No succor from the QCEW series that the Walker Administration previously touted 
DWD released QCEW figures for first quarter 2014. Since the QCEW figures are not seasonally adjusted, I have estimated a series consistent with the BLS private nonfarm payroll employment series based on the QCEW data.
Figure 1: Wisconsin private nonfarm payroll employment (blue), path implied by Governor Walker’s August 2013 pledge (red), and employment implied by QCEW series (orange), all in thousands, seasonally adjusted. Source: BLS, DWD, and author’s calculations.
Figure 1 includes an estimate of the private nonfarm payroll series using the QCEW figures as input. By March 2014, the BLS series and the estimated are close, although the BLS establishment survey was higher for much of the preceding months.
This result confirms Wisconsin’s lackluster employment growth. As noted in this post, in order to hit the January 2015 target laid out by Governor Walker, the Wisconsin economy will need to generate 22.6 thousand net new jobs in each of every month until January. Mean job creation over Governor Walker’s term thus far has been 2.7 thousand per month. This suggests that it is unlikely that the goal will be achieved.
The implied employment level is obtained by regressing the log BLS series on the log QCEW series, along with a constant and monthly seasonal dummies, over the 2001M01-2014M03 sample. The elasticity is 0.96, the adjusted-R2 = 0.99, and SER = 0.0019 (much as in the earlier case, suggesting stability in the relationship).
In other news, “… tax collections were $281 million less than anticipated for the fiscal year that ended in June. That puts the two-year budget on pace to be at a $115 million shortfall by June 30.” In other words, the hit to tax revenues was $81 million more than I assumed in this post. See this for additional information regarding the full enormity of the shortfall.
And Kansas travels its own path
Bruce Bartlett brings my attention to this article noting Minnesota’s economic performance. This reminded me to check on the Philadelphia Fed’s forecast for the next six months, released earlier today. What’s interesting to me is the fact the cumulative growth gap between Minnesota and Wisconsin (relative to 2011M01) is forecasted to grow — rather than shrink — over the next six months.
Figure 1: Log coincident indices for Minnesota (blue bold), Wisconsin (red bold), Kansas (green), California (teal), and United States (black), all normalized to 2011M01=0, seasonally adjusted. Observations for 2015M01 are forecasts implied by leading indices. Source: Philadelphia Fed and author’s calculations.
For those who argue that because Minnesota fell further during the Great Recession so it should be growing faster than Wisconsin — well that’s just plain wrong (not that I believed in that particular snapback argument). Wisconsin fell 8.3% from 2007M12 to trough, while Minnesota fell 5.0% (both in log terms).
The cumulative growth gap between Kansas and the Nation is also forecasted to rise, from the current gap of 2.7%, to 3.2%, in just the next six months. The forecast from a simple ARIMA(1,1,1) estimated over the 1986M01-2014M07 period yields the same conclusion: the gap will widen.
The U.K. will press European Union leaders to consider blocking Russian access to the SWIFT banking transaction system under an expansion of sanctions over the conflict in Ukraine, a British government official said.
“There’s no doubt that in the short term restricting Russian usage of SWIFT would be extremely disruptive to Russian financial and commercial activities,” said Richard Reid, a research fellow for finance and regulation at the University of Dundee in Scotland.
Russian economic authorities themselves accede to the increasing likelihood of recession , just as non-Russian forecasters . Economists’s beliefs that no further sanctions would be imposed, cited in the Bloomberg survey, are likely to be tested by new information regarding the incursion of Russian forces in Ukraine. (Note: the ruble has hit a new low, despite likely forex intervention; MICEX again down, 1.32%, RTS cash index, down 2.11%).
It seems no longer so implausible that the worst case scenario for the Russian economy, including a cumulative 5% decline in output (IIF, March 2014), becomes a reality.
Figure 1: Russian real GDP q/q growth rate (non-annualized). Source: TradingEconomics. Last observation is for 2014Q1.
NATO released photographs on Thursday that it said shows Russian artillery units operating in Ukraine, and asserted that more than 1,000 Russian soldiers had now joined the separatists fighting there against the Ukrainian armed forces.
The MICEX is down 1.84% today; the Russian Trading System Cash Index is down 3.43%.
Thus far, risk indicators such as VIX are not evidencing upward spikes, but that could change very quickly.
Given the downturn in Western Europe — partly attributable in Germany to the slowdown in exports to Russia  — this is unwelcome news for those who are looking forward to recovery in Europe. This development certainly places greater emphasis on expansionary monetary policy, and relaxation of contractionary fiscal policies.
Update, 8/30 2:45PM Pacific: Additional information regarding Russian supply of main battle tanks to the separatists, from IISS
The 2000s began with the Federal Reserve narrowly missing the zero lower bound on short-term interest rates. Then the US needed a housing bubble to attain full-ish employment in the mid-2000s, a decade that ended with the US and other major economies falling into liquidity traps, where they have been ever since. The causes of this sustained shortfall in demand, termed secular stagnation, was the topic of a recent VoxEU.org ebook. In this post, based in part on my job market paper, I argue that one of the main sources of this economic “time of troubles” has to do with relative prices, trade, and the rise of China.
A Theory of Secular Stagnation: Trade and China
This thesis is not new. Ryan Avent, Acemoglu et al. (2013), Autor et al. (2013), Dean Baker, and Pierce and Schott (2014) all tell stories in which trade and the rise of China have had left marks on the US economy. In my preferred version, there were two factors, the rise of low-wage China and a sharp appreciation of the US dollar in the late 1990s (Figure 1, the green line shows the Fed’s RER index; WARP will be explained later) fueled in part by the Great Reserve Accumulation. These two events led to a persistently large trade deficit (Figure 1, blue line) and a collapse in US manufacturing employment (Figure 2). This collapse caused the Fed to keep interest rates very low in the 2000s just to attain investment levels consistent with full employment, contributing to the housing bubble. The flip side of a trade deficit is a capital inflow, which, via an accounting identity, implies that domestic savings is less than investment. Less savings means more debt, and thus the trade deficit could itself have caused Americans to become overleveraged, leading to a balance sheet recession once the housing bubble collapsed. Finally, due to hysteresis and balance sheet effects, an overvalued dollar in 2002 can continue to effect aggregate demand today. Historically, temporary appreciations of relative prices have lead to persistent trade deficits and persistently smaller tradable sectors. In addition, households are still trying to replenish their balance sheets by reducing spending. Fortunately, US relative prices no longer appear to be much higher than our trading partners as a whole (not so for much of Europe). On the other hand, the “Rise of China” is still far from complete.
