Lessons from Elkhart, Indiana

In the 2008 presidential campaign then-candidate Barack Obama visited Elkhart, Indiana twice.  He visited again as president in February 2009 to make the case for his $800 billion stimulus package.  President Obama came to Elkhart three times because the city was in the midst of an economic freefall and seemed to epitomize the plight of the U.S. economy in 2008-2009.  Although Elkhart’s unemployment rate was 4.6% in 2007, by March of 2009 it would reach 20.2%.  Elkhart has bounced back from the recession much better than other cities.  Its unemployment rate is now 8.3%, slightly above the national average but dramatically lower than it was just three and a half years ago.

Indiana is the most manufacturing-intensive state in the U.S. with one of six private sector workers employed in manufacturing.  In addition, over 7 out of 10 manufacturing jobs in Indiana are in the durable goods sector.  Arguably, no city in the U.S. is more dependent on the production of durable goods than Elkhart, which has been called the recreational vehicle capital of the world.  While only 6.7% of private sector jobs in the U.S. are in durable goods manufacturing in Elkhart 43.6% of private sector workers are employed in the manufacture of durable goods, with the majority of those jobs in the production of recreational vehicles and motor vehicle parts.  Elkhart has been one of the top job creating cities in the U.S. since the recession ended.  In the past three years jobs in durable goods manufacturing have increased by 42.7% in Elkhart compared to 5.6% growth in the U.S. overall.

As a durable goods manufacturing center Elkhart’s downturn and recovery looks much more like the typical pattern for a deep recession followed by a brisk recovery.  Other cities hurt most by the recession (such as Riverside, CA and Las Vegas NV) suffered collapses in their residential real estate markets and residential investment.  These cities have not yet experienced the rebound seen in Elkhart.  The following chart indicates how the unemployment rate in Elkhart spiked much above the rates in Las Vegas and Riverside but has fallen more quickly since then.

There has been much discussion about why this recovery has been so slow and so weak.  Reinhart and Rogoff relied on cross-country and historical comparisons to argue that a slow and weak recovery is to be expected after a systemic banking and financial crisis.  It is difficult, however, to make consistent cross-country comparisons of unemployment rate fluctuations because of differences in the way each country measures unemployment.  Bordo and Haubrich limit their analysis to American historical data.  They find that “general recessions associated with financial crises are generally followed by rapid recoveries.”  They also conclude that one reason for the slowness of the recovery “is the moribund nature of residential investment.”

Another way to understand the 2008-2009 downturn and the subsequent recovery is by comparing unemployment rate fluctuations across U.S. cities.  Such a comparison shows that the unemployment rate has remained stubbornly high in cities where the housing bubble burst.  Durable goods manufacturing centers like Elkhart saw a big drop in the demand for the products they produce and a large increase in unemployment during the recession.  But manufacturing-intensive cities have recovered somewhat more rapidly – more typical of previous downturns.  That gives the edge, for the time being, to the explanations provided by Bordo and Haubrich.

More Thoughts on the Illusion of Declining Birth Rates

My earlier post on birth rates generated some interest and discussion and therefore deserves a bit more attention and clarification.  First, the average birth rate referenced in news headlines is simply the number of births divided by the number of women age 15-44.  This average birth rate conflates age-specific changes in birth rates with changes in the age distribution of the population.  The average birth rate is lower in 2011 in part because there are so many women age 15-19 and age 40-44, when birth rates are low.

Demographers and labor economists use the total fertility rate to study changes in birth rates over time.  The total fertility rate gives equal weight to women of each age from 15 to 44 and therefore is invariant to changes in the age distribution of the population.  My second point is that the total fertility rate in 2011 is higher than it was from 1975 to 1985 and therefore is not the “lowest ever.”  Finally, the total fertility rate understates the expected number of children born to women from a given birth cohort when women are choosing to delay childbirth.  Delayed childbirth will show up as an initial decline in the total fertility rate but not the number of children per woman.

