MORE DEATHS FROM UNEMPLOYMENT THAN COVID?

While a complete review of the forecast provided in this May 9, 2020 blog is not yet possible, subsequent data compiled as of mid-February 2023 indicates that a significant proportion of the excess deaths (excluding COVID-19) that have occurred since February 2020 are potentially associated with job loss and increased unemployment. For added information, see the final supplemental portion of this blog post. I now proceed with the blog post of May 9, 2020.

Floating around the internet has been a rumor that each increase of 1% in the U.S. unemployment rate leads to an added 40,000 deaths — resulting from either poor physical or mental health affecting those unemployed, in some cases even after they become re-employed or retire. Renewed interest in this question was recently stimulated by a New York Post article of April 20, 2020 by John Crudele, titled: “Is unemployment really as deadly as coronavirus?”

Crudele cites as his initial source a character named Ben Rickert in the 2015 movie The Big Short about the Great Recession. Played by Brad Pitt, it’s Rickert who challenges his fellow Wall Street workers with the pronouncement: “Every 1 percent unemployment goes up, 40,000 people die. Did you know that?”

And by logical extension, the Post writer notes that increasing the unemployment rate by, say 10%, could lead to “400,000 deaths that have nothing to do with the virus and everything to do with the distressed economy.”

Apparently deciding that a quote from a movie may not be adequate substantiation, writer Crudele tracked down what may be an original source, a 1981 book titled Corporate Flight: The Causes and Consequences of Economic Dislocation written by academics Barry Bluestone, Bennett Harrison and Lawrence Baker, then again cited by Robert Carson, Wade Thomas, and Wade Hecht in their 2005 co-written book titled Economic Issues Today: Alternative Approaches, 8th Edition.

The actual paragraph of interest from the 2005 book is this: “According to one study [the one by Bluestone et al.], a 1 percent increase in the unemployment rate will be associated with 37,000 deaths (including 20,000 heart attacks), 920 suicides, 650 homicides, 4,000 state mental hospital admissions and 3,300 state prison admissions.”

While the specific impetus of the 1981 research was about a different cause of unemployment — then the concern was corporate flight from America, today it’s pandemic — this early study asks a question that has never been more pressing than today. That question is: Does unemployment affect mortality?

The New York Post writer’s story should not be viewed as an endorsement of this prior research. The author cites two differences between then and now. One is that, in the current situation, the federal government has “acted quickly to help the unemployed” which did not happen in earlier years when companies were moving overseas. Another factor cited is that much of the current job loss is for furloughed workers who may “get their jobs back once companies reopen their doors.”

The concluding remark of the Post article is: “Let’s hope that the data from 1981 is — excuse the expression — dead wrong.” But, really?

Should we discount the relevance of this historical precedent. The potential size of the impact cited by Bluestone, et al is particularly troublesome. Could just a 1% point increase really be associated with 37,000+/- U.S. deaths? So again, this got me asking: Could this earlier research really be valid — potentially applicable even today?

And that’s what led to the bit of statistical research I have conducted which essentially concludes: Yes, there appears to be a definite association of mortality with unemployment.

And the impact likely is now greater than estimated by Bluestone, Harrison, and Baker nearly 40 years ago. With this updated analysis, a more current estimate appears to be more in the range of 47,500 deaths for every 1% point increase in America’s average annual unemployment rate.

The rest of this blog article describes how this tentative conclusion is reached.

THE SCIENCE OF UNEMPLOYMENT-MORTALITY RESEARCH

Before launching into my own statistical examination, I decided to first check out whether and in what ways other similar research has been conducted more recently. While not widely known or publicized, there actually is a compendium of academic research assessing the relationship of unemployment to various indicators and causes of mortality.

A public health and epidemiology researcher, named M. Harvey Brenner, PhD, has conducted numerous studies focused on the relationship between economic well-being and community health. Dr. Brenner has held positions in public health and epidemiology at institutions including the University of North Texas Health Science Center, Hanover Medical University, Johns Hopkins University and Yale University.

In a BBC podcast/interview dated March 4, 2016, Dr. Brenner is quoted as stating that the figure of 40,000 U.S. deaths for every 1% rise in unemployment is still a “good rule of thumb.” This statement is based on his work dating back to the 1970s. In a 1979 hearing before the Joint Economic Committee of the U.S. Congress, Dr. Brenner noted that a 1% increase in unemployment is related to a 2% increase in total U.S. mortality. Added mortality follows behind increased unemployment over a 2-5 year time lag. And with more recent work, Dr. Brenner has pressed forward with continued detailed analyses— encompassing research on both sides of the Atlantic.