What is the evidence that a temporary appreciation of US relative prices led to the surprisingly sudden collapse of US manufacturing employment? In my paper, “Relative Prices, Hysteresis, and the Decline of American Manufacturing”, I try to get at this question by studying the impact of relative price (or real exchange rate) movements on manufacturing. There are two key difficulties with answering this question.
The first difficulty, surprisingly, was to design a theoretically appropriate measure of the real exchange rate. Longtime readers of Econbrowser, of course, know from this post that commonly-used real exchange rate measures produced by the Federal Reserve and the IMF suffer from an index numbers problem. This problem is almost identical to “outlet substitution bias” in the CPI, the bias that arises when new stores, such as Walmart, open and offer lower prices than existing stores. This effective price reduction does not get reflected in the CPI. In the case of real exchange rate indices, China is essentially the new giant low cost entrant, wreaking havoc on various government statistics including the Fed’s widely used Broad Trade-Weighted RER index. In Figure 1 (green dashes), the Fed’s index is compared to a simple Weighted-Average of Relative Prices (WARP), which adequately reflects the growing role of trade with China, computed using price level data from version 8.0 of the PWT. In my research, I extend WARP back to 1820 for the US, and find that the US price level in 2002 had not been that high relative to trading partners since the worst year of the Great Depression. WARP, however, does not take productivity into account (Bangladesh may have low wages, but since it also has low productivity it may not be a competitive threat), and so I introduce two new series, a Balassa-Samuelson adjustment to WARP and Weighted Average Relative Unit Labor Costs (WARULC), both of which I use as my main measures of relative prices in my dissertation.
The second problem is ubiquitous in economics – how to identify a causal relationship as opposed to correlation. One strategy I use is to look at disaggregated, sectoral data. In the early 2000s, the manufacturing sectors that did the worst were those who were the most exposed to trade (and not just to China), and their worst years were when the dollar was the most overvalued (controlling for the most likely potential other factors, such as recessions and interest rates). Secondly, I look at several natural experiments, periods in which we can plausibly identify the cause of movements in relative prices. Several of the episodes I study are the US experience in the 1980s, when the Reagan deficits led to a sharp dollar bubble, and Canada’s experience since 2000, when a weak US dollar and rising oil prices led to an appreciation of Canadian relative prices. In each case, I find that relative price appreciations have a large adverse impact on trade, productivity, output, and employment concentrated in those sectors most exposed to trade. (I also found that productivity growth has not resulted in more jobs lost since 2000 than in previous decades when aggregate manufacturing employment was flat, a finding supported by others.) That the results appear to hold up out of sample in different time periods and countries give reason to believe that the relationship is causal.
The methodology in my study on manufacturing is readily apparent in Figure 3 below. In panel (a), I compare the most open manufacturing sectors in 1972 (where openness is the trade share of output) with the least open sectors. In the 1970s, when US relative unit labor costs were similar to our trading partners, there was no difference in employment (or output) performance of the most open sectors relative to the least open sectors. However, when US RULC’s appreciated sharply in the mid-1980s, the more open sectors lost ground relative to the sectors which were less exposed, controlling for other factors. Interestingly, once US RULCs returned close to fundamentals, the jobs lost in the most open sectors did not return – a striking example of hysteresis. Panel (b) of Figure 3 shows that much the same thing happened in the early 2000s. The finding of hysteresis is important, because the implicit model most economists assume when they think about trade policy, particularly vis-à-vis China, does not incorporate the potential for hysteresis.
Figure 3b: Employment Growth by Initial Openness – The 2000s Manufacturing Collapse (NAICS)
The Trade Deficit and the Rise of China: Big Enough to Matter?
Was the collapse in manufacturing large enough to lead to secular stagnation? My panel estimates imply that two million jobs were lost directly due to the appreciation of US relative prices through 2008. However, the input-output tables tell us that every dollar of manufacturing output requires 60 cents of input (a bit more than half of this is direct, the rest indirect) from other manufacturing sectors. This could seemingly put the actual number of jobs lost in manufacturing due to trade closer to three million. Additionally, manufacturing also consumes input from other sectors of the economy in a proportion similar to which it consumes other manufactures, and affects local labor markets and local government revenue. While we might not know what elasticity to apply, exactly, we do know that areas which lose manufacturing jobs typically struggle to attract other industries. If these areas were totally unable to attract other tradable sectors, then the long-run elasticity would simply be the inverse of the share of tradable employment in the economy. Defining tradable sectors very generously, I get a number of perhaps 3 or 4 to 1 for the US as a whole. Thus it isn’t hard to wind up with a total long-run shock to employment, in the absence of a central bank response, of more than 10 million. This is easily large enough to be the primary cause of secular stagnation, even if other factors, such as demography, are also at play.
