Detroit’s Job Creation Problems

Detroit urgently needs job creation.  No American city faces more difficult economic problems than Detroit.  Even seemingly good economic news is misleading.  Detroit’s unemployment rate fell from 27.8% in the summer of 2009 to 16.3% in the most recent jobs report.  However, the apparent decline in unemployment is illusory.  Unemployment dropped because jobless residents either left the city or gave up looking for work; employment in Detroit actually declined by 1.8% between the first five months of 2009 and the first five months of 2013.

Some of Detroit’s economic problems are due to the mass exodus from the city.  Detroit’s population declined by 810,000 (about 54%) since 1970, or the equivalent of the population of Columbus, Ohio, the nation’s 15^th largest city.  Enough residents have left so that the sprawling city of Los Angeles has a population density 63% higher than Detroit’s.  So many skilled workers have left Detroit that manufacturing businesses find it difficult to hire qualified workers even as the demand for their products has recovered in the past few years and the unemployment rate is 9 percentage points above the national average.

Detroit is No Longer the Motor City

In 1978 when the “Big Three” automakers had an 80% share of the domestic car market there were over a quarter of a million auto manufacturing jobs in Detroit.  Today, there are fewer than 38,000 auto manufacturing jobs in Detroit and suburban Wayne County, a decline of 85% since 1978.  Thirty five years ago Detroit was the home to one in four auto industry jobs in the U.S.  Today only 4.7% of U.S. auto industry jobs are located in Detroit and suburban Wayne County.  The decline in manufacturing jobs in Detroit is not limited to the auto industry.  Since 1978 there has been an 88% decline in employment in manufacturing industries other than motor vehicles.

A Longer Workweek in Manufacturing

At a time when many economists are concerned that most job creation consists of part-time jobs, the workweek in Detroit is getting longer.  The average length of the workweek in Detroit manufacturing establishments was 48.4 hours per week in 2012, about 6.7 hours longer (16.1%) than the U.S. average for manufacturing establishments.  The following chart shows that the average length of the workweek in Detroit’s manufacturing sector increased by 15.2% between 2003 and 2012, compared to a modest 3.2% increase for the U.S. overall.  (For ease of exposition the length of the workweek has been normalized to 100 in 2003.)

A substantially longer workweek means that during the economic recovery (from 2009 to 2012) about 44% of the increase in labor inputs in Detroit’s manufacturing sector were achieved by a longer workweek instead of job creation.  The following chart illustrates what might have happened had there not been increases in hours worked per employee.  Manufacturing employment in Detroit in 2012 would have been 15.2% higher had the length of the workweek remained constant (at 42 hours per week) from 2003 to 2012.  (Again for ease of exposition total labor inpouts in 2003 have been normalized to 100.)

Do Manufacturing Establishments in Detroit Face a Shortage of Skilled Labor?

There are a number of reasons why the length of the workweek in Detroit increased more substantially than in the remainder of the U.S.  If establishments in Detroit faced more economic uncertainty they may have chosen a longer workweek rather than incurring the costs of hiring additional workers.  If establishments in Detroit faced higher fixed costs of fringe benefits per worker, such as health insurance coverage, they may have also chosen to increase hours per employee instead of hiring more workers.  In addition collectively bargained agreements between manufacturing firms and unions may have specified that workers retained during previous reductions in force would see increased hours per week before new employees could be hired.

The sharp increase in the length of the workweek may also be evidence that manufacturing establishments in Detroit face a shortage of skilled workers.  If businesses find it difficult to attract qualified workers because of the exodus of skilled workers over the past decade, the best way to accommodate labor demand may well be to increase hours per employee instead of creating jobs.

Conclusion

The forecast for job creation in Detroit remains bleak despite the Federal government bailout of domestic auto manufacturers.  Detroit is no longer The Motor City as fewer than 5% of auto industry jobs are located there.  About 44% of the increase in labor inputs in manufacturing in Detroit has occurred due to a longer workweek instead of job creation.  Employers in Detroit are likely relying on a longer workweek rather than more job creation because of the exodus of skilled workers from the metro area as well as uncertainty about Detroit’s economic future.

Is Tiger Woods Half As Good As He Used To Be?

