Deficit Hypocrisy Watch
The WSJ editorial page last Thursday remarked upon:
“…the worst fiscal record of any President in modern times…”
Indeed, there has been a point in time when the Federal budget deficit, adjusted for how tax revenues and transfers responds to the state of the economy (what students in my undergraduate course are learning are called “automatic stabilizers”), hit a then record 4.4% as a share of potential GDP! That was in 1986Q3, and the president, as I recall, was Ronald Reagan.
Figure 1: Federal budget balance as a share of GDP (blue), cyclically adjusted Federal budget balance as a share of GDP (red), and cyclically adjusted Federal budget balance as a share of potential GDP (green). NBER defined recession dates shaded gray. Source: CBO, Budget and Economic Outlook, January 2012 automatic stabilizer spreadsheet, and BEA, 2011Q4 advance release, NBER, and author’s calculations.
The WSJ conveniently plotted only the blue line; for a summary of fiscal stance, the red or green lines are better. So, while the corresponding figure in 2011Q3 at 5% is larger in absolute value, it’s only so by about half a percentage point. However, I don’t recall corresponding cries from the Wall Street Journal in 1986. (Oh, and the output gap in 1986 was -0.5 ppts; it was -5.1 ppts in 2011, which I think could account for the additional stimulus. And while I’m at it, note the -2.7 ppt cyclically adjusted budget balance when the output gap was a positive 0.4 ppts (2005).)
Conspiracy at the BLS?
… with the benchmark revision & 3 different adjustments, i cannot have confidence in anything in this report…absent the seasonal adjustment, the actual number for january non-farm payrolls was a loss of 2,689,000 jobs; knowing that the BLS confidence interval is on the order of plus or minus 100,000; seasonally adjusting that job loss to show 243,000 jobs gained leaves plenty of room for an error in the methodology…
Since several (usually conservative) commentators (e.g., ) have raised questions about the BLS methodologies, I thought it of interest to look at how implausible the estimates are – with a focus on the seasonal adjustment procedure (which seems to be black magic to many). First, here’s graph of the seasonally adjusted and the seasonally unadjusted nonfarm payroll employment series (both easily available to the intellectually curious at the St. Louis Fed’s FRED database, category 11).
Figure 2: Reported nonfarm payroll employment, seasonally adjusted (black), and not seasonally adjusted (blue), in 000’s, 2007M01-2012M01. NBER defined recession dates shaded gray. Source: BLS January release via FRED, series PAYEMS and PAYNSA, respectively.
So, to the uninitiated it must surely be confusing that the not seasonally adjusted (nsa) series is declining while the seasonally adjusted (sa) series is rising. rjs argues that there is too much going on in the January figures to make anything of the rise. I thought it useful to at least examine the seasonal adjustment process. From BLS:
For the establishment survey, annual benchmarks are constructed to realign the sample-based employment totals for March of each year with the UI-based population counts for March. These population counts are less timely than sample-based estimates and are used to provide an annual point-in-time census for employment. For National series, only the March sample-based estimates are replaced with UI counts. For State and metropolitan area series, all available months of UI data are used to replace sample-based estimates. State and area series are based on smaller samples and are, therefore, more vulnerable to both sampling and non-sampling errors than National estimates.
Population counts are derived from the administrative file of employees covered by UI. All employers covered by UI laws are required to report employment and wage information to the appropriate State workforce agency four times a year. Approximately 97 percent of total nonfarm employment within the scope of the establishment survey is covered by UI. A benchmark for the remaining 3 percent is constructed from alternate sources, primarily records from the Railroad Retirement Board and County Business Patterns. The full benchmark developed for March replaces the March sample-based estimate for each basic cell. The monthly sample-based estimates for the year preceding and the year following the benchmark are also then subject to revision.
Monthly estimates for the year preceding the March benchmark are readjusted using a “wedge back” procedure. The difference between the final benchmark level and the previously published March sample estimate is calculated and spread back across the previous 11 months.
The wedge is linear; eleven-twelfths of the March difference is added to the February estimate, ten-twelfths to the January estimate, and so on, back to the previous April estimate, which receives one-twelfth of the March difference. This assumes that the total estimation error since the last benchmark accumulated at a steady rate throughout the current benchmark year.
Estimates for the 7 months following the March benchmark also are recalculated each year. These post-benchmark estimates reflect the application of sample-based monthly changes to new benchmark levels for March and the computation of new business birth/death factors for each month.