Policy Implications: A Return to Free Trade
Now that we know one of the chief causes of the poor growth since 2000, how does this change our knowledge of what needs to be done? Certainly one implication is the necessity of encouraging China to let the free market determine the value of its currency, particularly as manufacturing sectors which compete with China continued to shed jobs through 2011 even as overall manufacturing employment increased. And there are still millions more US manufacturing jobs which could eventually be relocated. Unilateral attempts by the Fed and/or Treasury to weaken the dollar directly or via fiscal policy may have been helpful in the early 2000s, but they wouldn’t work against China with its capital controls, and would (rightfully) meet with international opposition. I favor an indirect approach: the Fed should aim for higher inflation, which functions as a tax on China’s estimated 2.5 trillion in dollar-denominated foreign reserves. This policy makes sense for other reasons given that US inflation has been extremely low for years and prime aged employment has only just started to recover. Can the Fed achieve higher inflation? In short, yes. All recent evidence points to the conclusion that, for better or worse, the limits of monetary policy in a liquidity trap lie in the minds of FOMC members rather than in their policy tools. One strategy the Fed and the ECB might want to consider is to stop tightening monetary policy when core inflation is well below target and there is mass unemployment – something both central banks have done repeatedly since 2009 (in emulation of the Bank of Japan during its lost decades?). And, the lesson of hysteresis tells us that the legacy of continued slow growth as the world waits for the Fed (and the ECB) to discover their wits will be diminished economic possibilities for years to come.
 It is worth mentioning that the PWT is being revised in a way in which WARP for the US could be more comparable to the PWT v7.1, in which case the relative price shock in the early 2000s would have been about as large as the appreciation in the 1980s. This likely would not change the basic story.
 In addition, I found that Japan’s price level was on average nearly twice that of its trading partners during its two decades spent in a liquidity trap.
This post written by Douglas Campbell.
“Energy regulation efficiency” and economic growth.
This particular piece of research was brought to my attention by Patrick R. Sullivan, who is fond of quoting talking points from the MacIver Institute, the National Center for Policy Analysis, Cato, in addition to the Pacific Research Institute. The study in question purports to show:
The most interesting relationship is between a state’s [energy regulation efficiency] ranking and its economic growth rate. High ranked states on average grow faster than those ranked low. Moreover, the higher rate of economic growth is associated with faster employment growth. Energy regulation can, therefore, be an important factor in determining the eventual prosperity of a state.
The authors painstakingly compile indices for all fifty states; the indices and aggregate index are reproduced in all their technicolor glory in Table 16 from the study.
They then show the statistics for the quintiles for energy regulation efficiency ranking and growth, and note a positive correlation.
[NB: As far as I can tell, the authors have used the nominal growth rates of GDP, rather than real (which is pretty odd); the reported growth rates are not expressed in annual rates]
The document notes:
Interestingly, the strongest relationship to ranking is a state’s growth rate. High ranked states have faster growth rates than those ranked low. Table 18 below provides 5-year and 10-year growth rates by quintiles. The average growth rates for states within the quintiles follow a consistent trend. Over the 10-year period 2002-2012, states in the top quintile had on average cumulative growth rates that were more than 20 percentage points higher than those in the bottom quintile. The top quintile also had growth rates that exceeded those of middle three quintiles. The bottom quintile’s cumulative growth was lower than most of these other three.
The table and the text are notable for the omission of any discussion of statistical significance. At this point, any researcher worth his/her salt should hear the sirens going off. (The hand-waving in footnote 81 is also a tip-off, and also some cause for hilarity.)
If one estimates the regression analog to Table 18, using ordered probit, one finds that the relationship is not statistically significant at the 10% level for the ten year growth rate.
Of course, there is no particular reason to enter the dependent variable as a ranking (which requires the ordered probit estimation). One could just use the average growth rate (over ten or five years) as the dependent variable. Here are graphs of the underlying data.
Figure 1: Average ten year growth rates 2003-13, by state, vs. Pacific Research Institute energy efficiency ranking (higher, such is 1, is “better” than lower, such as 5) (blue circles); nearest neighbor (LOESS) fit (red), window = 0.3. Source: BEA, Pacific Research Institute, and author’s calculations.
Figure 2: Average five year growth rates 2007-12, by state, vs. Pacific Research Institute energy efficiency ranking (higher, such is 1, is “better” than lower, such as 5) (blue circles); nearest neighbor (LOESS) fit (red), window = 0.3. Source: BEA, Pacific Research Institute, and author’s calculations.
I estimate the regression:
y = α + β×rank + u
Where y is an average annual growth rate, and rank is a quintile rank. Estimation using ten year average growth rates leads to:
y = 0.025 – 0.002×rank + u
Adj.-R2 = 0.05. bold face denotes significance at 10% MSL, using heteroskedasticity robust standard errors.
Using five year average growth rates:
y = 0.018 – 0.003×rank + u
Adj.-R2 = 0.06. bold face denotes significance at 10% MSL, using heteroskedasticity robust standard errors.
Notice that dropping North Dakota (ND) further reduces statistical significance. Moreover, any borderline statistical significance is obliterated by inclusion of a dummy for states with large oil reserves (top ten). I include a dummy variable into the ten year growth rate regression, and obtain:
y = 0.018 – 0.001×rank + 0.012×oil + u
Adj.-R2 = 0.20. bold face denotes significance at 10% MSL, using heteroskedasticity robust standard errors.
Notice that the adjusted R2 increases substantially with the inclusion of the oil dummy, indicating the minimal explanatory power associated with the Pacific Research Institute index.
It is astounding to me that an organization can spend all the resources to compile these indices, and yet not do the most basic statistical analysis taught in an econometrics course. It is even more astounding that some people take these results at face value. Apparently the aphorism that there is “one born every minute” holds true.
Roger Farmer has taken a new look at an issue concerning the Federal Reserve’s program of large-scale asset purchases (referred to in the popular press as “quantitative easing”) that I’ve been discussing on Econbrowser and in my research with University of Chicago Professor Cynthia Wu for some time.
One theory of how LSAP might affect interest rates is that if the Fed takes a large enough volume of long-term securities out of the hands of private investors, the drop in net supply could in principle lower the yields on those securities. In the years since the collapse of Lehman in September 2008, the Fed’s total assets more than quadrupled, and the Fed’s share of the total Treasury debt that was held in the form of longer-term securities increased significantly.