By following a given birth cohort of women from the ages of 15 to 44 I calculate that:

• Women born between 1956 and 1960 had an average of 2.03 children
• Women born between 1961 and 1965 had an average of 2.06 children
• Women born between 1966 and 1970 had an average of 2.12 children
• Women born between 1971 and 1975 had an average of 2.11 children

The youngest of the women born between 1971 and 1975 are age 37 and are likely to have more children.  Thus 2.11 is an underestimate of the average number of children born per woman for the 1971-1975 birth cohort.  There is no evidence of declining birth rates among women who are at or near the end of their childbearing years.  For women born more recently (1976-1980 or 1981-1985) there are still many more years of possible childbearing.

More recent cohorts of women are having fewer and fewer children before the age of 25.  They are also likely to have more children in their 30’s than their older sisters, mothers and grandmothers.  It is far too early to predict whether lower birth rates for women under the age of 25 in 2011 will translate to lower birth rates over their lifetimes.  What does seem clear is that we are transitioning to a society where the 30’s become the prime childbearing ages for most women.  It is already true that there are more births (per capita) to women age 30-34 than age 20-24.

I do not believe that the delay in family formation we have seen thus far is due to decadence.  Couples are likely delaying children because of the weak economy and the high costs of raising children. Columnists and commentators should write and talk about the dramatic changes that will occur in our society when the majority of first-time parents are in their thirties.  I am skeptical, however, that one of the changes will be a decline in the U.S. population.

Tyler Cowen and Ross Douthat Have Confused Delayed Childbearing with Declining Fertility

Ross Douthat’s recent New York Times column is based on the premise that “American fertility plunged with the stock market in 2008, and it hasn’t recovered.”  Douthat cited a Pew Research Center report that “the U.S. birth rate dipped in 2011 to the lowest ever recorded” because it fell to 63.2 per 1,000 women age 15 to 44 compared to 71 per 1,000 women in 1990.

Unfortunately, because of changes in the age distribution the average birth rate across all age groups from 15 to 44 is a poor way to compare birth rates over time; fertility rates vary among women of different ages.  For example, women age 15-19 and 40-44 have the lowest fertility rates.  In 2011 there are relatively more women in these age groups than in earlier years: 13% more than in 1990.  Changes in the age distribution distort comparisons of average birth rates between 2011 and earlier years. 

Demographers tend to use the “total fertility rate” to describe trends in birth rates over time.  The total fertility rate sums fertility rates across all age groups and therefore puts an equal weight on all ages from 15 to 44, regardless of changes in the age distribution.  The total fertility rate in 2011 is not the lowest rate ever recorded and is, in fact, higher than it was for the entire decade from 1975 to 1985.  This is why Carl Haub of the Population Reference Bureau wrote that reports claiming that the average birth rate in 2011 was the lowest ever are misleading.

Some demographers use the total fertility rate as a proxy for the expected number of children born to women over their lifetimes.  This approximation will only be accurate if women age 15-19 in 2011 expect to have the same fertility rate 15 to 20 years from now as women in their thirties have in 2011.  The approximation will be inaccurate if women in 2011 are delaying childbirth longer than women from earlier birth cohorts.

In recent years the biggest declines in fertility occurred for women under the age of 25.  In the past 20 years births to women age 15-19 are down 49% and births to women age 20-24 are down 26%.  At the same time fertility rates for women in their thirties and forties are higher than ever.  Since 2003 the fertility rate for women age 35-39 has been higher than for women age 15-19 and the gap is widening each year.  Similarly, beginning in 2009 the fertility rate for women age 30-34 increased above the rate for women age 20-24.  There is every reason to expect this gap to increase as women outnumber men in colleges and universities and account for 47% of the labor force.

Although it is likely that couples have delayed childbearing because of the economic downturn, there is no reason to assume that this will lead to a permanent decline in the number of children ever born to women over their lifetimes.  The total fertility rate has consistently underestimated the number of children ever born at times when young women are delaying childbearing longer than women from earlier birth cohorts.

