The January Effect and the Jobs Report

Expect tomorrow’s jobs report for January to indicate that the economy “added” between 250,000 and 300,000 jobs.  Also expect jobs growth in the spring of 2013 to be disappointing.  These expectations have nothing to do with budget negotiations in Washington, the impact of austerity measures in the Eurozone, or corporate earnings reports.  The pattern of strong employment growth in January followed by disappointing payroll reports in the spring is the result of the BLS seasonal adjustment procedure.

One year ago the BLS reported that nonfarm payroll increased by 275,000 between December 2011 and January 2012 — the largest increase in employment since the recession (excluding May 2010 when hundreds of thousands of seasonal Census workers were hired).  In fact, nonfarm payroll decreased by 2.67 million employees, but that decline was much smaller than the BLS statistical model projected.

The BLS seasonal adjustment procedure is a moving average process, which means that seasonal factors change over time.  The BLS seasonal  factors in January 2011 and January 2012 were unusually large because they reflected a historic decline of 3.7 million jobs between December 2008 and January 2009.  The BLS X-12 ARIMA model for seasonal adjustment viewed this massive decline in employment as a signal that the “January effect” in payroll employment had become more pronounced.  The procedure requires that future January’s would have larger projected employment declines.  To be clear, a larger “January effect” is measured relative to other months of the year.  If the new seasonal factors add 100,000 more jobs in January compared to the previous factors, a comparable number of jobs will be subtracted from other months of the year.  Hence bigger “January effects” mean smaller payroll gains in other months, most notably the spring.

The data from last January suggest that it was a particularly deep recession, and not changing seasonal factors, that accounted for the steep employment decline four years ago.  The legacy of the 2008-2009 decline is likely to still have an impact on BLS seasonal factors, but not by as much as in 2011 and 2012.  Nonetheless, it would not be surprising to see surprisingly strong job growth of 275,000 in tomorrow’s report, and weaker than expected growth in April and May.

New Jobless Claims: 2013 Has Been a Good Year for Seasonal Adjustment

Headlines have highlighted the fact that  new jobless claims have fallen to 335,000 and 330,000, respectively, in the past two weeks.  News reports state that new unemployment insurance claims have dropped to their lowest levels in five years.  These statements are based on seasonally adjusted data.  The following table shows seasonally adjusted and unadjusted new jobless claims data in the first three weeks of 2012 and 2013.

	New Unemployment Insurance Claims
Date	Seasonally Unadjusted	Seasonally Adjusted
January 7, 2012	646,219	390,000
January 14, 2012	525,422	364,000
January 21, 2012	416,880	372,000
Avg First 3 weeks 2012	529,507	375,333
January 5, 2013	557,798	375,000
January 12, 2013	556,710	335,000
January 19, 2013	436,766	330,000
Avg First 3 weeks 2013	517,091	346,667

 The table indicates that while seasonally unadjusted new UI claims have been 2.3% lower in early 2013 than early 2012, seasonally adjusted new UI claims  dropped by 7.6% from one year ago.  The average seasonal adjustment factor in the first three weeks of 2013 reduced claims by 33% while the adjustment factor was 29% over the same three weeks in 2012.  Had the same adjustment factors been used in both 2012 and 2013, new UI claims would have averaged 366,533 per week in January 2013.

An average of 366,533 new UI claims per week is better than we have seen for much of the recovery, but it is certainly not the lowest level in the past five years.  As recently as late September and early October of 2012 the average number of seasonally adjusted new UI claims fell below 360,000 per week.  The last few weeks have seen unusually large seasonal adjustment factors making the difference between seasonally adjusted and unadjusted new UI claims larger than we have seen in recent memory.  The news on jobless claims is good, but the large seasonal adjustment may be overstating the improvement over the past few weeks.

Welch Consulting Employment Index Continued its Decline in December

The Welch Consulting Employment Index was 94.5 in December; down from 94.6 in November.  An index value of 94.5 means that full-time equivalent employment (from the BLS household survey) is 5.5% below its level in the base year of 1997, after adjusting for both population growth and changes in the age distribution of the labor force.  The index had recovered this fall from the sharp declines experienced in the summer of 2012, but has fallen for two straight months.  Moreover, the index is now below its level in March 2012.  Full-time employment has not quite kept pace with population growth over the past nine months.  The Welch Consulting Employment Index has grown by less than one percent per year over the past two years.

The Welch Consulting Employment Index, disaggregated by gender, is 92.4 for men and 97.1 for women.  Both the indices for men and women are down slightly from November.  Over the past nine months the men’s index is down 0.1% while the women’s employment index is down 0.8%.  Over the past two years the men’s employment index has grown by 1.4% per year and the women’s index has grown by 0.4% annually.

Technical Note: Full-time equivalent employment equals full-time employment plus one half of part-time employment from the BLS household survey.  The Welch index adjusts for the changing age distribution of the population by fixing the age distribution of adults to the 1997 base year.  The Welch Index adjusts for population growth by fixing total population to its 1997 level.  Seasonal effects are removed in a regression framework using monthly indicator variables.