For example, a May 2016 study by Dr. Brenner titled The Impact of Unemployment on Heart Disease and Stroke Mortality in European Union Countries was prepared under the auspices of the European Commission. While focused on two indicators of unemployment-related mortality, the research begins by summarizing a range of European and U.S. epidemiological studies that “have shown since the 1970s that socioeconomic status (SES) is a stable risk factor for illness and mortality in individual persons.” A key observation from this literature review is that: “Unemployment is an important risk factor for mortality and morbidity — especially if the unemployment is of long duration.”

An underlying factor behind employment-related mortality is the stress resulting from unemployment. This stress manifests itself psychologically in outcomes that have been cited to range from depression to human violence including indicators as for suicide and homicide. Economic stressors include loss of income, poverty and economic inequality. Medical implications range from changes in diet and exercise to mortality as with heart disease and stroke.

Specific findings of Dr. Brenner’s empirical research from the European experience of 2000 - 2010/11 (including the period of the Great Recession) include the following:

• The incidence of both heart attacks and strokes in the EU is strongly correlated with increases in unemployment.

• There appears to be some delay between the time of becoming jobless to when an individual is at greatest risk of death — about a two-year lag for heart-related mortality and a somewhat longer delay averaging about three years for strokes.

• Southern European countries appear to experience less incidence of unemployment related mortality than northern Europe — for reasons not entirely clear but potentially linked to healthier Mediterranean diets, warmer winters and more closely knit family ties, offering greater “social cohesion” for southern countries.

• Older workers experience higher rates of unemployment related mortality — including “spread effects” that influence health beyond a person’s normal working life and extending across to other members of affected families.

This background review provided a starting point for thinking about how to frame an updated initial statistical analysis, assessing the historical relationship between unemployment and mortality in the U.S. — a discussion which now follows.

A MORTALITY-UNEMPLOYMENT HYPOTHESIS

For this initial research, I have applied a simple linear regression model to assess the statistical relationship between unemployment and mortality rates in the U.S. — over a time period extending from 1929 (just at the onset of the Great Depression) to 2017, the years for which data was most readily available from recognized sources. Four data variables are assessed with this statistical testing process:

• Age adjusted mortality rates (as a % of the population), available for the years 1900-2017 as reported by the National Center for Health Statistics of the U.S. Centers for Disease Control and Prevention (CDC). Note: CDC/NCHS data adjusts death rates after 1998 to the age composition of the U.S. population as of 2000. Death rates prior to this time are taken from historical data.

• Annual average civilian unemployment rates as reported by the U.S. Bureau of Labor Statistics and reported by the Economic Report of the President (2020), covering the years from 1929-2019.

• Years with significant (above normal) mortality due to non-employment factors but that address considerations such as wars or pandemics are also included as a third binary variable in the analysis (as will be further described below).

• Time trend is of particular importance for mortality as rates have experienced a strong downward trend over the last century with the advent and continued positive health results of modern medicine.

The form of the mathematical relationship being tested is:

Mortality Rate = f (Unemployment Rate, War/Pandemic, Time)

But before we get to the statistical testing, let’s take a look at the underlying data.

U.S. UNEMPLOYMENT & MORTALITY EXPERIENCE

We start the presentation of U.S. unemployment and mortality experience since 1929 with the first in a series of graphs. The red line shows annual deaths in the U.S. as a percentage of total (age-adjusted) U.S. population. The blue bars depict annual average unemployment rates for the same years. Trend lines are also shown for mortality and unemployment (together with the statistical regression equations and statistical fits measured as R-squared).

At first glance, there appears to be little relationship between unemployment and mortality rates over the last 88 years. In large part, this is because the long-term trend is for rapidly declining mortality due to advances in medicine, public health and other related factors such as improved diets and reduced poverty in the U.S.

Mortality in the U.S. has gone from 2.081% of the U.S. population in 1929 to a low of 0.725% in 2014. As shown by the R-square factor, the linear trend extended through 2017 explains 95% of the variation in year-to-year mortality over this time period.