The following charts illustrate actual birth rates for women born from the 1956-1960, 1961-1965 and 1966-1970 birth cohorts and the projected birth rates implicit in total fertility rate calculations made in 1975, 1980 and 1985 respectively (when women from these birth cohorts were 15-19).  For each cohort actual birth rates exceeded the projected rates based on older women in the corresponding cross-sections because younger women were delaying childbirth longer than women from earlier cohorts.  Birth rate projections from cross-section extrapolations are 10.9% to 13.3% lower than the actual number of children ever born to women over their lifetimes. 

Although women age 15-19 and 20-24 today may have lower fertility rates than their older sisters and mothers had at the same age, they are also likely to give birth to more children in their thirties and forties than their older sisters and mothers did.

Nonetheless, Tyler Cowen writes that he basically agrees with Douthat’s thesis that:

“The retreat from child rearing is, at some level, a symptom of late modern exhaustion – a decadence that first arose in the West but now haunts rich societies around the globe.  It’s a spirit that privileges the present over the future, chooses stagnation over innovation, prefers what already exists over what might be.”

An alternative view is that couples delay child rearing because they recognize that education and human capital investments are expensive and the economic downturn and weak recovery forestalled the important decision to start a family.  I believe the decision of a couple to wait until they can better afford the education, housing and health care costs for their children is a noble sacrifice rather than an expression of decadence or a choice of stagnation over what might be.

The NBA Players’ Association Should Support Greg Popovich’s Decision to Rest Players

David Stern, commissioner of the NBA, fined the San Antonio Spurs $250,000 for resting four starting players, including stars Tim Duncan, Manu Ginobli and Tony Parker, in their game against the Heat in Miami on Thursday November 29.  The Spurs barely lost to the defending champion Heat in the closing minutes despite the fact that they played without four of their top players.  Coach Greg Popovich’s strategy may have worked, however, as the Spurs defeated the Memphis Grizzlies in overtime on Saturday night.  Memphis had the league’s best record entering the game.  Duncan, Ginobli, and Parker require more rest than many NBA stars because their ages are 36, 35 and 30, respectively.

Coach Popovich rested his top players because the Spurs were playing their third game in four nights and were in the midst of playing six games in nine days.  Teams playing three games in four nights tend to be fatigued and win fewer of the back-to-back games at the end of these sequences of games.  Most of the games played at the end of these sequences tend to be on the road, where the fatigued team is also the visiting team.  I analyzed NBA schedule data from the 2010-2011 season because the 2011-2012 season was strike-shortened and had a compressed schedule.  My analysis focused on away teams because of the small sample of home teams playing with such little rest in between games.  I found that NBA teams are significantly less likely to win a game when they are fatigued.

• Visiting teams won 41% of NBA games when playing with at least one night’s rest (and not in the midst of three games in four nights).

• Visiting teams won less than 29% of NBA games when playing back-to-back games at the end of a sequence of three games in four nights.

Playing games without much rest puts NBA teams at a disadvantage.  Rather than fining Greg Popovich for optimizing given the schedule the NBA has dictated to the Spurs, perhaps David Stern should be more concerned about scheduling equity.  Seven of 30 NBA teams (Cleveland, Golden State, Los Angeles Clippers, Milwaukee, Minnesota, New York and Washington) have to play three games in four nights on four different occasions this season.  Six teams (Boston, Dallas, Los Angeles Lakers, Memphis, Orlando and Utah) are scheduled to play three games in four nights at most once during the season.  The Spurs are scheduled to play three games in four nights on three occasions and therefore have one of the NBA’s least advantageous schedules in terms of opportunities for resting players.

It is not clear whether the Spurs will appeal Commissioner Stern’s fine.  If the Spurs appeal, the NBA Players’ Association should support their challenge.  First, resting players makes sense because it maximizes a team’s chance of success in a league where fewer than half of the teams are eliminated from the playoffs over the course of an 82 game schedule.  Second, resting players is likely to reduce the risk of serious injury.  As representatives of the players’ welfare, the NBA Players’ Association should be challenging the Commissioner’s decision.