What Happened to Defense in the NFL Playoffs?

The NFL has held Divisional Playoffs since 1970.  The playoffs match the top four teams in each Conference.  Until this year the average score in these games has been 28 to 15.  This year the average score was 38.5 to 30.5.  This means that, on average, teams combined to score 69 points per game in  compared to the historic average of 43 points.  In recent years the most successful NFL teams have had strong offenses but weaker defenses.  The past three years have seen three of the four highest scoring Divisional Playoff weekends in history.  In 2010-2011 the 8 teams playing in this round of the playoffs set the previous record by scoring a combined 232 points in four games.

This year the eight teams combined for 276 points.  No team scored fewer than 28 points (the Texans and the Seahawks scored exactly 28).  The San Francisco 49ers scored more points (45) than both teams combined typically score in a playoff game.  The scoring total this year is about 60% higher than the historic average and 19% higher than the previous record set in 2010-2011.

I am not longing for a return to 1970, when both the Cowboys and Colts shut out their opponents (the Bengals and Lions) and a total of 88 points were scored by all teams in the four playoff games.  This weekend’s games were no doubt more entertaining than the Cowboys 5-0 victory over the Lions in 1970.  The rules have changed since 1970 to favor offenses in general and the passing game in particular.  If the best teams in each conference are unable to hold their opponents to under 28 points, perhaps the pendulum has swung too far in favor of NFL offenses.

How Much Parity is there in the NFL? Or Why have the Detroit Lions been such a Bad Team for so Long?

The NFL Divisional playoff games are being played this weekend.  We are down to four teams out of 16 in each Conference.  This is the 43^rd year of NFL playoffs in which there were Divisional playoffs leading to a Conference Championship game.  For 41 straight seasons, the “final four” in each Conference differed by at least one team from the previous year.  This year, for the first time, the four teams representing the AFC (Broncos, Patriots, Ravens and Texans) are the same ones that advanced to the AFC Divisional playoffs last year.   Every NFL franchise has advanced to the Divisional playoffs at least once since 1970.  These facts make it appear that there is tremendous parity in the NFL.  Despite the changes in the top four teams in each conference from one year to the next, there is no doubt that some franchises are much more successful than others.

If all 25 of the NFL teams who played every NFL season from 1970-2012 were equally successful, they each would have appeared in the Divisional playoffs 11.9 times.*  The following tables list the six most successful teams in each conference, as measured by appearances in the Divisional playoffs.  The Steelers are the most successful franchise with 22 appearances, closely followed by the 49ers and Cowboys with 21 appearances.  The least successful NFC franchise is the Detroit Lions, who advanced to the Divisional playoffs only three times.  The least successful franchises in the AFC are the Kansas City Chiefs and Cincinnati Bengals, who each advanced to the Divisional playoffs only six times each.

Team	Number of Appearances in AFC Divisional Playoffs	% of Seasons that Team Appeared in AFC Divisional Playoffs
Pittsburgh Steelers	22	51.2%
Baltimore Ravens	8	47.1%
Miami Dolphins	17	39.5%
Oakland Raiders	16	37.2%
New England Patriots	15	34.9%
Indianapolis Colts	14	32.6%

Team	Number of Appearances in NFC Divisional Playoffs	% of Seasons that Team Appeared in NFC Divisional Playoffs
Dallas Cowboys	21	51.2%
San Francisco 49ers	21	47.1%
Seattle Seahawks	5	41.7%
Minnesota Vikings	19	39.5%
Washington Redskins	15	34.9%
St. Louis Rams	14	32.6%

The Cowboys and 49ers have advanced to the final four seven times as often as the Lions.  The Steelers have advanced to the Divisional playoffs about 3.7 times more often than the Bengals or Chiefs.  If all franchises were equally matched (on average), each would have advanced to the Divisional playoffs from 10 to 13 times over 43 years.  The vast difference in the success rates of the Steelers, Cowboys and 49ers on one hand, and the Lions, Chiefs and Bengals on the other hand, is far too large to be explained by random chance, or the difference between good luck and bad luck.

Another way to measure NFL parity is from one season to the next.  The following table indicates how often final four teams in each conference repeated from the previous season and compares this distribution to what would be expected if there was complete parity in the NFL.   

Number of final four NFL teams repeating from last season	Number of times this many final four teams repeated from last season	Fraction of times this many final four teams repeated from last season	Fraction of times this many final four teams would repeat with complete NFL parity
0	5	5.8%	29.0%
1	26	30.2%	44.6%
2	34	39.5%	23.2%
3	20	23.3%	2.8%
4	1	1.2%	0.4%

Typically two teams in each conference repeat in the Divisional playoffs from last year but with complete parity it would be much more likely that only one team would repeat in the Divisional playoffs from the previous year.  Thus, even though all four teams have returned to the Divisional playoffs in consecutive seasons only one out of 84 possible times (42 years each for the AFC and NFC) the NFL is far from complete parity.

