Colleges have become increasingly expensive. College graduates are leaving college with more and more debt, yet everyone says that you need to get a degree. As the costs get higher, it no longer becomes clear whether that declaration still holds (see here and here). It makes sense then that sites like Payscale try to give consumers more information about their choices. Payscale provides a report once a year on the average annual salary of several schools (over 900 institutions). The table below lists the top 10 schools in 2016-2017 at the Early and Mid-Career levels.
They are mostly elite colleges that only admit the top students, therefore, it isn't surprising that the receive high salaries. Also many of these universities are known for engineering. Thankfully, Payscale also provides the average salary for many college majors (over 300). Using this data and the IPEDS database we can reconstruct what colleges and universities average salary should have been had their graduates earned the average salaries for the degrees they earned. Using this constructed salary we can create a "mark-up" value. The two graph below show the top 10 Early and Mid-Career mark-ups.
What a difference. There are still a few elite colleges and universities, but there are several surprising names amongst these lists. However, we still are forced to admit that the quality of the graduating students certainly reflects the quality of the entering students. So do these mark-ups reflect what students are getting out of their experience in school, or do the numbers just reflect the ability they brought with them. To address that the following table subtracts the early-career mark-up from the mid-career mark-up. If the number is large then it is likely that something about their college experience is being recognized as valuable by the job market.
This top 10 is very different than the previous lists. This is a clear victory for small liberal arts colleges and their ability to transform lives. However, Babson College certainly deserve special recognition for being on all three top 10 lists. This analysis does not tell you which college to go to since it does not incorporate the costs. However, it does tell you which colleges provide an education that employers learn to value.
If you're interested in a particular college or university please leave a comment or send me an email.
Over the past year there has been less disagreement across forecasters predicting the Federal Funds Rate, and unemployment. Not surprisingly as the forecast horizon (the time between the forecast and it's realization) decreases the agreement among forecasters increases. That is, there are fewer outliers because more is known. The WSJ Economic Forecasts display this property, however, using the payrolls employment forecasts we can isolate the changes additional variation outside of the monthly uncertainty. The graph below plots the normalized standard deviations for Employment Payrolls, Inflation, Federal Funds Rate, and Unemployment forecasts.
The yellow line of employment payrolls is stable, with a slight decrease in the past half year. In contrast, the Fed Funds Rate exhibits a very steep decline from a year ago. This is likely due to increased consistent messaging amongst FOMC participants as well as improved (and consistent) fundamentals. A large portion of the decrease is likely just due to the shortened horizon. The following graphs will display the forecast variability over all the forecast horizons.
The graph above shows the four forecasts of inflation. Clearly once we control for the general uncertainty the slight downward trend of inflation forecast variability disappears. However, the graph below shows that that the downward trend very strong for the Federal funds rate forecast variability.
Also note that the drop is across all four forecasted dates, which implies that the result is not a normal change over forecasting horizons. Fed officials should be encouraged by this graph because it suggests the consistent messaging may be consolidating interest rate forecasts.
We see a similar, but slightly different graph for unemployment. Again it looks as though all four forecasted dates are decreasing in forecast variability. The main difference is that there is clear stratification across forecast horizons. From this graph it is unclear whether forecasters truly are more certain, however, I suspect the clear stratification across forecast dates indicates a typical spread of forecast horizon uncertainty.
The short answer? No. The long answer will probably need to wait for a detailed academic paper. However, this post will present some suggestive evidence that the short answer above is correct. We will look at end of day price data (obtained via wiki EOD) and monthly options volume data (obtained from the CBOE). As usual, the Hamilton cycle method provides my preferred measure. This post shows that options volume tend to have longer cycles than stock prices.
Our evidence will come from four stocks: Apple (AAPL), JP Morgan (JPM), TJ Maxx (TJX), and UPS. As you observe the graphs consider a cycle to be several months away from trend (zero). The graphs below present those cycles.
Options and stock price do not appear to exhibit any synchronicity. Options cycles seem the same across these stocks despite them coming from different sectors of the economy. The only consistent fact is increased options volume and a decrease in stock price during the Great Recession. Options volume appears more jagged, which makes it harder to assess the cycles. However, if we were to look at stock volume we would find even more volatility.
To answer the question at hand let us just count the number of deviations from trend for each company. For Apple, 5 option cycles and 6 price cycles. For JP Morgan, 5 option cycles and 7 price cycles. for TJ Maxx, 5-6 option cycles and 7-8 price cycles. Finally, for UPS, 5 option cycles and 5 price cycles.