70,000 Workers Displaced by Hurricane Sandy in New Jersey: Unemployment Rate May Reach 11%

Hurricane Sandy had a devastating effect on employment in New Jersey and a fairly large impact on employment in New York, as well.  A leading indicator of unemployment is the weekly report of new unemployment insurance claims.  A spike in new jobless claims means that a large number of workers were displaced from their jobs.  New Jobless claims have quadrupled in New Jersey and doubled in New York in the aftermath of Sandy relative to November 2011.  Using these data I estimate that Hurricane Sandy displaced 150,000 workers in the first two weeks after the storm hit, with 70,000 jobs lost in New Jersey and 50,000 lost in New York.  These job losses could push the November unemployment rate above 11% in New Jersey and above 9% in New York.

During the weeks of November 10^th and November 17^th (the most recent weeks for which detailed state data are available) about 96,000 jobless workers in New York filed for first-time unemployment insurance benefits, compared to about 48,000 new claims one year ago.  Similarly, almost 92,000 jobless workers in New Jersey filed for first-time unemployment insurance benefits during the weeks of November 10^th and November 17^th, compared to just over 24,000 new claims one year ago.  In the weeks after Sandy the rate at which workers lost jobs is about four times higher in New Jersey and twice as high in New York compared to November 2011.

The following charts compare the year-to-year change in new unemployment insurance claims the weeks of November 10^th and November 17^th and the corresponding change in claims for the previous 16 weeks (on average).  The charts indicate that new jobless claims remain very high in New Jersey while they have dropped recently in New York but remain above 2011 levels.  In both states new jobless claims in 2012 were consistently below 2011 levels until Hurricane Sandy hit.

Hurricane Sandy caused about 70,000 people to lose their jobs and file for first-time unemployment insurance benefits in New Jersey and 50,000 in New York during the weeks of November 10^th and 17^th.  These job losses are measured relative to the declines that would have been expected had the storm not hit the New Jersey coast.  Using similar methods, I estimate that about 30,000 additional jobs were lost in the rest of the country, possibly due to Hurricane Sandy.

The November jobs report (released on December 7^th) is the first one after the presidential election and the first to include data gathered after Hurricane Sandy.  The storm’s displacement of 150,000 workers in the past two weeks is enough to increase the U.S. unemployment rate from 7.9% to 8.0%.  Hurricane Sandy is also likely to increase the unemployment rate to 11% in New Jersey (from its current 9.7%) and above 9% in New York (from its current 8.7%).  The November unemployment rate is based on worker’s labor force status for the week ending November 17^th.  This means that continued job losses and displacement of workers in the second half of November, especially in New Jersey, will not factor into the November unemployment rate but could possibly cause the unemployment rate to increase further in December.

The Real Jackpot Payoffs for Powerball on November 28th

Powerball is advertising that the expected jackpot for tonight’s drawing is $500 million and that there is a 1 in 175 million chance of matching all six numbers and winning a share of the jackpot.  The ads are misleading because $500 million equals the undiscounted value of payouts received over a 30 year period, not the cash value, and assumes that the prize will not be shared.  A more accurate description of the jackpot prize distribution is:

• A one in 476 million chance of winning $163.7 million (the jackpot is shared by two winners)
• A one in 479 million chance of winning $327.4 million (a single jackpot winner)
• A one in 947 million chance of winning $109.1 million (the jackpot is shared by three winners)
• A one in 2.82 trillion chance of winning  $81.9 million (the jackpot is shared by four winners)
• A one in 11.23 trillion chance of sharing the jackpot among at least five winners

If the advertised jackpot increases to $600 million, a more accurate description of the jackpot prize distribution would be:

• A one in 539 million chance of winning $196.4 million (the jackpot is shared by two winners)
• A one in 682 million chance of winning $131.0 million (the jackpot is shared by three winners)
• A one in 852 million chance of winning $392.9 million (a single jackpot winner)
• A one in 1.29 trillion chance of winning  $98.2 million (the jackpot is shared by four winners)
• A one in 3.27 trillion chance of sharing the jackpot among at least five winners

The most likely outcomes are that there will be a single winning ticket or the jackpot will be shared by two winners.  As the advertised jackpot nears $600 million, it becomes much more likely that there will be at least a three-way split of the jackpot.  Either way, the best strategy for lottery players is to choose a combination of six numbers that the other 200+ million lottery players won’t select.