Following the revision of basic employment estimates, estimates for women employees and production and nonsupervisory employees are recomputed using the revised all-employee estimates and the previously computed sample ratios of these workers to all employees. All basic series of employment, hours, and earnings are re-aggregated to obtain estimates for each sector and higher level of detail. Other derivative series (such as real earnings and payroll indexes) also are recalculated. New seasonal adjustment factors are calculated and all data series for the previous 5 years are re-seasonally adjusted before full publication of all revised data in February of each year. [emphasis added – mdc]
In general, one should consult the documentation if one has questions about the NFP series.
Once one has a handle on how the series are created, one can evaluate how sensitive the estimates are to differing approaches. Now, the BLS documentation makes clear the seasonal adjustment procedure is applied to components of aggregate employment, and I don’t have ready access to the n.s.a. versions of the components. But we can still apply the seasonal adjustment process to the aggregate n.s.a. series. Below, I report results when I apply the procedure to a series including and excluding temporary Census workers.
Built into many time-series statistical packages is the standard seasonal adjustment procedure, Census X-12 ARIMA.
Figure 3: Log nonfarm payroll employment, seasonally adjusted by BLS (black), seasonally adjusted using X-12 ARIMA applied to PAYNSA over 2006-2012 (dark red), applied to PAYNSA ex.-temporary Census workers (green). NBER defined recession dates shaded gray. Source: BLS January release via FRED, series PAYEMS and PAYNSA, respectively, NBER; X-12 ARIMA executed in EViews 7.
To see if these results were driven by the Census procedure, I used a generic seasonal adjustment procedure which estimates seasonal factors as deviations from a moving average (the seasonal factors are assumed to be additive, given I am working on log series). I also estimate the seasonal on differing samples: 1967-2012M01, 1987-2012M01, 2007-2012M01, and the latter, using a n.s.a. series excluding temporary Census workers.
Figure 4: Log nonfarm payroll employment, seasonally adjusted by BLS (black), seasonally adjusted using difference from moving average over 1967-2012 period (dark red), over 1987-2012 period (green), over 2007-2012 period (purple), and over 2007-2012 period on series excluding temporary Census workers (orange), in 000’s, 2007M01-2012M01. NBER defined recession dates shaded gray. Source: BLS January release via FRED, series PAYEMS and PAYNSA, respectively. Additive seasonal executed in EViews 7.
As is obvious from Figures 3 and 4, I do not obtain drastically different results when I apply X-12 ARIMA to the aggregate n.s.a. series, or apply a generic adjustment procedure to a sample starting in 2007. In terms of changes in 2012M01, I find the BLS estimate of 243 thousand to be bracketed by 203 thousand (X-12 applied to PAYNSA) and 282.3 thousand (additive seasonal applied to ex.-Census PAYNSA).
Figure 5: First difference of log nonfarm payroll employment, seasonally adjusted by BLS (black), seasonally adjusted using X-12 ARIMA applied to PAYNSA over 2006-2012 (dark red), applied to PAYNSA ex.-temporary Census workers (green), seasonally adjusted using additive seasonal factors estimated over 2007-2012M01 period on PAYNSA (purple), on PAYNSA ex.-temporary Census workers. NBER defined recession dates shaded gray. Source: BLS January release via FRED, series PAYEMS and PAYNSA, respectively, NBER; X-12 ARIMA and seasonal adjustment executed in EViews 7.
Finally, as I illustrated in Figure 1 of this post, the household series, adjusted to conform to the nonfarm payroll employment concept, which conservative commentators had urged the BLS to construct, evidences a similar upward trend (and has recorded figures consistently higher than the official establishment series. This is something no one else has remarked upon, to my knowledge.
So, there might be some conspiracy in the bowels of the BLS, busily and deliberately churning out misleading employment data, as some conservative commentators suggest. But for me, a little investigation leads me to doubt that thesis.
Wisconsin Coincident and Leading Indicators Pointing Down (Still)
As I noted in a previous post, Wisconsin stood out as one of the few states with negative economic activity trends, starting with implementation of the Governor’s new budget in June.
The Philadelphia Fed’s leading indices indicate that there is little prospect that such trends will reverse. Given the Contractionary policies implemented in the budget , this is no surprise.