 

How much lower are Tiger Woods’ chances of winning a Major Championship than they were in his prime?  The statistical evidence indicates that Tiger’s chance of winning this week’s Open Championship are substantially lower than they were in 2008.  The evidence is not yet conclusive that his chance of winning a Major has dropped by more than 50%, however.

During Tiger’s prime (1997-2008) he won an incredible 30% of the Majors he entered (14 out of 46) but has not won any of the last 16 Majors in which he has competed.  Tiger in his prime would have experienced a 16 Major drought about once every 83 years.  Even if Tiger’s chance of winning a Major were half as much as in his prime, he would still be expected to win 3 Majors every 5 years – an accomplishment over an extended career surpassed only by Jack Nicklaus.

Tiger’s drought in Majors is inconclusive evidence so far that his odds of winning a major have dropped by half – but it is getting close.  A golfer with a 15% winning percentage in Majors has a 7.4% chance of not winning 16 consecutive Majors.  It would, however, be a significant departure from the norm for a golfer with a winning percentage of 15% to go 5 years without a Major Championship.

Do Stand Your Ground Laws Deter Violent Crime?

Florida’s “stand your ground” law is coming under attack after Saturday’s acquittal of George Zimmerman for murder and manslaughter charges in the death of Trayvon Martin.  Stand your ground laws expand the legal justification for the use of lethal force in self-defense.  Since Florida passed its law in 2005, 20 other states passed similar laws between 2006 and 2009.  A recent paper by Cheng Cheng and Mark Hoekstra of Texas A&M University presents empirical evidence that these laws “increase homicides by a statistically significant 8 percent, which translates into an additional 600 homicides per year” in the states (including Florida) that passed “stand your ground” laws from 2005 to 2009.  Moreover, they find that these laws do not appear to deter burglaries, robberies or aggravated assaults.

The Cheng and Hoekstra study compares crime rates before and after laws were passed in “treatment” states to crime trends in the “control states” where there was no change in the law.  This is a standard practice in social science policy evaluation studies.  Cheng and Hoekstra control for differences in policing and law enforcement, changes in economic conditions, and changes in the demographic composition of states and provide several different empirical specifications of their model.  Their careful study suggests that their key results are robust to alternative specifications of the empirical model.

I remain skeptical of the Cheng and Hoekstra results for three reasons:

• The raw decline in homicides per 100,000 residents between 2000 and 2011 is very similar for states that did and did not enact “stand your ground laws” from 2005 to 2009.  The Cheng and Hoekstra result relies on percentage changes in homicide rates.  The percentage decline was smaller in “stand your ground” states because they had higher homicide rates in 2000.
• Cheng and Hoekstra did not present results for rapes.   The raw decline in the number of rapes per 100,000 residents between 2000 and 2011 is larger for states that enacted “stand your ground laws.”  In other words, a simple comparison of crime rates suggest that “stand your ground” laws prevented more than 1500 rapes per year in the 21 states that passed these laws.
• Homicide rates and trends differ substantially across states which makes this kind of policy evaluation quite difficult.  For example, I compare homicide trends in Nevada, ground zero for the real estate bust, and North and South Dakota where the economy has been booming because of natural gas and oil exploration.  A consistent estimate of the impact of “stand your ground” must control for cross-state differences in homicide levels and trends.

Homicides Declined by Similar Amounts in States That Did and Didn’t Pass “Stand Your Ground” Laws

The following chart compares annual population-weighted average homicide rates across two groups of states, those that passed “stand your ground laws” between 2005 and 2009 and those with no change in the law since 2000.

 In states that would eventually pass “stand your ground” laws the average homicide rate was 27% higher in 2000 than it was in states that would not change their law.  The reasons for these cross-state differences in homicide rates are likely to persist regardless of a change in the law.

Homicide rates fell in both groups of states, but the timing of the decline was somewhat different.  Homicide rates started declining about two years earlier in states with no change in the law.  A simple comparison of three-year average homicide rates from 2000-02 to 2009-11 indicates:

• The average homicide rate in states that passed a “stand your ground law” declined by 0.79 per 100,000 residents between 2000-02 and 2009-11.
• The average homicide rate in states with no change in the law declined by 0.89 per 100,000 residents between 2000-02 and 2009-11.