This by no means is a statistical test, however, it does suggest that over the same time period there were fewer options cycles than price cycles. One can think of the option cycle as the force of speculation on the future stock price, whereas the current stock price cycle reflects more frequent news about firm value. Perhaps the lack of options trades (relative to stock trades) slows down the formal speculative market.
When this post idea came to me, I expected a stronger correlation between prices and options. The lack of correlation true (more or less) when comparing stock volumes (the graphs were messier though). Any thoughts? Please comment below...
If you would like to have a similar graph of a specific company let me know.
On June 14th the FOMC decided to increase the Federal Funds Rate. This move was almost perfectly anticipated by the WSJ Economic Forecast Survey participants. However, a close look at their revisions to the future rates has them leaving the Fed funds rate mostly unchanged, but seeing significant declines in Ten-year bonds particularly in the short term. The graph below shows the changes in expected interest rates through the end of 2019.
While June and December 2017 Federal funds rates forecasts are almost identical, the Ten-year Bond rates have dropped significantly. This could reflect greater certainty over Fed policy, and less perceived economic risk in the short-term. That is, the Fed has been effectively communicating their criteria for raising rates and participants forecasts have not seen a reason to change the projected path. Since the data have been, for the most part, positive, forecasters believe the bond markets will incorporate the lower economic risk into the bond rates. Taken as a whole, this suggests that expectations about the short-term future economy are good.
Turning to the longer-run (in 2019), both expected rates have dropped slightly. Given the long forecasting horizon it may only reflect reversion to the mean (what goes up must come down). However, I suspect that forecasters recognize that while the economic data has been positive, it has not signaled robust growth. Essentially, we are still (very slowly) climbing out of the hole the financial crisis created and we should continue to do so for the next two years or so.
Hillary Clinton stated in a recent interview that misogyny contributed to her loss in the 2016 election. No doubt that some voters cast votes based on gender (in both for and against Clinton), but those voters (and votes) decided long before the election season started. Many claim that Bernie Sanders and Donald Trump tapped into working class anger over inequality. However as Nate Silver has pointed out on numerous occasion (here and here just to mention a few), GDP growth has some explanatory power in predicting elections. Specifically, low or negative GDP growth tends to hurt the chances of the incumbent party. This post dissects the claims of inequality and headline economic growth.
The table above provides statistics of states in which the majority of voters voted for Clinton or Trump and those states where neither received the majority. For inequality we have data on the Gini coefficient within each state in 2010 and the share of income commanded by the top 10 percent within each state in 2013. The Gini coefficient measures the degree of inequality on a scale from 0 (least unequal) to 1 (most unequal). Clearly by both measures, within-state inequality was worse for states that overwhelmingly voted for Clinton.
Hosehold median income arguably shows the inequality story as a national phenomenon, but those statistics were qualitatively the same at the time of Barack Obama's re-election in 2012. However, when we turn to the average growth rate of median household income over Obama's tenure (2007-2015) we see a striking disparity between the states where the majority voted for Clinton and the rest. The solid Clinton states fully recovered from the recession and have even surpassed pre-crisis income levels. In contrast, the solid Trump States have not. In the contested state, the "incumbent" democrats faced strong headwinds from the slow growing economy.
What have we learned? Local inequality does not seem to be driving election results, however, the lack of economic recovery in states that either voted for Trump or were narrow contests surely influenced the election. A suggestion for future elections would be to consider the long-term economic growth within a state as opposed to the entire nation over the term of the incumbent.
Notes: Gini data comes from the US Census Bureau. Income share data comes from Mark Frank. Household income data comes from the GeoFred database.
The Wall Street Journal economic forecast for June has been released. The first post of the June series will take a more in-depth look at the GDP forecasts. Previous posts (see here and here) looked at how consistently participants annual forecasts matched their intermediate quarterly forecasts. Alternatively, we could consider the influence of those quarterly forecasts had on the annual forecasts using a basic regression.
The table below explores what quarterly forecast information had the greatest impact on annual forecasts. The sample of 49 analysts provided growth rates for all quarters and full year in both May and June (see the end of the post for some technical details).
The results suggest that analysts with first quarter forecasts that were 1 percent above the mean tended to increase their annual growth forecasts by 7 hundredths of a percent more than the average. Basically those who are optimistic about the beginning of the year are optimistic for the whole year.
However, a more subtle result arises from the third quarter estimates and revisions. First, being optimistic about the level of third quarter growth was correlated with downward revisions of annual growth. Second, becoming more optimistic about third quarter growth was correlated with upward revisions of annual growth. A likely story that explains these facts is that revisions in third quarter growth are largely driving revisions in annual growth, but that those who are most optimistic about third quarter growth have somewhat muted expectations about annual growth.
