A good friend of mine, Chris Gibbs at UNSW-Sydney, mentioned some of the things that led to Puerto Rico being in its current debt position (hurricane not included). This post tells that story in graphs. The first is one that everyone has been talking about, that Puerto Rico has been in a recession for several years: Real GDP per capita has been negative or just barely above zero from 2005 until 2013. Note that the downturn occurred prior to the financial crisis. Before that Puerto Rico enjoyed strong growth except for the impact of the recessions in the 70's and 80's. The gradual decline in GDP growth also makes sense since they were catching up to mainland United States. The argument some bring up is that the local government has been spending too much, instead the long recession lowered tax revenues. One might argue that the Puerto Rican government should have controlled the situation, but the reason for the sudden prolonged recession was actually due to US policy. Prior to 1996 Puerto Rico benefited from a provision in corporate tax law that encouraged businesses to locate there. However, Bill Clinton signed a law in 1996 that would undo that special status, because companies were perceived to use that law to evade taxes. Ten years later that law took full effect. And here were the consequences: Jobs just started disappearing after 2006 and have not really recovered since. To be clear, the bill that Clinton signed was likely appropriate policy in terms of corporate taxes, but it failed to consider the ramifications to the people of Puerto Rico. Where did this drop off come from? Well take a look at the pharmaceutical/chemical industry: That steep decline starting in 2006 was a direct result of the tax policy changes. As the businesses left, and the high paying jobs left with them, the impact was textbook economic death spiral. Consumption of services by the wealthy who have left disappears, which causes those service industries to fire their employees. Those employees no longer have money to afford other goods and services and so the firms that rely on them have lower demand an must fire some more employees and so on.
Puerto Rico did have 10 years to prepare for the change, and they did end up signing some territory tax incentive laws, but after decades of special tax policy they probably did not understand the impact of losing that status. Which is to say, when the administration blames Puerto Rico's current debt and predicament on bad policy the administration may be correct. Except, the bad policy was the Federal government leaving Puerto Rico in the lurch when taking away a major influence of the Puerto Rican economy.
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The latest WSJ economic forecasts are out, and they contained only a few negatives compared to the September edition. Major indicators of economic weakness were higher oil prices and lower housing starts, and Q3 GDP growth. The average third quarter GDP growth forecast dropped by about 0.25 percentage points, but this was offset by an increase in fourth quarter growth. The declines for housing starts were not very large. However, oil price forecasts all shot up by a dollar or more.
On the bright side, the spread between the federal funds rate and ten-year bonds tightened indicating less perceived economic risk over through 2019. Annual GDP growth increased across the board, though not by much. The consensus is for more than 2 percent growth through 2019. We also continue to see declines in the predicted unemployment rate, and the decline was stronger for the longer horizon indicating that forecasters are skeptical about a quick reversal in the job market tightens. Last month I discussed the likelihood of recession based on unemployment forecasts. The recession probability dropped a little (almost a quarter of a percent), and payrolls increased. The unemployment trough still is timed for the end of 2018, but the 2019 forecasts dropped more than the 2018 forecasts, which suggests that the window for the pivot point is expanding. That is, there is more uncertainty about when the labor market and the economy will start to contract. Several sources have discussed the possibility of a hosing bubble (see The Great Recession Blog). Most people who make this claim point to the following graph and say prices are now higher than they were pre-crisis: I think it is fair to say there definitely was a bubble before the recession, but the housing market has really only made it back to it's trend. If we look at a graph of the housing market cycle we can see that the previous bubble was due to spending almost a decade well above trend: That decade led people to believe housing prices always increase and caused them to engage in risky borrowing. However, the drop in housing prices was so severe that it has taken almost a decade just to get us back to trend. Real estate loan data more or less supports this claim: Realestate loans were severely depressed for about four years post recession, and only in the past two years have we approached the trend. One area where that may indicate the beginnings of a new bubble is housing starts: There have been very few experiences in the past 50 years where starts have been above trend this much for so long. One could argue this signals excessive growth in housing. However, the increase in supply should, according to basic economic theory, depress housing prices and stave off any bubbles.
Under the recently announced tax plan rates will be reduced for both corporations and individuals. When considering plans it isn't just the stated rate (12,25,35), or the effective tax rate (what happens after deductions and so on), but also how much income the government receives. This post shows that there despite the drop in income taxes, government receipts have more or less stayed constant for the past 70 years, however, corporate tax receipts have fallen dramatically. Using data from the Tax Policy Center, the graph below shows income tax receipts as a percentage of GDP and the highest marginal tax rate: Note that tax receipts have more or less stayed the same (as a fraction of GDP) regardless of the highest rate. That's because the vast majority of taxes come from the middle. However, the peak of income tax receipts com after the Clinton tax increases in the 90's. It wasn't just the increase in taxes, but also the roaring economy, cutting into the standard arguments of higher taxes hampering growth. In a previous post, I show that tax increase actually lead to higher future growth and tax decreases are more or less ineffective. The graph that I find much more compelling is that of Corporate Taxes: Given how much corporate tax receipts have fallen since WWII and how little corporations contribute to the government, it seems strange that they should receive such a large tax cut (35 to 20 percent). Again there have been plenty of discussions of the effective tax rate, which is actually on par with the rest of the world. It is possible that such a decrease would result in no change in receipts (observe the late 80's), but the political signal seems to say: corporations get a huge cut in burden even though they don't pay very much to the government, and the vast majority of people who do pay get very little change to their tax burden.