Homicide rates fell less in states that passed a “stand your ground law.”  The magnitude of the difference is about 1 homicide per year per 1,000,000 residents.  There were 7,272 homicides in 2011 in the 21 states that passed “stand your ground.”  The simple calculations above suggest that there might have been 135 fewer homicides in these 21 states had the law not been passed.  This difference is neither practically or statistically significant.

Cheng and Hoekstra estimate a much larger effects of the law, about 4.5 times as large as the raw difference in homicide rates presented above.  They contend that “stand your ground” was responsible for an additional 600 homicides in 2011.  Their effect is larger in part because the percentage decline in homicide rates was larger in states that did not pass the law (because these states already had a lower homicide rate).  In addition their methodology attempts to control for other factors that could explain homicide trends.  It is not clear which of these factors caused the regression-adjusted results to be larger than the simple comparisons of mean homicide rates.  They also attempt to exploit differences in the timing of changes in the law to measure it’s effects.  Unfortunately 13 of the 21 states that passed “stand your ground” did so in 2006 and 4 more in 2007, so it is difficult to ascertain whether the impact of the law occurs with a lag.

Rapes Declined More in States That Passed “Stand Your Ground” Laws

Cheng and Hoekstra attempted to estimate the deterrent effect of “stand your ground” on burglary, robbery and aggravated assault crime rates.  They found no effect.  They did not, however, attempt to measure the impact of these laws on rapes.  Had they done so they would have seen the following pattern:

The rate at which rapes occurred was 28% higher in 2000 in states that would eventually pass “stand your ground” relative to the states that would not change their law.  Rapes declined in both groups of  states between 2000 and 2011.  A simple comparison of three-year average rates from 2000-02 to 2009-11 indicates:

• In states that passed a “stand your ground law” rapes declined by 5.9 per 100,000 residents between 2000-02 and 2009-11.
• In states with no change in the law rapes declined by 4.7 per 100,000 residents between 2000-02 and 2009-11.

The decline in rapes was more substantial in states that passed a “stand your ground law.”  The magnitude of the difference is about 1.2 rapes per year per 100,000 residents or more than 10 times the impact of the law on homicides as described earlier.  Taken at face value this implies a reduction of 1,579 rapes due to the “stand your ground” law in 2011.  Despite the larger decline in rapes in “stand your ground” states, the percentage decline is slightly larger (16% compared to 15.6%) in states with no change in the law because these states started with a lower crime rate in 2000.

State Difference in Crime Trends

The purpose of the policy evaluation study is to impute state-specific crime rates but for the passage of the “stand your ground” law.  This is an extremely difficult task and requires an understanding of why crime trends differ from one state to another.  As the previous charts have shown murders and rapes were generally declining in states regardless of whether a “stand your ground” law was passed.  It is clear that “stand your ground” was more likely to be enacted in higher crime rate states.   A similar correlation between state-specific crime trends and the passage of “stand your ground” would invalidate the empirical approach used by Cheng and Hoekstra to evaluate this law.

As an example of the difficulties encountered when comparing cross-state differences in crime trends consider the case of Nevada and the Dakotas.  The following chart shows that the homicide rate in Nevada fell by 43% between 2006 and 2011 after increasing from 2000 to 2006.  In contrast the homicide rate in North and South Dakota (combined) increased by 265% between 2000 and 2011.

 

South Dakota and North Dakota passed stand your ground laws in 2006 and 2007 while Nevada did not change its law.  Clearly there are many other important differences between these states and the economic conditions in these states have changed substantially over the past decade.  Nevada suffered more than any state from the real estate collapse and recession.  Between 2006 and 2011 its unemployment rate jumped from 4.2% to 13.2%.  Between 2006 and 2011 North and South Dakota benefitted economically from an energy boom with some of the lowest unemployment rates in the U.S.  The boom also brought big population growth and an increase in crime rates.