Forecasters project a 2.27 percent annual growth rate for the whole year and 1.20, 2.98, 2.57 and 2.47 percent for Q1, Q2, Q3 and Q4, respectively. Even though first quarter growth is still expected to be slow, forecasters anticipate that the subsequent quarters will be robust. The analysis above suggests that a critical component to changing expectations are optimism about third quarter growth and third quarter revisions.
Technical Notes: The data for the levels are percent deviations from the mean. The data for the revisions are the percentage change from May to June. The regression utilizes standard Ordinary Least Squares, which means it does not account for endogeniety (quarterly and annual forecasts are created at the same time by the forecasters), therefore we cannot say anything about causality using this technique.
Last July a little over 66 million passengers traveled via plane within the US up from a little less than 62 million in 2015. With the summer fast approaching, one might wonder if we will have another robust travel season. This post describes how the strong demand for air travel has been driven by a steady decline in ticket prices.
The graph above shows the cyclical component of the number of passengers on domestics flights in the US. (The graph including international flights is similar.) Not surprisingly the financial crisis depressed air travel, but the industry recovered well hovering around the trend from 2011 until late 2014. More recently, however, air traffic has hit a bit of a boom, driven, in most part, by a decline in ticket prices.
The graph above displays the cyclical component of tickets prices for domestic airline tickets. Since 2014 ticket prices have been consistently below trend, only recent showing signs of reversing course. This corresponds nicely to the above trend cycle in the number of passengers. Consumers are currently taking advantage of relatively low ticket prices, but the increased demand should increase pressure for a gradual increase in future ticket prices.
This post examines the WSJ Survey specifically looking at forecasts of the unemployment rate. Compared to previous posts on GDP, Fed Funds Rate, and Economic Risk, forecasters have high expectations for the future unemployment rate. In particular, they have become increasingly optimistic about 2018 relative to 2017.
The graph below depicts the average forecasted unemployment rate for each month since December 2015 across June and December in 2017 and 2018. The lines become darker the closer the forecast is to the present.
Forecasters are not just becoming optimistic about short-term unemployment, but they are also expecting the trend to continue for a year and a half. A year ago they were expecting the end of 2017 to be the low point in the unemployment rate. The current consensus forecast for December 2018 is 4.21 percent, a rate not seen since prior to the Dot Com bubble, however given the recent unemployment numbers (4.3%) this expectation seems plausible.
The upbeat unemployment forecasts put the more tepid GDP and interest rate forecasts in perspective. More than likely the most recent revisions are an overreaction to the strong April payroll employment numbers. The more recent May payroll and unemployment values send a more mixed message more consistent with previous GDP and interest rate forecasts.
The Trump administration has plans to cut taxes. There have been other blogs and articles that have explained that tax cuts do not necessarily increase growth (see Dietrich Volrath, and Brookings for example). Most of these consider only US economic history, which may be idiosyncratic. Instead, this post will look at recent economic tax policy and economic performance of several OECD countries from 2000 to the present (the data can be found on stats.oecd.org).
The graph above depicts the average growth rate before and after a tax policy change. The blue lines represent tax cuts and the yellow lines, tax hikes. The darker lines averages the five year average (annualized) growth rate within each country, and the lighter lines average the one year growth rate. The results confirm what has been found in most research, tax cuts have little impact on growth, and tax hikes can lead to higher growth in the long run.
The figure does a nice job of showing this, but numbers may also put it in perspective. The difference between the five year average before and after a tax cut is -0.877 percentage points. For a tax hike the similar number is 1.256 percentage points. In other words the five years after a tax cut have almost 1 percentage points lower GDP than the five years prior, whereas the years after a tax hike have a 1.25 percentage point increase.
Some caveats to this analysis:
1) One might think that a tax cut follows the start of a recession. In this case 4 out of the 15 hikes and 9 of the 37 cuts occurred in 2008 through 2011. Roughly the same percent.
2) Deciding whether there was a tax hike or cut depended upon a change in the marginal income tax rates. Sometimes tax reforms changed both the margins and the rates, making it unclear whether there was a cut or a hike. Those cases were ignored.
3) Finally, the analysis above is crude and one dimensional. It is possible that countries that tend to cut taxes rather than raise them have other characteristics that lead to lower growth.
Even with those caveats in mind, the evidence seems pretty clear. The lower tax rates will not be offset by higher economic growth, certainly not to the extent predicted by the administration or Congressional republicans.