But at the same time that the Fed was buying long-term Treasury debt in order to take these securities out of the hands of private investors, the Treasury was significantly increasing the fraction of debt that it issued that came in the form of longer maturities. The net result was that the fraction of Treasury debt that was of 2-10 year duration and that was held outside of the Federal Reserve actually increased significantly, despite the Fed’s bond-buying efforts. Thus according to one theory of how LSAP might affect the economy, all the Fed’s LSAP accomplished was to incompletely offset a contractionary impulse originating from the Treasury’s separate maturity issuance decisions.
A number of researchers have documented that on the days that changes in LSAP policy were announced there were noticeable changes in long-term interest rates; see for example Christensen and Rudebusch (2012). This evidence suggests that LSAP did indeed matter for yields. Notwithstanding, at the Fed’s Jackson Hole conference two years ago, Columbia Professor Michael Woodford noted that the net move in long-term interest rates over the entire duration of these programs provided little evidence to convince the skeptics. The 10-year yield actually rose between the beginning and end of the Fed’s first bond-buying program (LSAP1). The common interpretation is that some of the declines in the yield prior to the first purchases were actually a response to announcements and anticipation that the program was coming. Even so, any effects during the year of the program were evidently outweighed by other factors influencing bond yields that were changing at the same time. Nor are effects during the actual period when the Fed was engaged in its second bond-buying program (LSAP2) nor its deliberate effort to extend the maturity of its portfolio (MEP) particularly easy to spot in a long-term graph.
The market’s response in the minutes following new announcements about the Fed’s bond-buying program make it hard to deny there is some effect. For example, last year yields rose sharply attributed in part to speculation that the Fed would soon announce a slowdown in the rate of bond purchases, which some observers described as the market’s taper tantrum. Nevertheless, developments in addition to speculation about the Fed’s LSAP plans also contributed to the rise in rates at that time.
The natural reconciliation of this evidence is that it was not the bond purchases themselves that moved the market, but instead it’s what the purchases signaled in terms of future Fed decisions on short-term rates. Federal Reserve Bank of San Francisco researchers Michel Bauer and Glenn Rudebusch have provided some interesting evidence consistent with that interpretation.
My view is that LSAP did have an effect, but that it was primarily through this signaling channel rather than a direct impact on bond markets of the Fed’s purchases of long-term Treasury securities.
The BEA has released a new quarterly Gross State Product (GSP) series for states — a tremendous innovation for those of us interested in tracking state economies.
One interesting comparison is Minnesota vs. Wisconsin. With GSP data, one can look at the ratio of per capita income expressed in Chained 2009$ (rather than the ratio of indices, as I have been doing using the Philadephia Fed data, e.g., ).
Here is the log ratio:
Figure 1: Log ratio of GSP per capita of Minnesota to Wisconsin (blue) and five quarter centered moving average (red). GSP in Ch.2009$, SAAR; population is annual resident population, interpolated using quadratic match average. Source: BEA, FRED, and author’s calculations.
Notice that Minnesota starts moving up and away from Wisconsin around 2011. That’s eyeballing. Using a structural break test (n-step ahead recursive residuals test) confirms that conjecture.
Figure 2: n-step ahead recursive residuals test for constant percent difference in MN and WI per capita income.
There is a break with borderline significance indicated at 2011Q4.The recursive residuals indicate that Minnesota per capita income has been statistically significantly higher than Wisconsin since end 2011 (equivalently, Wisconsin’s per capita income lower than Minnesota’s).
Update, 8:45AM Pacific, 8/23: Louis Johnston also delves into the new quarterly data series, focusing in with much greater detail on Minnesota.
The Chinn-Ito index revised and updated to 2012 is now available here.
Figure 1 depicts the evolution of this measure of financial openness for three groups of countries:
Figure 1: Evolution of KAOPEN for Different Income Groups. Higher values indicate greater financial openness.
The Chinn-Ito index (KAOPEN) is an index measuring a country’s degree of capital account openness. The index was initially introduced in Chinn and Ito (Journal of Development Economics, 2006). KAOPEN is based on the binary dummy variables that codify the tabulation of restrictions on cross-border financial transactions reported in the IMF’s Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER). This update is based on AREAER 2013, which contains the information on regulatory restrictions on cross-border financial transactions as of the end of 2012.
The CI index is a de jure measure, based on written (and self-reported) information (a characteristic it shares with most extant indicators). Moreover, it does not contain as much detailed information regarding the types of restrictions, nor does it differentiate between restrictions on inflows and outflows. On the other hand, it does cover a wider range of countries over a longer span than all others. (See Quinn, Schindler, and Toyoda (2011) for a discussion of the various measures.)
Capital openness measures are useful for assessing the international trilemma — the proposition that one cannot simultaneously pursue full financial openness, exchange rate stability, and monetary autonomy — as discussed here.
It’s of interest that financial openness has declined for emerging market economies (on the whole — this is a simple average); this result is not inconsistent with the increasing use of capital controls to stem capital inflows.
Financial openness is germane to the prospects for the rise of new international currencies (e.g., Eichengreen (2013), Chinn (2012), Ito and Chinn (2014), and Prasad (2014)). Most studies concur that one of the pre-requisites for international currency status (thus far) is financial openness, so it’s useful to inspect the evolution of openness in the BRICs (compared to the US).
Figure 2: Chinn-Ito KAOPEN series for Brazil (blue), Russia (black), India (green), China (India) and US (teal). Source: Chinn-Ito.
It’s interesting that China and India have made no progress toward liberalization in recent years on paper. Russia was liberalizing at least through 2012. What happened in 2013 is a question that will have to await the next AREAER.
I saw an interesting statistic in the latest issue of Journal of Economic Perspectives. If you rank North American economics Ph.D. programs in terms of the publishing success of their median student in the first six years after graduating, UCSD comes in second.
Number one? Seems to be Princeton.
Today the Philadelphia Fed released coincident indices (measures of aggregate economic activity) for the states and the US. Wisconsin outperforms Kansas — a very low bar — and yet has lagged all her neighbors.
Consider first Wisconsin compared to Kansas (like Wisconsin an ALEC darling) and Minnesota and California (not ALEC darlings).