The comparison of Nevada and the Dakotas illustrates why one should be skeptical of studies that compare percentage changes in homicide rates across states.  In the Dakotas in 2000 there were only 0.8 homicides per 100,000 residents per year.  Thus even a massive percentage increase in the homicide rate in the Dakotas led to an increase of about 2.2 homicides per 100,000 residents per year by 2011.  In contrast the 43% decline in the homicide rate in Nevada between 2006 and 2011 led to a decrease of about 3.9 homicides per 100,000 residents per year.  It makes more sense for a policy evaluation study to compare changes in homicide rates per 100,000 residents than percentage changes in state-specific rates, because even modest percentage declines in high crime rates states will save more lives than very large percentage changes in lower crime rate states.  Finally, it is worth noting that even after the large percentage increases in homicides in the Dakotas and the more modest percentage declines in homicides in Nevada, the homicide rate in Nevada remains 74% higher than in the Dakotas in 2011. 

Conclusion

Although it is tempting to evaluate the impact of “stand your ground” laws by comparing the trend in homicides in states that passed laws since 2005 to trends in states with no change in the law, one should do so with great caution.  First, homicide rates differ widely among states and there are equally important underlying differences in crime trends among states.  “Stand your ground” laws were passed in states with higher violent crime rates, on average.  This means that roughly equal declines in homicides per 100,000 residents in states with and without changes in the law translated into smaller percentage reductions in homicide rates in “stand your ground” states.  I consider this to be weak evidence that “stand your ground” laws increase homicides because it presumes that in the absence of the law homicides per 100,000 residents would have declined proportionately more in the states with the highest homicide rates.

This post also presented new evidence that rapes per 100,000 residents declined more substantially in states that passed “stand your ground” laws.  While I am skeptical of cross-state comparisons in crime trends, if one believes the evidence that “stand your ground” increased homicides one should also believe that rapes were reduced by more than 10 times as much as the increase in homicides after the passage of “stand your ground.”

Washington DC’s $12.50 Living Wage Will Harm Working Class Residents of the District

Yesterday the Washington D.C. City Council approved a $12.50 living wage that would apply to retail establishments operating in spaces with at least 75,000 square feet if they are owned by companies with at least $1 billion in annual sales.  If Mayor Gray signs the bill the cost of operating big box retail establishments in the District will increase substantially.  The living wage ordinance is bad news for workers and consumers in Washington, D.C.  There is no doubt that the ordinance will reduce job and shopping opportunities for people who live and work in the District.  The biggest losers will be working class District residents who would have worked and shopped at the retail establishments that would have been built but for the ordinance.

The Washington Post’s Wonkblog erroneously claims that: (1) “the literature” suggests that raising minimum wages in cities has no negative effect on employment and (2) large corporations “are better equipped to absorb higher labor costs.”  Businesses are not sponges that exist to absorb costs – they operate to generate income for their owners, investors and shareholders.  If Washington D.C. adopts laws that raise costs for retailers, there is no shortage of alternative locations elsewhere in the U.S., Mexico, or China to locate retail outlets.

Wonkblog also believes that big box retailers can afford a nationwide $12 minimum wage because some Berkeley professors said so.  Apparently a $12 wage would lead to a “pretty negligible” increase in costs equivalent to 1.1% of revenue.   It is clear that Wonkblog does not understand how razor-thin profit margins are for big box retailers as they compete with each other and with online retailers.  Combined profits at Wal-Mart, Target, Home Depot, Lowes and Best Buy last year were 3.6% of their combined annual sales.  An increase in costs equivalent to 1.1% of revenue would put a massive dent in the profitability of brick and mortar retailers when one considers that online retailers would be largely immune to the higher costs caused by a $12 minimum wage.

The D.C. living wage will kill jobs in the District because retailers in D.C. compete with suburban retail outlets.  According to the Department of Labor the most common jobs in the retail sector are “cashiers” and “retail salespersons.”  The D.C. ordinance would require big box retailers in the District to pay wages (even to beginning entry-level employees) that are much higher than wages paid by other retailers in the metro area.  The most recent data from the Bureau of Labor Statistics Occupational Employment Survey indicates that 75% of cashiers in the Washington D.C. metro area earn less than $11.65 per hour and approximately 64% of retail salespersons in the Washington D.C. metro area earn less than $12.50 per hour.  In other words, the ordinance will cause big box retailers to continue to locate stores in suburban Virginia and Maryland and avoid the District.  Wealthier District residents, who rely less on public transportation, are inconvenienced less by commuting to the suburbs to shop.  Working class District residents will find it much less convenient and much more expensive to commute to the suburbs to work as a cashier or retail salesperson or shop at a discount store.