While there is also some long-term downward trend indicated for unemployment, the greater factor is the still intensely cyclical nature of unemployment in the U.S. — with boom followed by bust — albeit of less severity (so far) than experienced in the Great Depression starting in late 1929. Only 13% of the variation over time in unemployment is explained by the trend line.

While there is a clear and strong overall mortality trend, substantial variations nonetheless appear at the micro level. This is most apparent when the mortality variable is focused on year-to-year changes in death rates — as illustrated by the next chart. The unemployment data remains the same as shown in the prior chart.

This graph reveals considerable swings with year to year changes in the mortality rate — which are otherwise obscured by the overall long-term and strong downward secular trend. Note that despite the now more readily apparent fluctuations, most years are associated with a lower death rate than the year before. The biggest upward spikes in mortality occurred during the years of the Great Depression.

Much as when evaluating prices and trading on the stock market, these period-to-period gyrations often obscure more fundamental changes. A way of addressing this issue is to calculate moving averages — applied to both the unemployment and mortality data. In this case, a three-year moving average has been applied. For example, the moving average shown with the year 1932 would be the average of the years 1931-33.

The next graph depicts essentially the same information as with the prior graph, but with application of three-year moving averages to smooth over some of the volatility associated with annual data.

We are now at a point where it is possible to begin seeing more of the relationship between unemployment rates and annualized changes in U.S. mortality. However, if it is the case that mortality is influenced by unemployment, this effect is not necessarily felt immediately.

This is illustrated by studies such as those of Dr. Brenner which indicate that there is about a 2-year lag between unemployment and the most pronounced changes in heart-related mortality. With strokes and based on European experience, there is a somewhat longer average 3-year delay between the event of unemployment and resulting mortality increase.

In this case, we are concerned with overall mortality associated with a range of morbidity factors. Some will take longer to materialize than others — depending both on the eventual cause of death and the circumstances specific to each individual situation. In this case, after experimenting with several time delay factors, the analysis appears to best support an average 2-year lag from time of unemployment to resulting increased incidence of mortality.

This shift is shown by the following graph. The red mortality line is the same as with the prior graph — as we are measuring mortality as of the year that death occurred. However, the unemployment data is now shifted two years ahead of what was shown on the prior graph. In effect, the unemployment occurs ahead of any subsequent mortality.

In this graph, we now begin to see a much closer alignment between the timing of significant changes in unemployment and resulting changes (averaging about 2 years later) in mortality.

So far, we have explored two key factors affecting mortality, first, the long-term trend of reduced death rates over time due to improved medicine and better standards of living. And second, we have addressed the potential shorter-term effects that changing unemployment rates may have on mortality.

So, it is useful to now ask the question: Are there any other factors largely independent of the time trend and unemployment rate that may also potentially have a measurable effect on mortality?

With the European study, Dr. Brenner realized that there was a difference in the mortality profile between northern and southern Europe. Measuring the extent and the validity of this effect involved placement of a “dummy” or binary variable in the equation - a “1” for living in one part of Europe and a “0” for living in the other part of Europe.

With the current analysis, there are two factors that clearly appear to have effects on mortality other than time and unemployment — war and pandemic. In evaluating these two additional factors, it is clear that over the period of 1929 to present, there have been perhaps eight such events worth assessing for their relative significance:

Major War & Pandemics (with 100,000+ fatalities per year of the event):

• World War II (with significant mortality from 1942-45)

• Asian Flu (with most U.S. deaths in 1958)

• Hong Kong Flu (with most U.S. deaths in 1969)

Minor Wars & Conflicts (with far less than 100,000 fatalities per year but yet significant both for combat death and post-war trauma):

• Korean War (from 1950-53)

• Vietnam War (with the most active combat from about 1964-73)

• Gulf War (short lived and with relatively few fatalities in 1990-91)

• The 9/11 Terror Attack (in 2001 but with potential spread effect)

• Iraq/Middle East Conflicts (spread over a long time period, most active from about 2003-11)

The following chart is a replay of the prior graph but with these events and their timing also shown.

As this composite graph shows, there are varied spikes and troughs with the war and pandemic events considered. However, the overall effect on mortality is also being influenced by changing unemployment which has been mixed over the duration of these pandemic events.

This brings us to the regression analysis which is intended to better sort out the relative effects of each variable considered together with its statistical significance.