Is a Powerball Ticket a Good Bet?

The advertised Powerball jackpot has now been revised up to $500 million and the game rules indicate that the chance of winning the jackpot is 1 in 175 million for each $2 ticket that is purchased.  To the naïve Powerball customer this may seem like the expected payout from a $2 ticket is well above $2.  That reasoning is incorrect because the $500 million payout over 30 years has a cash value of only $327 million and there is a 63% chance that a winning ticket will be shared with one or more other players.  If Powerball lottery officials are correct and the jackpot is $500 million on Wednesday night, the expected payout on a $2 ticket will be $1.53.  If lottery officials have underestimated Wednesday’s jackpot the expected value of a $2 ticket will be somewhat lower.  For example, if the jackpot is $550 million (paid out over 30 years) the expected value of a ticket declines to $1.50.

Lottery officials expect about 227 million $2 tickets to be sold.  32.5% of the proceeds from these ticket sales are added to the cash value of the jackpot with another 17.5% funding consolation prizes.  This suggests there should be a 50% return on a $2 ticket, or a $1 expected value based only on current sales.  The expected return on a $2 ticket is substantially higher than this, about $1.53, because of the players in the 15 previous drawings where no winning ticket was selected.  These prior Powerball players have subsidized players in this week’s drawing.

Although 227 million tickets are expected to be purchased between Saturday night’s drawing and Wednesday’s drawing, with the odds of a winning ticket being 1 in 175 million, there is a  37% chance that there will be no winning ticket on Wednesday night.  If even more than 227 million tickets are sold, the jackpot increases but so does the chance of sharing the prize with other winners.  The following table illustrates some possible outcomes if the jackpot continues to increase for the November 28^th Powerball drawing.

Jackpot Values and Expected Returns for November 28 Powerball Drawing
Advertised Jackpot	Cash Value of Jackpot	Tickets Sold	Chance of No Winning Tickets	Expected Value of $2 Ticket
$500 million	$327 million	176.3 million	36.6%	$1.53
$550 million	$360 million	226.7 million	27.4%	$1.50
$600 million	$393 million	277.0 million	20.6%	$1.48

A $2 Powerball ticket typically has an expected return of $1 meaning that half of the money spent on tickets will not be paid out in prizes.  Because there have been 15 weeks of drawings with no winners the rollover in the Powerball jackpot is about $203 million.  This means a $2 ticket purchased this week has an expected value of $1.53.  It is more likely than not that ticket sales will be above the projected amount of 227 million, which will raise the jackpot but dilute the expected value of each ticket sold.  The expected value of a ticket will likely remain near $1.50, but as the jackpot rises it becomes much more likely that there will be at least one winner on Wednesday night.  A winner on Wednesday will reduce the advertised jackpot on Saturday December 1 to $40 million (cash value of $25.8 million).

(Not) Leaving Las Vegas: When Unemployment Happens in Vegas the Jobless Stay in Vegas

Since February 2009 the unemployment rate in Las Vegas has averaged 13.1% and never dropped below 10.1%; it now stands at 11.5%.  Jobless workers have not left the metro area despite the persistently high unemployment rate and lack of job growth since the recession ended.  This stunning lack of out-migration, Las Vegas’ labor force of about 980,000 workers declined by less than 200 people in the past four years, is puzzling because there are better job prospects for the unemployed and underemployed in other parts of the western U.S. 