The WSJ Economic forecasters report on two housing market indicators: FHFA housing price index growth, and housing starts. Admittedly I do not spend a lot of time forecasting housing, but the following graph makes sense to me: Over time forecasters are reaching a consensus of the likely value of housing starts in December. It is a little lower than the mean and median at the beginning of 2016, but still easily within the range of forecasts at that time. The economy in general has not been as robust as people thought it might be (a strong recovery has never materialized), and so the decline in consensus is consistent. However, the following graph confuses me: We do not see a similar convergence to the mean, and instead we observe the forecast becoming more skewed over time (lots of high forecasts a few very low forecasts). We are only three months away from the realization and there is the same spread between the 5th and 95th percentiles as 21 months ago. What would cause some of the forecasters to hold such low housing price growth expectations relative to their peers? The only thing I can think is that those few outliers must anticipate an immediate and steep decline in housing prices.
On another note: I wonder if the change in forecast averages (higher prices and lower quantities) might indicate the the supply-side factors (production/construction) dominating the housing market. That is productivity is lower than forecasters expected. Whether that is low enough to signal a coming recession is not clear, but I keep looking at this forecast data with a fair amount of pessimism. The next recession surely is on the horizon, some commentators a recession quickly approaching (usually citing the historically strong stock market) while most point to labor market slack and lack of inflation as signs the the recovery continues to progress. Forecasters, however, have signaled a turning point around the end of 2018, at least in terms of the unemployment rate: This v-shape has been well defined since the end of 2016, and while it may not perfectly correlate with the beginning of the next recession, it does provide some insight. Because we are rapidly approaching the close of 2017 there are two other variables that might give further indication of an impending recession, payrolls forecasts and recession probability: At this point, both of these year-ahead forecasts have been stable for the past few months. This past month the recession probability tick up quite a bit, and given the unemployment graph, one might expect it to continue to rise over the next six months. If the expected recession probability does start to increase and payrolls forecasts decline, that trough in unemployment will materialize around the beginning of 2019.
The dynamics in the labor market and higher education are remarkably different depending on whether we look at males or females. This post will highlight those differences and specifically look at how the dynamics differ by the type university (for-profit vs not-for-profit). First just to illustrate the different long run dynamics and their importance we quickly examine the labor force. Male labor force participation has steadily decreased since the WWII. While female labor force participation has leveled off since the 90's, female participation is recovering from the great recession, whereas males participation continues to decline. The story is even more incredible if one looks at the unemployment rate. Pre 1980, the female unemployment rate was consistently above the male unemployment rate. Since the 80's an unusual pattern has developed. Men face higher unemployment rates during and after a recession, but then eventually unemployment rates equalize. We have only observed four such occurrences, so it is unclear (at least to me) why that is the case. However, we know that many of the long-run changes in the labor market in the US has been the shift toward high-skill, college-educated, workers. Women have enrolled in college and universities at a higher rate than men for many years, which may be what is influencing the differences observed in the labor force and unemployment rate. To investigate this I used the IPEDS data service and collected data on 1574 colleges in 2001 and 2015. What struck me as interesting is how the different types of higher education institutions interact over the gender over time. The table below presents the enrollment rates of public, private not-for-profit, and private for-profit schools in 2001 and 2015. Not surprisingly enrollment rates have dropped for both men and women over that time frame. In addition, the not-for-profit institutions have lower enrollment rates than for-profit institutions. While public institutions do have higher enrollment rates than private not-for-profits, in 2001 both more or less treated men and women the same. However, in 2015 females have a lower enrollment rate than males. If we compare for-profit to not-for-profit, the for-profits have actually improved their "gender bias." Why might these numbers look this way? It's likely that these institutions are responding to the general call for attention to diversity. Since more women enroll in college, colleges must try to attract more men. In that case, we should look at the ratio of applications to enrollment: As mentioned, more females apply and enroll in not-for-profit colleges. However, in 2001 the application to enrollment ratio was very nearly 1, but that value dropped in 2015.This means that relative to the male to female ratios in the applications there were fewer females actually enrolling relative to the males at not-for-profit institutions. This suggests that women apply to more colleges than men.
