The recent release of second quarter GDP growth came in below WSJ expectations as well as a downward revision of first quarter growth (from 1.4 to 1.2). This new data follows weak inflation numbers from last week.
1. For the past two years expectations exhibit an upward bias. Which, if that bias holds true, means low future expected growth would indicate even lower actual growth. Projected growth in 2018 is 2.38 percent, and 1.94 percent in 2019. These are significantly below the administrations overly optimistic 3 percent promise.
2. Expected federal funds rates have been falling since the beginning of the year. Not by a lot, but it has been consistent, suggesting that new data will give the Fed pause in increasing their target rates.
3. Expected housing starts continue to fall. At the beginning of 2016 expected housing starts were quite robust, however, subsequent data have curtailed consensus optimism. If this expectation falls below 1200, that could be a signal the beginnings of a slow down in the housing market.
While there some signs of growth remain, these three sets of expectations should worry investors and the current administration. All indicate at the very least, that the economy will not grow particularly quickly in the coming year. In the worst case scenario we may see the beginnings of a recession by the end of 2018.
A common way to consider technological innovation and growth comes from patents. Today's post uses an unobserved components analysis to examine the level, trend, and cycle of US utility patents since the early 1800's. Early patent growth seems quite normal growing slowly with a few eras of slightly more inventions (WWII for example):
However, the pattern appears remarkable different from the 1990's on. The exponential growth can be traced to the beginnings of the computer age and in fact the decline in the early 2000's was a direct response to the "dot com" bubble. In addition the volatility of both the trend and the cycle appears increase over the past 2 decades.
Are we really adding that much technology to our lives? Economists typically think of technology and innovations as driving increases in standards of living. If so we should expect sharp increases in standards of living in the coming years. However, many patents created today cover mundane and/or incremental improvement to an existing technology or tool. For example, consider the light bulb, refrigeration and internal combustion engines. All had huge implications for the way we lived our lives and created dramatic improvements. Does a patent on how an app icon bounces or the precise way a phone's edge is beveled come anywhere close to that standard?
That's not to say that with the dramatic increase in patents we are not going to discover some grand invention that changes the world as we know it (can I get a Star Trek transporter please?). However, it does seem that patents are losing their usefulness as a measure of technological progress. In the mean time we can keep hoping. Energize!
The BLS recently released the new CPI and inflation statistics. This signals a weakening economy along the lines of Tim Duy's analysis. As Mark Thoma points out, the Fed does not have a lot room to defend the economy against a recession, and congress seems incapable of doing anything at the moment. The WSJ Economic Forecasters expectations indicate further trouble, since they are above actual inflation, but are dropping:
If lower inflation expectations exist in the rest of the economy we can expect slower growth in the coming months. Lower inflation expectations usually are a self fulfilling prophecy, since workers, firms, and households make decisions that reinforce the low inflation future. For example, firms might anticipate lower revenues and therefore lower prices in order to drive up sales.
How do these lower inflation expectations fit into the larger long term picture? Well, the same analysts forecast GDP growth at 2.38 and 1.94 in the 2018 and 2019, respectively. In addition, my previous post on Fed funds rate expectations uncertainty indicates that Federal Reserve credibility (at least in terms of future interest rates) has improved. Inflation expectations for 2017 are low, but expectations for 2018 and 2019 are both firmly above 2 percent. This suggests that analysts believe the economy will slow down in the next two years and that the Fed will take appropriate measures (with what little room they will have) to fend off or minimize a recession.
Endogeneity, or the chicken and the egg problem. Previously addressed in a post on Housing Prices in the US, this time we exam alcohol. Do we drink more during and after and recession because we are depressed? Or does the fall in income curtail our ability to purchase alcohol? Coming into this analysis, it seemed likely that the former was true, turns out, the latter seems to be true during the great recession.
The state level data collected comes from the Tax Policy Center and GeoFred. Surprisingly alcohol tax revenue per person has been increasing by about 2 percent per year, more or less keeping up with inflation. The graphs below show relative changes in Alcohol Tax Revenue per Person and per Worker in all 50 states (and DC), with a base year of 2009. While there are certainly a few states that saw large increase post recession, it is unclear whether we are observing increasing or decreasing growth rates.
To address that question we can calculate average growth for each state in the five years prior to 2009 and and the five years after. Calculating the difference of the average we find that growth of alcohol tax revenue per person declined by 0.35 percentage points while per worker it declined by 1.59 percentage points. If instead we choose 2007 as the mid point, so that the 18 months of the recession are part of the post period, the numbers for per person and per worker are a 1.46 percentage point decline and a 0.11 percentage point increase, respectively. Those results suggest that it's likely that the income effect dominates the "feeling depressed" effect.
Naturally with a rough analysis like this there are some caveats. Tax revenue is not the same as consumption, but I suspect they are highly correlated, particularly when the tax laws have not changed. To really be sure of this result one would need to control for increases (and decreases) in alcohol sales tax rates. In addition, the recession hit states disproportionately. A more proper analysis would take that into account. Finally, this analysis also lacks a way to control for substitution effects, that is, a switch from expensive to cheap alcohol.
The WSJ economic forecasting survey published mostly good news. Surprisingly, the positive jobs report did little to change forecasts of payroll employment, however it has lowered expectations of a federal funds rate hike by December:
Most of the consensus revisions saw improvements over the next six months to a year, most notably with the probability of a recession dropping by almost one percentage point to 14.77 percent. The consensus also revised oil prices in December down by almost 3.5 dollars to 47.44. Though predicted GDP growth for the year increased slightly, predictions for Q2 fell by 0.2 percentage points to 2.72 percent. The table below summarizes the changes in forecasts for some key variables.
