Economics Students

D is for Demand: Sources of Success of 3-D Movies

According to the Wall Street Journal, 2009 was the first year since 2002 where sales at the U.S. box office beat those of DVD releases by passing the $10 billion mark. The success of Avatar has certainly ramped up the industry with its worldwide revenue of more than $2.7 billion; such an amount is comparable to the GDP of a small but affluent country like Barbados! Also interesting is the recent trend in increasing ticket prices: Prices of domestic movie tickets, according to the Los Angeles Times, have increased by more than 10% in the first few months of 2010. Specifically, not only is a 3-D movie ticket sold with an average surcharge of $4 over the 2-D ticket prices, 2-D tickets are also up by roughly 4%. Similar to books and cars, the market for movies is monopolistically competitive. Every movie is a separate product that faces competition from a multitude of similar but not identical products, namely, other movies. Entry of new products normally puts a downward pressure on the prices of existing products and the average market price overall. So, the question is: Why, despite the increased competition, have prices gone up for both formats? Why are people willing to pay significantly more to go to the movies?

Just as in the case of many other goods, the demand for movies is sensitive to the changes in ticket prices. The extent of sensitivity, which economists term price elasticity of demand, can be different for different movies because it is determined by a number of factors such as availability and prices of similar goods (substitutes), the number of consumers, and tastes and habits. The demand for goods that have many close substitutes is typically more elastic than for goods with fewer substitutes. The reason for this is that when the price increases, consumers have the option to buy other similar and potentially less expensive products. For example, when the ticket price of a traditional movie increases, more viewers are likely to decide to watch the film on DVD. This is especially true as improvements in technology have made such substitutes as DVDs, Blu-Ray Discs, On Demand products, and streaming Netflix accounts easily available. Thus, generally increasing prices of movie tickets might not boost revenues, as the decrease in sales (quantity demanded) may wipe out any gains that would arise from the price increase. But if this is the case, then how can we explain the fact that movie theaters are receiving record-breaking revenues while ticket prices are increasing?

For a long time, 3-D films have not made any noticeable impact on the industry, but James Cameron’s inventive cinematography created a new product, a dramatically different viewing experience, which shook the entire market for entertainment. Movie theater technology was updated to accommodate mass screenings, and other directors and studios followed suit. Since Avatar’s release, 3-D movies have generated more than one-third of all domestic movie revenue. At least some of the factors responsible for the price elasticity of demand for movies have changed. It is hard and relatively expensive to replicate a 3-D experience outside of the movie theater. Thus, the 3-D format has fewer close substitutes, as home theaters with 3-D capabilities are still rare and quite expensive to install, which lures more viewers out to 3-D screenings despite more expensive tickets. The demand for 3-D movies is, therefore, less elastic, which creates the opportunity for the producers and movie theaters who deliver these products to the consumers to charge higher prices.

At the same time, the 2-D and 3-D markets are intertwined as the number of movie theaters and seats is limited, at least in the short-run. Thus, the story would not be complete without taking into account theater operators’ decisions about prices and allocation of seats. The necessary reduction in the number of rooms where 2-D movies are shown shifts the supply of 2-D to the left, which drives the price of that traditional format up despite the reduction in its demand. So, ultimately, rising prices of both formats have different (although related) explanations. If the industry experiences more consumer interest in its new products, like we see with Avatar, the result is growing revenues along with higher average prices.

While it is possible that the spectacular success of Avatar was a sort of surprise attack on the consumer followed by a box office record, the systemic changes it brought to the movie industry suggest that the effects will be longer-lived. Due to the advent of the 3-D movie format that can show in regular theaters, more movies are money-makers now than before. This situation is likely to persist for some time until affordable home theaters catch up in quality and increase the competitive pressure on the movie theaters.

Discussion Questions:

1. Similar to how Napster was used as an illegal medium to download music, online services are emerging for downloading movies in theaters not yet released for home viewing. How might this affect the price elasticity of demand for movie ticket sales? How might 3-D movies be protected from this type of piracy?

2. The “second law of demand” implies that price elasticity of demand is greater in the long run. How and why might moviegoers’ behavior change in the long-run? What would this mean for theater owners?

3. The explanation offered here is based on the assumption that 2-D and 3-D movies co-exist in the market for entertainment. Do you agree or disagree? What would be an alternative approach to explain the rising average prices of movie tickets?

September 02, 2010

Aplia Econ Blog: News for Economics Students

Cherry Picking: An Economist’s Guide to Used Cars

After recently moving, I needed a used car that was both well maintained and reasonably priced. However, there was a problem: I am not a car expert. The prospect of finding a cherry, or a used car that’s worth its price, among the thousands of available cars in my area seemed a little daunting. Fortunately, by turning my search into an economic exercise, I was able to demystify the process and ease my nerves about making such a significant investment choice.

Used cars have been a popular topic in economics since George Akerlof (who eventually won the Noble Prize in Economics for his work on the subject) described how the market for used cars can be affected by imperfect information. A key premise in Dr. Akerlof’s work is the idea of asymmetric information: a scenario where one party has access to information that the other party does not. In his most famous paper on the subject, “The Market for Lemons: Quality Uncertainty and the Market Mechanism,” Akerlof argues that in any market where rational people are met with asymmetric information, eventually the only goods for sale will be those that are not worth buying. Economists use the term adverse selection to describe this idea. When a market suffers from adverse selection, skepticism affects the prices that buyers are willing to pay. The downward pressure on prices drives out the high-quality sellers. Ultimately, the only sellers left at a given price level will be those of low quality. When buyers eventually realize that the only cars available are of low quality, they will decide not to buy any cars at all. Now you can understand why I was feeling a little overwhelmed about the whole process of buying a used car.