Figure 1: Log coincident indices for Minnesota (blue), Wisconsin (bold red), Kansas (green), California (teal) and US (black), all normalized to 2011M01, seasonally adjusted. Source: Philadelphia Fed, and author’s calculations.
Only the disastrous trajectory of Kansas’s economy makes Wisconsin’s performance look tolerable.
Some observers have argued that the dissimilarities between these states invalidates the preceding comparison. However, the comparison with Wisconsin’s neighbors does not cast Wisconsin economic performance in a noticeably better light.
Figure 2: Log coincident indices for states adjoining Wisconsin — Minnesota (blue), Wisconsin (bold red), Illinois (teal), Iowa (green), Michigan (purple), and US (black), all normalized to 2011M01=0, seasonally adjusted. Source: Philadelphia Fed, and author’s calculations.
It is interesting to observe that — despite the ample scorn heaped upon Illinois by conservative commentators (including in the comments section of this weblog) — Illinois has outperformed Wisconsin for essentially all of the past three and a half years. And, as I mentioned, Wisconsin lags the (regional) pack.
(Note that regardless of whether one normalizes to the previous trough or peak, Minnesota outperforms Wisconsin.)
Update, 12:40PM Pacific, 8/21: Gross State Product figures released yesterday, extending up to 2013Q4 (i.e., to the end of last year) confirm the relative poor performance of Kansas and Wisconsin.
Figure 3: Log Gross State Product for Minnesota (blue), Wisconsin (bold red), Kansas (green), California (teal) and US (black), all normalized to 2011Q1, seasonally adjusted at annual rates, in Chained 2009$. Source: BEA (August 20, 2014), and author’s calculations.
As of 2013Q4, cumulative growth since 2011Q1 was 2.4% higher in Minnesota than that in Wisconsin; for the Nation as a whole, it was 2.1% higher than that in Wisconsin.
Following up on last Thursday’s post, here is a depiction of how Wisconsin and Kansas — ALEC darlings — fare against Minnesota and California.
Figure 1: Log nonfarm payroll employment for Wisconsin (red), Minnesota (blue), California (teal), Kansas (green) and the US (black), all seasonally adjusted, 2011M01=0. Vertical dashed line at beginning of terms for indicated governors. Source: BLS, and author’s calculations.
The negative correlation between a high ALEC-Laffer economic outlook ranking and economic growth remains negative.
I’ve read several comments lauding the move toward a structural budget balance in Wisconsin under Governor Walker’s administration. I decided to take a look at what the actual evidence for a surplus is, and what the economic impact has been of policies purported to improve economic performance.
Figure 1 depicts the budget balance by fiscal year over the past decade and a half, plus projections from the Legislative Fiscal Bureau.
Figure 1: (Negative of) General Fund Amounts Necessary to Balance Budget, by Fiscal Year, in millions of dollars (blue bars); and estimate taking into account shortfall of $200 million (red square). “structural” denotes ongoing budget balance, assuming no revenue/outlay change associated with economic growth. Source: Legislative Fiscal Bureau (May 22, 2014), Table 6, Wisconsin Budget Project, and author’s calculations.
Recent actions have pushed the balances in the 2015-17 biennium into deficit. But even the news for the 2013-15 biennium has turned dark. By June 2014, it was apparent that there were both tax revenue shortfalls and outlays in excess of planned [Peacock/WBP]. I’ve included an alternate projection for FY2013-14, shown as a red square in Figure 1. There are also some downside risks to the balance on the spending side (primarily Medicaid/Badgercare), which I have not included, so this is not the worst case scenario.
To some extent, the shortfall in revenues is not surprising. The administration has implemented a contractionary fiscal policy (cutting taxes , cutting spending to increase the budget balance which drives down output and hence revenues. The main tax cuts were:
- An income tax rate reduction, included in the state’s two-year budget that passed in 2013, which reduced revenue by $648 million over two years;
- A 2013 property tax cut of $100 million; and
- A 2014 tax package that further cut income tax rates and also included another property tax cut. The combined package reduced revenue by $507 million over two years.
The cuts amounted to $1.9 billion. 
My predictions of what would occur have been borne out in actual developments in aggregate economic activity. Wisconsin has experienced substantially slower growth than it otherwise would have.
Figure 2: Growth rate of real GDP for Minnesota (blue), Wisconsin (red), and United States (black), calculated as log first differences. NBER defined recession dates shaded gray. Source: BEA, NBER, and author’s calculations.
Note that a regional comparator, Minnesota, that embarked upon a different fiscal path has experienced substantially faster growth, as documented above. Forecasts indicate continued lagging.
To sum up: A procyclical fiscal policy has been undertaken in the past three and a half years, with predictably counterproductive results. The depressed level of economic activity has resulted in a revenue shortfall.
The unfunded liabilities of the San Diego County Employees Retirement Association have increased every year for the last five years, reaching $2.45 billion last year, more than quintuple the level in 2008. The calculation of how big the shortfall is assumes that the fund is going to be able to earn a 7.75% return on its investment after subtracting administrative costs. If it earns less than 7.75%, the shortfall will be even bigger. A 10-year Treasury bond currently pays 2.4%, and a typical stock has a dividend yield under 2%. So what do you do if you’re in charge of the system’s $10 billion in assets?
One thing you could do is ask the taxpayers for more money right now.
What the SDCERA board did instead was to approve a strategy that is supposed to increase the return on the fund’s assets.
And how do you do that, exactly? Suppose you invest $50,000 outright in the S&P 500. If the market goes up 1%, next year it will be worth $50,500, and you’ve earned 1% on your investment. (I’m going to ignore the role of dividends, which complicate a little the calculations I’m about to describe, but don’t change the basic story.)
Or you could use your $50,000 to cover the initial margin requirements for a couple of S&P 500 futures contracts, which would have a notional value of around a million dollars. That means that if the market goes up 1%, the notional value goes up to $1.01 million, and you get to keep all the $10,000 gain for yourself. That’s a 20% return on your initial $50,000 investment– not bad money in a ho-hum market!