The ordinance will keep big box retailers from locating in the District and cause the few big box retailers already in D.C. to leave for the suburbs.  Washington, D.C. has 230 convenience stores and 173 liquor stores but only one big box discount department store.  The number and type of retail establishments in the District with at least 100 employees are listed below (These data are from the Census Bureau’s County Business Patterns and Zip Code Business patterns):

Retail Establishments With At Least 100 Employees in Washington, D.C.
Supermarkets	21
Non-discount Department Stores	2
Baked Goods	2
Electronics/Television	2
Discount Department Stores	1
Home Center	1
Computer	1

In contrast, Davidson County Tennessee (Nashville), with approximately the same population as Washington, D.C. has 22 supermarkets, 6 discount department stores and 5 non-discount department stores with at least 100 employees.    Nashville residents have many more options when it comes to shopping for discounts than D.C. residents.  The living wage ordinance will mean that District residents will continue to be deprived of discount shopping opportunities in their own neighborhoods.

Let’s hope that Mayor Gray has the courage to veto the living wage ordinance.  It is bad public policy that harms the working class residents of the District.

Which National League Teams Have The Most Consistent Hitting?

Baseball team hitting statistics tend to focus on a single number, such as an overall batting average for the team.  A single average may mask wide dispersion in batting averages across players.  For example, two National League teams with identical .250 batting averages for their eight starters (other than the pitcher) can pose very different problems for opposing pitchers.  A .250 team batting average can be achieved with half of the players hitting .200 and the other half hitting .300 or with all starters possessing a batting average of .250.  Of course, more unequal batting averages makes it easier for opposing teams to pitch around a team’s best hitters.

The most common measure of dispersion in random variables, such as batting averages, is the standard deviation.  A team with a high standard deviation in individual batting averages has less consistent hitting up and down the lineup.  A team with a low standard deviation in individual batting averages has consistent hitting throughout the lineup.  A second measure of dispersion is the coefficient of variation, which in the case of team batting averages would be the ratio of the standard deviation to the mean batting average.

The following table lists the mean batting average, the standard deviation and the coefficient of variation for the 8 hitters on each National League team with the most at bats so far this year.  Teams are ranked by the standard deviation of their team’s batting average.  Philadelphia and Los Angeles have the smallest standard deviation in team batting.  New York and Atlanta have the least consistent team batting, with standard deviations more than three times higher than in Philadelphia.

Team	SD	CV	BA
PHILADELPHIA	0.0143	5.2%	0.277
LOS ANGELES	0.0168	6.3%	0.267
MIAMI	0.0200	8.3%	0.239
CHICAGO	0.0201	8.1%	0.247
WASHINGTON	0.0218	8.2%	0.265
SAN FRANCISCO	0.0223	8.1%	0.277
SAN DIEGO	0.0270	10.2%	0.264
CINCINNATI	0.0291	11.0%	0.265
ARIZONA	0.0310	11.6%	0.267
PITTSBURGH	0.0310	12.0%	0.258
ST. LOUIS	0.0402	13.8%	0.291
MILWAUKEE	0.0414	14.8%	0.279
COLORADO	0.0420	14.7%	0.286
NEW YORK	0.0435	17.8%	0.244
ATLANTA	0.0499	20.2%	0.248

 The next table lists the mean on-base percentage, the standard deviation and the coefficient of variation for the 8 hitters on each National League team with the most plate appearances so far this year.  Teams are ranked by the standard deviation of their team’s on-base percentage.  Philadelphia and Los Angeles have the smallest standard deviation in on-base percentage.  Colorado and Cincinnati have the least consistent team on-base percentage, with standard deviations more than five times higher than in Philadelphia.