Every other state has a healthier labor market than Nevada and every major metropolitan area has a lower unemployment rate than Las Vegas.  The unemployment rate in North Dakota is 3.1% and has not been above 4.2% since February 2009.  Unemployment rates in Nebraska, South Dakota, Utah and Wyoming are 3.8%, 4.5%, 5.2% and 5.2% respectively.  The combined labor force in these states is more than 3.5 times larger than Las Vegas, and could easily absorb jobless workers leaving Las Vegas.  The labor forces in these relatively healthy states have grown by an average of less than 1.4% over the past four years.  In other words their labor markets are expanding at a steady but unspectacular rate.

The last four years stand in stark contrast to Las Vegas’ recent history.  Between 2004 and 2008 the Las Vegas labor force grew by 16.8%.  Between 2000 and 2004, a time of relatively slow economic growth for the U.S. overall, its labor force grew by 14.1%.   This means that about one-quarter of the Las Vegas labor force arrived between 2000 and 2008.  These recent arrivals came to a city in the midst of a real estate boom but have persevered through four years of high unemployment and plunging real estate values.

A comparison of Las Vegas and cities on the Gulf and Atlantic coasts that also experienced a real estate boom and bust in the past decade is informative.  Between 2004 and 2008 the labor force grew by 13.4% in Fort Myers-Cape Coral, Florida and by 12.7% in Myrtle Beach, South Carolina.  Since then their labor forces decreased by 1.3% and 4.2% respectively.  If the labor force in Las Vegas contracted at the same rate as it did in Fort Myers or Myrtle Beach, because unemployed workers left the city to find employment elsewhere, the Las Vegas unemployment rate would be 1.3 to 3.9 percentage points lower.

Labor forces have declined in many cities across the U.S., even those that did not experience a real estate boom and bust, but not in Las Vegas.  Much like the gambler who stays at the blackjack table believing his luck will change with the next shoe the people who came to Vegas for economic opportunities are hanging on and hoping that 2013 will be different.

Hurricane Sandy Likely to Increase Unemployment Rate to 8.0% or 8.1%

The November jobs report (released on December 7^th) will be the first one to include household and payroll survey data gathered after Hurricane Sandy.  It is likely that November’s unemployment rate will jump from its current level of 7.9% to 8.0% or 8.1% due to Hurricane Sandy.  Sandy had a devastating impact on the tri-state area of New York, New Jersey and Connecticut where about one eighth of U.S. output is produced.  A leading indicator of the unemployment rate is the weekly report of new unemployment insurance claims.  A spike in new jobless claims means that a large number of workers were displaced from their jobs.  As I explain below, Hurricane Sandy displaced 145,000 workers as measured by new jobless claims in the first full week after the storm hit.

During the week of November 10^th (the most recent week for which detailed state data are available) over 63,000 jobless workers in New York filed for first-time unemployment insurance benefits, compared to about 21,400 new claims one year ago.  Similarly, over 46,000 jobless workers in New Jersey filed for first-time unemployment insurance benefits during the week of November 10^th, compared to just over 12,000 new claims one year ago.  The rate at which workers lost their jobs nearly quadrupled in New Jersey and nearly tripled in New York compared to November 2011.

The following charts compare the year-to-year change in new unemployment insurance claims the week of November 10^th, the first report to reflect Hurricane Sandy effects, and four-week moving averages of year-to-year changes in new claims over the previous 20 weeks.  For example, the annual percentage change in new claims for November 3^rd is based on a comparison of data for the week of November 3^rd and the three previous weeks to the corresponding weeks in 2011.  The charts indicate that, for New York and New Jersey, new jobless claims were consistently below 2011 levels until Hurricane Sandy hit.

Hurricane Sandy caused about 80,000 people to lose their jobs and file for first-time unemployment insurance benefits in one week in New York and New Jersey alone.  Although the effect of Hurricane Sandy on the rest of the country is smaller, it isn’t negligible.  The following chart compares the year-to-year change in new jobless claims the week of November 10^th to four-week moving averages of year-to-year changes in new claims for the rest of the United States (excluding New York and New Jersey).  The chart indicates that new jobless claims were up about 12% in the first full week after Hurricane Sandy, or an increase of 65,000 claims.