Unfortunately, the downside is that if the market goes down 1% rather than up, you lose 20% on your investment. Oh well, what’s life without a little excitement?
The San Diego Union Tribune and the Wall Street Journal reported last week that the board of directors for the San Diego County Retirement Employees Retirement Association has decided to use more strategies like these to increase the funds’ effective leverage to around 100%, meaning when the underlying investments go up 1%, the fund earns 2%, and when they go down 1%, the fund loses 2%.
Now, there are two reasons you might give for such a strategy. The first is you’re really, really good at making the right picks, so we’ll only magnify positive returns and never magnify a negative return. Of course, these are the same folks who lost big on the Amaranth hedge fund fiasco. But maybe they’ve learned from past mistakes, and this time they’ll get everything right.
Or a second argument you could give is that the county pension fund is particularly well suited to take on extra risk, having (you think?) a base of taxpayers quite willing to fork over the extra funds necessary to cover the additional shortfall we’ll experience in a down market, even though they’re apparently not willing to pony up the funds for the shortfall that’s already on the books.
Here’s what Barry Ritholtz thinks:
I am not fond of forecasts, so instead, I will offer one of two likely outcomes: Eventually, San Diego County’s pension fund blows up. The losses are spectacular, and the county taxpayers are saddled with billions in new tax obligations. Alternatively, the townsfolk figure out how much risk is being put on their shoulders, and fires everyone involved, from the pension board to the advisers to anyone who voted for these shenanigans.
I have seen this movie before. I know how it ends.
Continued stagnation in July.
Wisconsin’s Department of Workforce Development (DWD) released today figures for July employment (release here).
Figure 1: Log private nonfarm payroll employment in United States (black), and in Wisconsin (red), both seasonally adjusted, and normalized to 2011M01=0. Source: BLS for US, and DWD for Wisconsin, and author’s calculations.
Figure 2: Log nonfarm payroll employment in United States (black), and in Wisconsin (red), both seasonally adjusted, and normalized to 2011M01=0. Source: BLS for US, and DWD for Wisconsin, and author’s calculations.
There were downward revisions to both series, pushing down the already pathetic June growth rates.
(Note that the more volatile estimates derived from the household survey indicate a loss of 1.4 thousand jobs in July, on a seasonally adjusted basis.)
Since January 2011, Wisconsin private nonfarm payroll employment growth has been a cumulative 2.8% lower than that for the United States as a whole, while NFP has been 2.1% lower.
According to these figures, private employment is now 105.1 thousand below the trend consistent with his promise to create 250 thousand net new jobs by the end of his first term (reiterated one year ago).
These figures are likely to be revised downward when the Quarterly Census of Employment and Wages (QCEW) numbers are released (see here). Recall, Governor Walker was for the QCEW before he was against the QCEW…(or at least preferring the establishment survey).
Figure 3: Private nonfarm payroll employment in Wisconsin (red), linear trend consistent with 250,000 net new jobs by 2015M01 (dark gray), and employment forecast from Wisconsin Economic Outlook (March 2014), quadratic interpolation. Source: DWD, Wisconsin Economic Outlook, and author’s calculations.
In order to hit the target by January 2015, the Wisconsin economy will need to generate 22.6 thousand net new jobs in each of every month until January. Mean job creation over Governor Walker’s term thus far has been 2.7 thousand per month. This suggests that it is unlikely that the goal will be achieved.
Data on Kansas, which has experienced stagnant employment in recent months , has not yet been released. I will update as soon as they are available. For depiction of June data for CA, MN, KS, relative to WI, see .
Update, 11:30AM 8/15: The Wisconsin GOP has their take on the employment situation: “Wisconsin is Back On, More Jobs Created Under Scott Walker”.
Consider this prognostication from 2011:
Americans face the most predictable economic crisis in this nation’s history. Absent reform, the panic ahead is no longer a question of if, but rather when. A deterioration of confidence by investors in government’s ability to pay its bills will drive interest rates up, increasing borrowing costs for government, small businesses and families alike. A vicious cycle of debt will compound upon itself; the available exit options once the crisis hits will be limited; and all will involve pain. (p.59)
Writing about the President’s FY2012 budget:
…Autopilot spending will soon crowd out all other priorities in the federal budget, with spending on Medicare, Medicaid, Social Security and interest on the national debt eclipsing all anticipated revenue by 2025. Borrowing and spending by the public sector will crowd out investment and growth in the private sector. … [emphasis added] (p.56)
Those quotes are from Representative Paul Ryan’s FY2012 “Path to Prosperity”.
It is all the more surprising, then, to consider actual data that has come out over the past three years since publication of that document.
First, consider the sources of saving available to the US economy, by reference to the National Savings Identity:
(S-I) + (T-G) ≡ CA
where S and I are private savings and investment, (T-G) is the public sector budget balance, and CA is the current account. Figure 1 shows the evolution of these series, all expressed as a share of nominal GDP (note that I have omitted the statistical discrepancy for the sake of clarity).
Figure 1: Private sector net savings (S-I) (blue), public sector net savings, or budget balance (T-G) (red), and current account (CA) (green), all as a share of GDP. NBER defined recession dates shaded gray. Source: BEA, 2014Q2 advance release, NBER, and author’s calculations.
Notice the drastic reduction in the public sector deficit, even prior to the implementation of the sequester in March 2013. The current account balance has improved substantially since the onset of the Great Recession — in line with a conventional macro model that relies on a marginal propensity to import — although it has stabilized at about 2.5% of GDP.
The identity is merely that — an identity, useful for accounting purposes. However, it does make clear that the US is not borrowing as much from the rest of the world as it was during the G.W. Bush years (peaking at about 6.2% annualized in a couple quarters). That is, the rest-of-the-world has thus far been happy to finance the US aggregate saving needs (as well as that of the Federal government — more on that in a bit).
The improvement in the public sector balance, particularly the Federal one, is quite marked if one abstracts from cyclical effects. Figure 2 presents (by fiscal year) the actual Federal budget balance and the one abstracting from automatic stabilizers, normalized by the potential GDP.