Figure 6: Leading indices for Wisconsin (blue), and for US (black). Dashed line at 2011M01. Source: Federal Reserve Bank of Philadelphia, February 2 release.
To see how remarkable the outlook for Wisconsin is, see the Philadelphia Fed’s map of trends.
Figure 7: Map of leading indices for December. Indices are forecast for growth rate of coincident indices over next six months. Source: Federal Reserve Bank of Philadelphia, February 2 release.
Figure 8: Three month trend in coincident indices. Source: Federal Reserve Bank of Philadelphia, “State Coincident Indexes: December 2011″ (January 26, 2012).
Update, 3:45PM Pacific: From WisPolitics Budget Blog this morning:
The Legislative Fiscal Bureau said today the state is now facing a gross deficit of $143.2 million for the 2011-13 biennium, a difference of almost $216 million from past projections.
The LFB said a number of factors combined to worsen the state’s fiscal outlook, including a $272.8 million drop in projected tax revenues through June 30, 2013. The agency noted the projected shortfall exceeds the trigger requiring the guv to submit a budget repair bill and says the administration is discussing steps to address the deficit, including debt refinancing and restructuring.
Gov. Scott Walker said in a statement when compared to other states “Wisconsin is headed in the right direction” and that his administration is “confident in our ability to manage the Wisconsin taxpayer’s money well.” The statement did not offer any steps the administration may take to address the shortfall. Rather, it focused on the LFB projection that the state would end the current fiscal year with a positive gross balance and compared Wisconsin to other states and referenced actions by Walker’s predecessor without mentioning Jim Doyle by name.
“We’ll keep our budget balanced without the job-killing tax increases implemented in the years before we took office; tax increases that led to over 150,000 Wisconsinites losing their jobs in the years before we took office,” Walker said. “In contrast, we’ve added thousands of jobs this year, have the lowest unemployment rate since 2008, lowered the tax burden and will end the year with a surplus.”
Update, Friday 2/10, 11:30PM Pacific:: Readers rjs and tj insist that the January 2012 seasonally adjusted nonfarm payroll number is distorted by the exceptional snowfall in winter 2009-10 combined exceptionally mild winter 2011-12. To see how much this affects the annual growth rates, I compare the log-differenced s.a. and n.s.a. series.
Figure 9: Annual growth rates in seasonally adjusted nonfarm payroll employment (blue), and in not seasonally adjusted nonfarm payroll employment (red), both calculated as 12 month log differences. NBER defined recession dates shaded gray. Source: Source: BLS January release via FRED, series PAYEMS and PAYNSA, respectively, NBER, and author’s calculations.
If you can see a difference for the2012M01 observation, please tell me. (The 12 month growth rate for PAYNSA is 1.5% compared to 1.49% for PAYEMS.)
Update, 2/12, 3PM Pacific:
Reader Jeff criticizes me for concluding that just because the state of Wisconsin is cutting state expenditures (including state and local government employment) that output should fall; apparently, in his mind, it’s just a coincidence that government employment and activity should both start declining with the beginning of the new budget year. To examine this issue a little more closely than in Jeff’s critique, assume Wisconsin economic activity is driven by US economic activity (as assumed in the state government’s Wisconsin Economic Outlook), and state government expenditures. Further assume the latter can be proxied by state government employment:
yWI = f(yUS, gWI)
Run a regression of log Wisconsin coincident index on log US coincident index and log Wisconsin government employment ex.-Federal workers, on a quarterly basis, 2008Q1-2011Q4. Allow an AR(1) correction so that the residuals are not serially correlated at the 10% msl, using Q-statistic (12 lags).
yWI = -7.01 + 0.98 yUS + 0.54 gWI + 0.0000 time
AR(1) = 0.64. Adj. R2 = 0.98. n = 16, 2008Q1-11Q4. SER= 0.002.
The actual, and fitted values using the estimated relationship, and assuming Wisconsin government exployment ex.-Federal held constant at 2011M07 levels for 2011Q3-11Q4 is shown in Figure 10. The implied reduction in economic activity as proxied by the coincident indicator is 1.8%. Since the elasticity of employment with respect to the coincident indicator is 0.74 over this period, the implied reduction in jobs due to the lower state and local employment is 36,800.
Figure 10: Actual log Wisconsin coincident indices, fitted values and fitted using counterfactual state and local government fixed at 2011M07 levels.