Team	SD	CV	OBP
PHILADELPHIA	0.0107	3.3%	0.329
LOS ANGELES	0.0202	6.1%	0.333
MIAMI	0.0218	7.3%	0.297
PITTSBURGH	0.0262	7.8%	0.335
SAN FRANCISC0	0.0315	9.4%	0.334
CHICAGO	0.0323	10.6%	0.305
SAN DIEGO	0.0330	9.9%	0.333
ST. LOUIS	0.0349	9.9%	0.351
ARIZONA	0.0357	10.8%	0.330
WASHINGTON	0.0386	11.7%	0.329
ATLANTA	0.0405	12.4%	0.326
MILWAUKEE	0.0444	13.2%	0.337
NEW YORK	0.0475	15.0%	0.316
COLORADO	0.0506	14.6%	0.347
CINCINNATI	0.0594	17.1%	0.347

Of course a low standard deviation in either batting average or on-base percentage is only valuable if averages are high.  Uniformly poor hitting is also undesirable.  Each team’s starting hitters can be evaluated by both the mean and standard deviation in their hitting statistics.  In this sense team hitting statistics can be displayed in the same type of graph that financial economists use to show risk and return.  Teams prefer both a higher mean and a lower standard deviation in hitting statistics.

The following graph shows the batting average risk-return graph for National League teams.  Philadelphia and St. Louis are on the “frontier”.  Philadelphia has the lowest risk.  Among the higher batting average teams, St. Louis has the lowest standard deviation.

The following graph shows the on-base percentage risk-return graph for National League teams.  Philadelphia and St. Louis are again on the “frontier”. 

Whether hitting is measured in terms of a batting average or on-base percentage, the Philadelphia Phillies have the most consistent hitting and lowest dispersion across players.  The St. Louis team batting average is 14 points higher than the Phillies and the risk-return tradeoff means that the Cardinals’ higher average was obtained by nearly tripling the dispersion in averages across players.

Regardless of how hitting is measured, Miami, Chicago and the New York Mets are the worst hitting teams.  The Mets, for example, have more dispersion in batting averages across players than the Cardinals but have a team average that is 47 points lower than St. Louis.  The inconsistent hitting in the Mets lineup makes it much easier for opposing teams to pitch around their best hitter David Wright (who is hitting .306).

The Granite Mountain Hotshots And Fatal Risks Faced By Firefighters

On June 30 nineteen firefighters from the Granite Mountain Hotshots lost their lives fighting the Yarnell Hill Fire in Yarnell, Arizona.  This tragedy was the worst for firefighters and first responders since 9/11.  According to USA Today the Yarnell Hill fire was “the worst wildland firefighting tragedy since 25 were killed in the Griffith Park Fire in Los Angeles in 1933.”  Yesterday the bodies of the nineteen firefighters returned home to Prescott, Arizona.

The disaster reminds us that firefighting is one of the most dangerous occupations in the U.S.  According to the Bureau of Labor Statistics the fatality rate for firefighters is about 2.5 times as high as the fatality rate for the rest of the workforce.  Until the Yarnell Hill fire, the fatality rate for firefighters had dropped over the past few years.  The annual number of firefighters killed on the job in 2009-2011 (2011 is the most recent year available for the Census of Fatal Occupational Injuries) was about 33% lower than the average from 2003-2008.  The following chart indicates year-by-year fatality totals for firefighters from 2003 to 2011.

The chart shows that the recent improvement in firefighter safety is due to a 50% reduction in traffic-related fatalities.  Firefighters face the risks of fire, explosions, falling objects and harmful and toxic substances once they arrive on the scene, but many firefighters lose their lives in traffic accidents speeding to the fire.  From 2003-2008, traffic related fatalities accounted for about half of all deaths of firefighters on the job.  Since 2009, traffic related fatalities have accounted for 35% of firefighter deaths.  Finally, the chart indicates that the loss of nineteen lives in Yarnell is more than the annual average number of firefighter deaths while fighting fires (non-traffic fatalities) over the past three years.

While reductions in traffic accidents involving firefighters have saved lives over the past few years, the Yarnell Hill fire reminds us that firefighters have one of the riskiest jobs in the U.S. workforce.  Every day firefighters face the risk of serious injury or death while protecting others from harm’s way.