Figure 2: Federal budget balance (blue) and balance without automatic stabilizers (red), as percent of potential GDP (%), by fiscal year. NBER defined recession dates shaded gray. Source: CBO, Budget and Economic Outlook, February 2014, and NBER.
Hence one should not be too surprised that the apocalyptic vision regarding interest rate trajectories held by Representative Ryan in his various “Paths” have not come to pass. In fact real borrowing costs for the US government have remained quite low by historical standards.
Figure 3: Ten year constant maturity Treasury yields (blue), ten year constant maturity yields minus ten year (median) expected inflation (red +), and ten year constant maturity TIPS (black). NBER defined recession dates shaded gray. Source: Federal Reserve via FRED, Survey of Professional Forecasters, and author’s calculations.
Substantial (portfolio) crowding out of private investment as feared by Representative Ryan is unlikely to occur if interest rates do not rise appreciably (relative to the counterfactual); and in fact if investment depends on output, then decreased government spending and hence government deficits might actual yield greater crowding out than the counterfactual (see this post for discussion).
Is it all the Fed’s doing? Fed holdings of Treasury debt (all maturities) were about 18.4% of total publicly held Treasury debt, as of March 2014. Compare this against the previous peak of 17.3% in March 2003, and 24% in June 1974. So Fed purchases are part of the story, but not by any means all of the story. In fact, one might point to another factor as being even more important, namely foreign demand. Thus far the foreign sector (both private and public) seems quite content to continue acquiring US Treasurys, despite the best efforts of some policymakers to drive up the risk premium; see also . Figure 4 depicts foreign holdings of long term US Treasurys.
Figure 4: Foreign holdings of long term US Treasurys (blue) and holdings of the People’s Republic of China (red). +,x indicate benchmark data, line indicates TIC monthly estimates. Source: Treasury.
Looking at levels of holdings in dollars can be misleading, given the increasing size of US Federal debt. However, normalizing doesn’t yield a substantially different perspective. The ratio of long term US Treasurys to total publicly held Federal debt has held fairly constant over the past several years.
Figure 5: Foreign holdings of long term US Treasurys (blue) and holdings of the People’s Republic of China (red), all divided by Federal government publicly held debt (both short and long term). +,x indicate benchmark data, line indicates TIC monthly estimates. Source: Treasury, FRED, and author’s calculations.
One reason why Treasurys have been so favored during the global financial crisis and aftermath was the safe haven aspect of US Federal debt — not only in terms of default risk, but also in terms of geopolitical risk. To the extent that the VIX proxies for these factors — including geopolitical ones like Ukraine/Russia, one should expect (1) an obvious inverse correlation and (2) an abatement of the demand once risk dissipates. On (1), consider Figure 6.
Figure 6: Ten year constant maturity Treasury yield (blue) and DJIA VIX, divided by 10 (red). Observations for August are for August 8. Source: FRED.
I think there is something to the inverse correlation, in addition to the clear downward trend in the nominal ten year government yield in the background. But it clearly isn’t everything.
Regarding (2), it’s not obvious to me that there is going to be a sustained drop in uncertainty; if that is the case, elevated demand for US Treasury’s may be a persistent condition. That in turn suggests that the idea that long term funding costs for the Federal government (including in real) reverting to long run norms might not occur for some time. By the way, these arguments abstract away from the discussion of secular stagnation, as modeled by Eggertsson and Mehrotra (2014), among others. Secular stagnation merely adds onto the reasons why one should expect low Treasury borrowing costs going into the future.
What are the ramifications of an outcome where Federal funding costs are diminished for an extended period of time? One implication is the growth of government debt-to-GDP is less pronounced than, for instance, what was implied in the CBO scenarios used in Kitchen-Chinn (2011) (see discussion in this post). The urgency for rapid fiscal consolidation is thus commensurately reduced.
I was interested to take a look at our recent weak economic performance from a longer-term perspective.
The graph below plots private domestic fixed investment as a fraction of GDP for the United States going back to 1929, along with the median value for this fraction over the whole period. We always see investment fall as a fraction of GDP during an economic downturn. And in prolonged economic slumps– the Great Depression and its aftermath in the 1930s, and more recently the Great Recession and its aftermath– investment as a fraction of GDP remains significantly below normal for a prolonged period.
Clearly the feedback runs in both directions. When the economy is doing badly, nobody wants to invest, and when investment is low, there’s that much less spending to contribute to GDP. Interestingly we see the same pattern in Japan. Japan’s anemic economy over the last two decades has been characterized as a prolonged period of below-normal investment spending.
It’s interesting also to look at the individual components of investment. They each exhibit the same broad comovements with output, but with some idiosyncrasies. Notably, residential fixed investment accounted for almost all of the gain in investment as a fraction of GDP in the years just prior to the Great Recession. And in 2013, residential investment was still below normal as a fraction of GDP, and is the primary reason that investment overall remains lower than normal.
Allowing Private Sector Innovation Holds the Most Promise, if Government Doesn’t Impede Progress
Today we are fortunate to have a contribution written by Clifford Winston, Searle Freedom Trust Senior Fellow at the Brookings Institution. This post is based on a more extensive analysis available here.
Since 2005 Congress has not passed a long-term transportation bill and has instead engaged in political theatre about how to revive a wheezing Highway Trust Fund that is running a deficit. This embarrassing vaudeville act needs to be yanked off the stage with a cane because increasing government spending has not significantly improved infrastructure performance. Instead, implementing private sector technologies hold the most promise for improvements for the American traveler. Government can help by not impeding private sector efforts.
There is no “strategy” in the public sector’s decades-long history of increasing spending to build the nation’s way out of congestion and the public sector is unlikely to ever develop a sustainable strategy that could improve infrastructure performance. Accordingly, there are three ways that the private sector could help: purchase infrastructure facilities from the government and operate them more efficiently (privatization); develop technological innovations that the public sector could implement to improve current infrastructure performance; and make technological advances that greatly improve the operations of transportation modes that use the infrastructure.