If instead we examine the for-profit schools we see the opposite dynamics. First, males were a higher percentage of the number of applications and enrollments (about 50% more in 2001!). But relatively speaking compared to the enrollment ratio, there were more females who should have enrolled in these for-profit institutions (about 5% more). That number improved from 2001 to 2015, but it still suggests that women are applying to more institutions then men. The final takeaway from this data: Women are applying to colleges at a higher rate then men, and that behavior has equalized across the types of colleges. That is, in 2001 men and women applied to the roughly same number of not-for-profit colleges, but more women applied to more for-profit colleges. In 2015, however, women applied to more colleges then men regardless of the type of college. Over that same time period it has become relatively easier for men to attend colleges. The new WSJ forecasts were released last Friday and it looks as if the recent data has caused the forecasters to be pessimistic about the short-term, but optimistic about the long-term. Forecasts for inflation and unemployment through 2018 worsened (unemployment ticked up, while inflation decreased), but consensus predictions for for both variables in 2019 improved. In addition, only the last two quarters of 2017 GDP growth were revised downward, and all subsequent quarters and annual projections rose. These general macroeconomic indicator forecasts were somewhat at odds with the changes in specific indicators like the ten-year bond rates and crude oil prices. Bond rates were all revised downward, despite increases of the expected federal funds rate in 2019. Crude oil prices are still expected to rise slowly over the next two years, but only reaching the low 52 dollar mark, instead of 53 or 54 from a couple of months ago. Despite the lackluster September employment report, payroll forecasts for next year rose by over 10,000 to 16,080. These numbers suggest that the recent data implies that the economy is sliding a little below the long-run growth path. As I pointed out while discussing the recent payroll report, long-run time-series dynamics seem to be dominating current forecasting (as opposed to structural modeling and forecasting). I believe these recent round of forecasts supports that idea, because this pattern of revision is consistent with the behavior we observe. To see what I mean look at Crude Oil Price forecasts: The graph above shows forecasts at different points in time (light to dark indicates old to new). All we see are level shifts (the intercept) holding the dynamics (the slope) the same. That suggest the new data are not changing anything about the fundamentals, which would alter the trajectory, but instead only reveal changes in the starting point of a more or less unchanged dynamic system.
But is that good news or bad news? The good news: there really isn't any bad fundamental news. The bad news: models based on dynamic systems are correct on average, but since they are essentially data driven, it makes forecasters appear to be agreeing with each other. So the recent drop in forecast uncertainty (defined as the standard deviation amongst forecasters), does not necessarily indicate that we know a lot about where the economy is heading. A previous post analyzed the cycles in stock and options volume. This post seeks to expand on that topic using a different technique. While that previous post examined individual companies, this post will look at the markets as a whole. Using a dynamic factor model to extract a common factor between stock and option trading volume I find that the common factor is mostly driven by stocks. In addition the impulse response functions indicate periodic dampening. A dynamic factor model takes two or more series of data and considers whether there are some common, unobserved forces (the factors) that influences those variables. For instance, in this stock and options exercise that common factor might be market enthusiasm. Since we don't observe the factor we can't actually give it a name. Dynamic factor models generate results that tell us how correlated the factor is with each variable and how much each variable contributed to determining the factor. I will use the NYSE trade volume data, and the VBOE options data, which are daily volume statistics from November 1, 2006 on. I find that the factor loadings (correlation of the variables with the underlying factor) are 0.42 and 0.30 for stocks and options, respectively. The coefficients of determination (the degree to which each variable contributed to the factor) are 0.99 for stocks and 0.19 for options. These numbers tell us that this unobserved factor can explain about a third of the growth rates for trade volume in stock and options markets. Further, stock market trade volume is the primary driver of the common force that drives these markets. Finally, we look to the impulse response function: An impulse response function is a graph of what happens to the variables of interest if there is a one time shock (increase or decrease of the variable). That is, suppose the factor increases by 1% today, and impulse response function test us the impact of that 1 percent growth have on the subsequent days of trading. A critical component of this is that there are no additional shocks on the subsequent days. For our data, you can see that the first period has an increase equal to the factor loading, if the factor goes up by 1, stock volume goes up by 0.42 etc.
The surprising part of this graph is the periodic dampening effect. This leads me to believe that the factor is measuring some sort of herd behavior or enthusiasm cycle (Keynes' "animal spirits" comes to mind). When there is a spike in trading, participants back off to assess the situation, as that happens, the market starts to look more attractive so they pile back in, leading to another (smaller) spike and so on. The numbers are in: huge revisions to the previous two months, and a meh of an initial estimate. Most analyses of this payroll report mention Hurricane Harvey and the potential impacts, which likely may have an influence in subsequent payroll updates. However, the greater concern should be how uncertainty about the future job market conditions influence the economy. The graph below shows the history of payroll forecast standard errors in the WSJ economic forecasts. Not surprisingly uncertainty peaked during the recession, but the gradual decline, observed over the past two years, should be welcome news. The lower uncertainty is also striking given the volatile news/political cycle we have been in for the past 6 months. A decrease in forecast uncertainty suggests that markets are comfortable with the direction of the economy. The forecast uncertainty during the recession was not because it was a recession, but rather that everyone was unprepared for its arrival and severity. Therefore, suggesting that low forecast uncertainty means a strong economy may not be wise.
Like much of the actual data coming in, forecasts are tepid. Essentially, forecasts are converging to their long-run means (or moving averages) because there is not enough information in the incoming data to give clear signals. Different models stress different variables, which is what typically gives the forecast uncertainty we saw during the recession. However, the general consensus we now observe implies that most forecasts are emphasizing the basic time-series aspects of the data. |
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