Thankfully, the research didn’t end there. Joseph Stiglitz (who shared in Dr. Akerlof’s Noble Prize) developed the notion of screening as a method to get around the difficulties of asymmetric information. Screening is a way for both sides of a deal to use what they already know to learn about hidden information. By reducing the level of asymmetric information, Stiglitz argued that you could avoid situations of adverse selection. Knowing this, I started to put together some tools that would help me to screen my potential sellers.

I first used car-pricing guides to give me a general sense of when a seller was giving a fair price. If I couldn’t trust a seller to give an honest price, I certainly couldn’t trust the seller to be honest about other aspects of the car. I also requested reports that outlined what work had been done on the cars. In addition, I asked my car-savvy uncle to help me spot work that may have gone unreported. When requesting these reports, I judged the sellers’ reactions. Those with high-quality cars had nothing to fear from the increased inspection, while nervous sellers made me nervous. One desperate seller attempted to drop his price by nearly 20% when I told him I’d be back in a day with my uncle. Needless to say, we didn’t make it back to look at that car!

With patience, I found a nice prospect at a dealership that didn’t typically sell used cars. As it turned out, the previous owner had traded in her commuter car for a discount on something a bit more…luxurious. Before it was accepted as a viable trade-in, the car had to pass an inspection by the dealership, in addition to the standard state inspection. The dealer was also happy to meet my uncle and answer any questions he had about the vehicle. By exhibiting a willingness to provide as much information as possible on the car’s history, the dealer was using a technique called signaling. Michael Spence (who also shared in Dr. Akerlof’s Noble Prize) developed an explanation of how signaling is used by the person with more information in a particular deal to inform the other side about quality. In his willingness to let me explore the potential problems of the car, the dealer was trying to signal that he was confident about its quality and that it was worth my investment.

The inspections checked out, and the asking price was right on target with what my pricing guide suggested—I had found myself a car! By using my head and applying a little economic theory, a seemingly impossible task turned out to be fairly easy and nearly fun. Spread over about a week and a half, the process took about 15 hours. Only time will tell if it was worth the temporal and monetary investment, but, after a month of driving the car pretty vigorously, I’m confident that I found a cherry.

Discussion Questions:

1. How could car sellers that are really confident about the quality of their cars guarantee value? Why are used car sellers hard-pressed to offer such guarantees?

2. Signaling is a helpful tool when a transaction suffers from asymmetric information. Can you think of how signaling is used in the job market? What are some ways that prospective employees can use signaling to their advantage?

3. How do markets for products besides cars (like books or classes offered) suffer from asymmetric information? What tools have you have used to make more informed choices?

August 11, 2010

Aplia Econ Blog: News for Economics Students

The Opportunity Costs of Relationships

Since it is generally easy to compare the price-tag cost of one good or service against another, people tend to consider only the monetary cost of a decision. However, what’s also important to consider is the whole value of what you are giving up when you make a decision. In economics, this is known as the opportunity cost. A simple textbook example describes a market that offers two goods for sale: apples and oranges. If an apple can be bought for $1 and an orange for $0.50, the monetary cost of buying an apple is $1, but the opportunity cost is equal to how much you value the two oranges that you give up if you choose to buy an apple. While this observation may not seem particularly important in this context, it can be applied far beyond the realm of monetary dealings.

Romantic relationships are obviously not regular commodities like apples and oranges in that you don’t just head to your local date market and buy a girlfriend or boyfriend. Despite this violation of the competitive hypothesis, relationships have opportunity costs too. That is, the opportunity cost of a relationship is comprised of all the things one foregoes to be in that relationship. While it is not difficult to see the many wonderful things you gain from having a romantic partner, it is easy to overlook the things you give up in exchange.

Here’s a list of some of the things that most people forgo to some degree to be in a relationship:

(1) Spending time with friends and family
(2) Going out and meeting new people
(3) Developing or engaging in hobbies
(4) Working
(5) Exercising

Some people may find that being in a relationship allows them to do more of some of these things (maybe you work out together or spend lots of time with mutual friends), but usually the time you spend with your significant other tends to edge out at least some of the things you like to do on your own.

In economics we represent such trade-offs using graphs like the one below. The red line is known as the budget constraint, and while it typically represents a monetary budget, in this case it represents a sort of time budget for an individual in a relationship with eight hours of leisure time per day (assuming eight hours of sleep and eight hours of work). The eight hours of leisure can be divided anywhere between spending all 8 hours with your significant other or all 8 hours doing other things. Regardless of what allocation a person chooses on the red line, any movement along the line represents a tradeoff of one activity for another.

Despite the perception of economics as dismal science, the point is not that the cost of relationships outweighs the benefits, but rather that there is an opportunity cost to everything. So if you’re single and accustomed to thinking about all the things you’re missing out on, take comfort in the things that you aren't giving up.

Discussion Questions:

1. Consider the graph depicting the time-budget constraint. If a person quits their job and suddenly has more time, how does this affect the person’s position on the line or the position of the line itself?

2. If person A and person B primarily give up time spent with friends when they are in relationships, and person B really likes being with friends, which person’s relationship comes at a higher opportunity cost? If you were to draw each of their indifference curves on the budget constraint graph, how would the two compare?

3. How would being in a relationship affect your overall consumption? If you are in a relationship, are there some goods or services that you would consume more or less of in a given week? Which of these goods would you say are “complementary goods” with relationships? Which are “substitutes?”

4. Sometimes when economists model consumption choices for goods that are consumed over longer periods of time, they introduce switching costs. What sorts of things associated with a break-up may be considered a switching cost? If you assume that breakups are costly, how might this change a person’s decision to allocate their time?

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