All the options are promising, but outright privatization may be premature without experiments in the United States given the mixed experience with privatization in other countries. The best course of action is to rely on the private sector to develop technological advances in the major modes and for the government not to impede those advances. Policymakers have shown a decided lack of interest in implementing technological innovations to improve transportation. For example, they could encourage the use of technologies such as:
- GPS devices, Bluetooth signals, and mobile software applications to provide motorists with real-time information about traffic speeds, volumes, and conditions on alternate routes, thereby allowing drivers to make better informed decisions about their routes.
- Weigh-in-motion capabilities to provide real-time information about truck weight and axle configurations to do away with weigh-stations and to set efficient pavement-wear charges, which would encourage truckers to shift to vehicles with more axles that do less damage to roads.
- Governments also could apply adjustable-lane technologies and variable speed limits, adapting to traffic flows, and could set real-time tolls to encourage drivers to explore alternative routes, modes, and times of day to travel.
- Efficiency in air travel could be enhanced through technologies such as heated runways, which would reduce delays caused by time-consuming manual snowplowing; advanced screening technologies, such as full-body scanners and biometrics, to speed security measures; and the adoption of a next-generation satellite-based air traffic control system known as NextGen.
Unfortunately, the government has a status quo bias, which impedes the adoption of new technologies and slows economic progress.
Fortunately, the private sector does not share this bias. In spite of public sector foot-dragging, the private sector continues to innovate in myriad ways, such as: automobile safety technological advances, including electronic stability control, warning and emergency braking systems, speed alerts, and mirrors with blind spot warnings; airlines have installed more powerful and efficient jet engines and are planning to incorporate improved wing designs to reduce fuel consumption.
Moreover, major innovations in the modes are on the horizon. There is no doubt that driverless cars and trucks can be operated effectively, gathering and reacting to real-time information about traffic conditions and eliminating human failings, such as distracted or impaired driving; digital communications and GPS could automate routine air traffic control measures; and drones could be used for commercial purposes—if the FAA would lift its ban on their use.
Those innovations and undoubtedly others could significantly improve the efficiency and safety of current infrastructure and significantly reduce the need for government to pass huge spending bills to expand transportation capacity.
This post written by Clifford Winston.
An ambitious plan to cut income taxes in Kansas will end up costing the state more money than it initially estimated after a key ratings agency downgraded the state’s debt on Wednesday.
Standard & Poor’s cited structural imbalances created by the tax cut in its decision to slice Kansas’s bond rating from AA+ to AA. That means Kansas will have to offer a higher interest rate to lenders when it issues new bonds.
The package of tax cuts, backed by Gov. Sam Brownback (R) and his conservative allies in the state legislature, was never offset with equal spending cuts, S&P said Wednesday. The lost revenue is expected to eat up much of Kansas’s budget reserves during this fiscal year; S&P said it expected the state to face a $333 million budget shortfall this year.
“In our opinion, there is reason to believe the budget is not structurally aligned,” S&P analysts wrote.
Moody’s has already downgraded Kansas. And so the experiment continues.
John Fund, in National Review Online, writes of:
“…an ever-expanding government that chokes off economic opportunities for the middle class and those who aspire to it.
Time for some data. Figure 1 shows Federal government current expenditures normalized by the size of the economy.
Figure 1: Federal government current expenditures as a share of GDP (red) and as a share of potential GDP (blue). NBER recession dates shaded gray. Source: BEA, 2014Q2 advance release, CBO (February 2014), NBER and author’s calculations.
Federal government expenditures are now at 22.7% of GDP, far below the 25% recorded in 1982Q4 during the Reagan administration.
One point that all can agree on — even if there is no agreement on how to deal with the issue — is the deficient level of public investment. The American Society of Civil Engineers (ASCE) provided an assessment in 2013; their current estimated funding requirement through 2020 is $3.6 trillion. Figure 2 presents real investment as a log ratio to output.
Figure 2: Log ratio Federal real government investment expenditures to real GDP (red) and to real potential GDP (blue). NBER recession dates shaded gray. Source: BEA, 2014Q2 advance release, CBO (February 2014), NBER and author’s calculations.
Note plotted are real quantities for real government investment (which includes intellectual property products, such as software). This graph can be read as follows: the log ratio of investment to GDP rose from -4.99 in 2007Q4 to -4.82 in 2011Q1, which means that real government investment grew a cumulative 17% faster than real GDP. On an annual basis, over this 3.25 year period, investment was growing 5.2% faster.
Since 2011Q1, the log ratio has plunged to -5.03, which is a cumulative decline of 21% over a 3.25 year period. On an annual basis over this period, this is a 6.5% rate of decline.
A lot of concern has focused on the decline in government investment in physical capital — bridges, roads, sewer systems, etc. That concern is focused not only on Federal investment but also state and local. I haven’t had time to generate the real investment ex-intellectual property products series, so I present in Figure 3 investment in structures and equipment as a share of nominal GDP.
Figure 3: Federal nondefense investment in structures and equipment (blue) and state and local investment in structures and equipment (red) as a share of nominal GDP. NBER defined recession dates shaded gray. Source: BEA 2014Q2 advance release, NBER and author’s calculations.
To me, the implications are clear. With borrowing costs extremely low, it makes sense to invest in infrastructure. Some disagree. Then, the question is, are you with Barry Eichengreen or Sarah Palin? By the way, despite talk of the taper, and incipient Fed tightening, borrowing costs for the government remain quite low; this is shown in Figure 4.
Figure 4: Ten year constant maturity Treasury yields (blue), ten year constant maturity yields minus ten year (median) expected inflation (red +), and ten year constant maturity TIPS (black). NBER defined recession dates shaded gray. Source: Federal Reserve via FRED, Survey of Professional Forecasters, and author’s calculations.
For a discussion of transportation investment needs and potential impacts, see this NEC/CEA report.
For previous installments in the series on the myths regarding the ever-expanding government, see , , , , .