Hurricane Sandy’s displacement of 145,000 workers in one week is enough to increase the U.S. unemployment rate by 0.1 percentage point, from 7.9% to 8.0%.  The November unemployment rate is based on worker’s labor force status for the week ending November 17^th.  That means that one more week of new jobless claims data will factor into November’s unemployment rate.  The preliminary new claims data for the week of November 17^th shows a smaller increase in displaced workers, probably half as many as the 145,000 displaced in the prior week.  We will know more on November 29^th when more detailed and complete data for the week of November 17^th are released.  At this point it is most likely that the November unemployment rate will jump to 8.0% or 8.1%.

1.7 Trillion Weeks of Unemployment Benefits

In the past four years Federal and state unemployment insurance programs paid about 1.7 trillion weeks  (32.7 million years) of unemployment insurance benefits to jobless workers as they continued their job search.  32.7 million years is a remarkably long time period that is usually reserved for events measured on the geologic time scale (South America fully detached from Antarctica about 32.7 million years ago during the Oligocene Epoch).  Unemployment benefits were paid to an average of nearly 8.2 million workers per week, every week, for the past four years.  The unemployment insurance rolls have been quite high for an unusually long time because of the depth of the recession, the weakness of the recovery and because Congress and the President extended unemployment benefits so that job losers could collect benefits for up to 99 weeks. 

More generous benefits undoubtedly provided greater financial support for job losers and their families, but also encouraged jobless workers to be more selective in their job search and remain unemployed longer.  Many Democrats and Keynesian economists view the unemployment insurance program, food stamps and other social safety net programs as economic stimulus.  On the other hand conservatives, such as Casey Mulligan of the University of Chicago, argue that the work disincentives of the unemployment insurance program and other safety net and entitlement programs increased the depth and length of the recession.

One way to quantify the opportunity cost of providing unemployment insurance benefits to approximately 8.2 million jobless workers per week is to consider how many employees could have been hired using those resources.  Although unemployment insurance benefits vary by state, the typical weekly benefit is about one half of a worker’s previous weekly wage.  This means that the cost of insuring 8.2 million jobless workers per week is about the same as the wage and salary costs of employing 4.1 million workers per week. 

Many economists have complained that the government stimulus didn’t include enough investment in infrastructure or purchases of goods and services.  Our representatives in the Federal government chose to pay people to search for work rather than employ them directly for public works projects.  But how many roads, bridges, schools and other valuable public sector investments could have been completed instead of paying for 1.7 trillion weeks of job search?  Instead of paying half of the typical weekly salaries of 8.2 million people looking for work each week, we could have instead:

• Paid the salaries of every worker employed in the construction and repair of streets, highways and bridges for the next century
• Paid the salaries of every elementary and secondary school teacher in the U.S. for four years.
• Paid the salaries of all workers in the motor vehicle (and parts) industry for two decades.

Democrats and Keynesian economists lament that state and local government employment has fallen 1.8% over the past five years instead of the 3.9% growth from 2002 to 2007.  The relatively small decrease in state and local government payrolls pales in comparison to the cost of jobless benefits over the past four years.  The money paid to unemployed workers per year over the past four years is equal to about 1/5 of the annual salaries of all state and local government employees combined.

Hoover Dam, The Grand Coulee Dam, LaGuardia Airport, The Lincoln Tunnel and many other public works projects were built during the Great Depression when many of the workers on these projects had few other job options.  The economic approach to dealing with the 2008-2009 recession has been quite different.  In 2013 we will reach 2 trillion weeks of unemployment benefits paid since the recession began.  When the history of this recession and recovery is written it will be clear that we did not use this time of excess capacity and idle workers to re-build and re-tool our infrastructure.  We will not be able to point to the dams, bridges, highways, schools and hospitals that were built during the recovery even though about two million construction workers lost their jobs after the residential real estate market collapsed and many of them are still out of work.  Instead the approach of this Administration and this Congress has been to pay people who lost their jobs to look for work, even though many of the jobs that were lost in the recession are no longer there.