In my view, the biggest deviation from parity in the NFL is that some franchises, such as the Detroit Lions, have been much less successful than top franchises over more than four decades.  If teams were equally matched, on average, I would expect the top franchises to have about 30% more Divisional playoff appearances than the franchises with the worst records over 40 decades.  The NFL shares most revenue equally, and gives advantages to the teams with the weakest record in each year (better selections in the player draft and play weaker opponents in the following season).  Thus, as opposed to other professional sports, long-run differences in the average success of NFL franchises is due primarily to talent differences among  owners, coaches and general managers rather than differences in payroll across teams.

*Note: Complete parity in the NFL occurs when all 16 teams in each conference are evenly matched and there is no correlation in a team’s performance from one year to the next.   Although there were 26 teams in the NFL in 1970, the Cleveland Browns did not play for three seasons in the late 1990’s after their team moved to Baltimore and became the Ravens.

Role Reversal: Clippers and Lakers

This is the 43rd NBA Season for the Los Angeles Clippers.  Over the first 42 years of their existence (in Los Angeles, San Diego and Buffalo) they won 36.7% of their games.  They had a winning record in only 7 of their first 42 seasons and have never won more than 50 games in a single season.  Their typical season has been 30 wins and 52 losses.  This year’s team has the best record in the NBA and has won 28 of their first 36 games.  The Clippers of old, with a 36.7% winning percentage, would be expected to start the season this well once in every 1.65 million seasons.

The Los Angeles Lakers have been playing in the NBA for 65 seasons (including Minneapolis).  They won 62% of their games in their first 64 seasons and had a losing record in only 12 of those seasons.  The typical Laker team has a record of 51 wins and 31 losses.  In fact, the Lakers have won at least 50 games in 32 different seasons.  This year’s team has won only 15 of their first 35 games and is in danger of missing the playoffs.  The Lakers of old, with a 62% winning percentage, would be expected to get off to that poor of start once in every 60 seasons.

The New 39.6% Tax Bracket Will Directly Impact 9.5% of Aggregate Personal Income

I estimate that the new top marginal income tax rate of 39.6% on annual income in excess of $400,000 per year will have a direct impact on about 7/10 of one percent of taxpayers.  However, the income earned by these taxpayers in excess of the $400,000 threshold accounts for about 9.5% of aggregate personal income.  Had President Obama prevailed in the fiscal cliff negotiations, and the marginal income tax rate increased for incomes in excess of $200,000, higher marginal rates would have directly affected about 2.6% of taxpayers.  The income earned by taxpayers in excess of the $200,000 threshold accounts for about 14.7% of aggregate personal income.

Changes in marginal tax rates have incentive effects as well as indirect effects on workers and consumers not subject to the higher marginal rates.  The economic debate over raising marginal tax rates on high income earners centers on the magnitude of the disincentives for work, saving, investment, and job creation caused by higher marginal tax rates.  As high income earners change their behavior in response to the reduced incentives to earn income, all consumers and workers will be impacted by the new top marginal rate.

Unfortunately there have been few empirical studies that have focused on income earners directly impacted by the 39.6% marginal rate.  This is because less than one in one hundred earners generate enough income to pay the new top marginal rate.  Second, little information about these earners is known because most publicly available databases protect the confidentiality of survey respondents by “top coding” personal income.  When the income variable in a database is “top coded” it is impossible to tell whether an individual’s annual income is $500,000 or $500 million.  Finally, while the income generating activities of middle-income earners is typically summarized by annual hours of work, the productive activities of high income earners are less likely to be captured by surveys that merely report hours and weeks worked per year.  High income earners engage in risk-taking and entrepreneurial activities that may be more or less responsive than hours worked to changes in marginal tax rates.  In other words, although there are many economics studies measuring how the hours worked by low and middle-income earners respond to changes in marginal tax rates, these studies tell us little about how income earners will change their investment and risk-taking behavior now that the top income tax rate is 39.6% not 35%.  Whatever the incentive effects of this new tax rate will be, they will be felt by all Americans not just taxpayers earning in excess of $400,000 per year.

Note: My calculations are based on the summary statistics presented by economists Emmanuel Saez and Thomas Piketty, using the 2010 Statistics of Income data from the IRS.  These data are not top coded and provide the best information about high income earners.  Unfortunately tax returns data have no information about how many individuals earned income in the taxpaying household, how many hours were worked, or how much risk-taking and entrepreneurial activities were taken by the household in the tax year.  Saez and Piketty reported that in 2010, excluding capital gains from income, the top:

• ½ of one percent of tax returns reported annual income of $488,000 or more and accounted for 13.4% of all personal income.
• 1% of tax returns reported annual income of $336,000 or more and accounted for 17.4% of all personal income.
• 5% of tax returns reported annual income of $148,000 or more and accounted for 33.7% of all personal income.

Using these summary data, and the conditional means of income reported by Saez and Piketty, I fit a simple model for the top percentiles of the empirical distribution of personal income in order to estimate the fraction of aggregate personal income above the $200,000 and $400,000 thresholds reported above.