Inflation revisions seem correlated with significant drops in crude oil prices, however the unemployment revisions seem at odds with the federal funds rate revisions. If unemployment is expected to be better, forecasters should be predicting increasing federal funds rates. Perhaps they believe that their lower inflation forecasts imply a more accommodating stance from the Fed.
Following up on Monday's post about the employment report, today's post will look at the most recent employment data using Hamilton's filtering method. The commenters on the Federal Reserve (see Tim Duy or Narayana Kocherlakota) have noted the uncertainty surrounding employment and inflation. Kocherlakota specifically sees weakness in employment and downplays inflation worries. Tim Duy is less prescriptive, but also notes the uncertainty. The graphs below demonstrate where this uncertainty is coming from.
Employment and weekly hours appear to be at their trend, or falling slightly below (not good), but initial jobless claims and unemployment are well below their trend (good). The most telling sign is the continued drop off in weekly hours, which could mean that employers have worked through the "slack" in the economy. The graph of real wages below corroborates that story:
From 2013-2015 real wages finally started to rise as weekly hours fell and the job market tightened. However, recent wage reports have indicated a return to stagnant real wages (meaning wages are only just keeping up with inflation). Real wages climb when employees have bargaining power, which happens when fewer qualified people apply for the same jobs. Despite a healthy June jobs report, the likelihood of sustained jobs growth seems to be dimming with each month.
Bill McBride at Calculated Risk, commented on the strength of the recent employment report. Indeed, the news was much rosier than expected. Tim Duy's Fed Watch agreed with market expectations of approximately 170,000 jobs. The WSJ Economic Forecasters expectations were slightly lower at 165,000 and they have typically underestimated payroll employment:
However, the graph above and the graph below hint at the Federal Reserve's continuing conundrum. Payroll employment seems to be consistently beating expectations, but inflation is lower than expected. The WSJ Forecast below is for June 2017 year over year inflation, and the trajectory of actual inflation it seems unlikely that we will break the Fed's two percent target.
The upcoming release of the July WSJ Economic Forecasts may provide more insight to where market participants think the Fed is heading. It is likely that the jobs report will boost both GDP and fed forecasts, but that relies on forecasters optimistic outlook on inflation.
Housing prices in the US have crossed their pre-recession levels as shown in the graph below. Housing prices, much like many economic variables, tend to have an upward trend, usually exhibiting compound growth. This post uses state-level data on real household income and housing prices to explore the concept of endogeneity, or the chicken and the egg problem.
In economics, we frequently need to disentangle a vicious (virtuous) cycle. An example of a vicious cycle, a poor people tend to be the ones who purchase lottery tickets, which comes first: being poor or buying lottery tickets. This type of story exists in house prices and income in Pennsylvania depicted in the graphs below.
Though real household income displays more volatility (the HPI is monthly and not seasonly adjusted) the general trends are more or less the same. What we might want to know is whether housing prices impact real incomes or the other way around. Clearly as people earn more income they can afford to purchase a house. However, as we saw during in the lead up to the great recession as housing prices start to soar, more individuals receive income from real-estate, either through sales, rentals, or "flipping."
Macroeconomists utilize the irreversibility of time as a way to control or mitigate endogeneity. Something that happens today surely cannot impact what happened yesterday. After converting the state-level data into annual growth rates we can evaluate how lags (old values) of data impact current values. The table below reports those results.
In these tables we see an interesting pattern. First, and not surprisingly, the lag of the dependent variables are all significant. Past performance influences future performance. Real household income exhibits a significant but weak negative response to the past. If last year was a good year this year is likely to be a little worse. In contrast, housing prices exhibit positive response to the past. If last years housing prices grew by the same amount as the previous four you can count on a year that is at least as good if not better.
The more important results are the off-lags, or how past prices influence income and vice versa. For housing prices, we see a relationship that is not too surprising. A build up of positive real income over distant past will lead to an increase in house prices. Intuitively, several years in a row of positive income growth leads to higher demand for houses, which drives up prices. The impact of housing prices on income is less clear. A rise in last years prices increases income (and maybe even the 3 year lag as well), which makes sense, since as price start to increase we can expect more sellers to join in. However, the 5 year lag is puzzling. I suspect that this is an artifact of the great recession.
What we found was that both stories have some truth to them. It seems that the strength and duration of the impact of income on prices seems more and longer than that of prices on income. Therefore, it seems more likely that the recent increases in housing prices are in response to increased real incomes rather than housing prices predicting future real income increases.
Technical notes: both regression included fixed effects by state. Data was from 1986 to 2015 for all fifty states and DC.
WSJ Economic Forecast participants report the probability of a recession within the next 12 months. The consensus of the June survey participants has that probability at 16 percent. It appears that Q3 GDP growth appears to play a critical role in forecasters recession probabilities. The table below reports the results of a simple regression.
A forecaster who predict zero GDP growth in Q3 would place, on average, a 40 percent probability on the chance of a recession in the next 12 months. For every one percent increase in predicted Q3 GDP growth, that average recession probability would decrease by 10 percentage points. The other quarters do not appear to have a statistical relationship with recession probabilities.
Taken altogether, while a recession does not appear likely to these forecasters, the third quarter plays a pivotal role in these forecasts. The current consensus is 2.6% growth in the third quarter, only slightly below Q2 expectations. If we see weakness leading into the third quarter, we can expect forecasters to start increasing the probability of a recession.