STATISTICAL REGRESSION ANALYSIS

After considering multiple options, the rough form of the selected regression equation is calculated as:

Mortality = (0.01448 x U-Rate) + (0.00089 x War-Pandemic) - (0.000126 x Time) + 0.01608

where:
Mortality = U.S. age adjusted death rate (as % of total population - calculated as a 3-year moving average)
U-Rate = annual average unemployment rate (expressed as % or decimal - also calculated as a 3-year moving average)
War-Pandemic = dummy or binary figure of “1” for major war/pandemic, “0.5” for minor war/conflict, or “0” otherwise
Time = Year from start of observations, extending from 1932 (Year 1 of observations) to 2016 (85th observation)

Note: Due to the use of a dependent mortality variable significant to five decimal places, coefficients to at least 8-9 decimal places are required as per the ANOVA tables provided at the end of this blog post.

For purposes of this analysis, the unemployment rate (U-Rate) is the primary independent variable of interest. If the unemployment rate increases by 1% (or by .01 in decimal terms), the death rate goes up by a 0.0001448 proportion (or by 0.01448%) of the total U.S. population. This may not seem like much, but when multiplied by 328.2 million U.S. residents (in 2019 as estimated by the U.S. Census Bureau), this equals about 47,500 added deaths for each year of this 1% unemployment increase.

For a few added statistics (as detailed at the end of this blog), the R Square factor for this regression is 0.9828 with the adjusted R Square at 0.9822, meaning that 98% of the variation in annual U.S. mortality rates is explained by the regression equation.

All variables are significant at a 95% confidence level. Based on t-statistics and P-values, the most significant independent coefficient is time — reflecting the strong downward mortality trend over 84 years. The unemployment (U-Rate) variable is also strongly significant at a 11.25 t-value. The War-Pandemic variable is also significant, but less so at a t-value of 4.23.

The relationship between mortality as predicted with this linear regression versus actual mortality experience is depicted by the following graph.

The predicted values generally do well to catch the upsurge in mortality with the Great Depression through World War II and provides its best fit in the period from about 1950-74. From about 1974-93, actual deaths run consistently somewhat below projection with the biggest mismatch in the early to mid-80’s — a time of two recessions but with a relatively large and healthy baby boomer population in prime working years.

The forecast is then relatively on-track with exceptions of somewhat underestimating mortality in the wake of the recession of 2000 and the 9-11 event of 2001. And mortality in the most recent years of 2013-16 has not declined as much as would have been expected with recovery from the Great Recession — which may be in-part due to some flattening out of the long-term mortality trend.

CONCLUDING OBSERVATIONS

While preliminary, the results of this initial analysis are several-fold and sobering:

• There is a statistically significant relationship between unemployment and mortality in the U.S.

• There appears to be a time lag of about two years, on average, between the event of unemployment and subsequent associated mortality.

• Results of this analysis are consistent with other economic and epidemiological studies that have also quantified both a significant and time-lagged relationship between unemployment and subsequent changes in mortality.

• The one-year increase of 47,500 deaths for a 1% point increase in unemployment associated with this analysis would represent 1.67% of the 2.84 million deaths (from all causes) across the U.S. as of 2018. This is below the 2% mortality increase previously estimated by Dr. Brenner but is consistent with a decline in overall mortality due to improved U.S. health and medical care since the time of his earlier analysis.

• This 47,500 estimate also corresponds reasonably well with the Bluestone, et al estimate of up to about four decades ago. With 40-45% U.S. population growth over 35-40 years, the Bluestone 37,000 estimate made then would translate to about 51,500-54,000 today based on population growth alone — but then adjusted down for overall declining mortality in the intervening years.

• The mortality numbers can get much higher, as the annual unemployment rate and/or the duration over which unemployment lasts increases. If annual average unemployment increases by 10% points over a full year through about April of 2021, the nation is at risk of as many as 475,000 unemployment-related deaths. If the unemployment impact were to move into the 20%+ range over a 1-year time frame, the death toll conceivably escalates to nearly the million-person range. The same result ensues if there is an average 10% increase that lasts over two years.

• The time lag means that the most substantial after-effects on mortality resulting from current rapid increases in unemployment are likely to be experienced about 2-3 years from onset of the pandemic and large scale job layoffs — potentially even after the initial mortality effects of the COVID-19 virus have clearly subsided.

• The public health perspective nationally and at state/local levels should be adjusted to address not just pandemic deaths but also mortality that reasonably might be expected with increased unemployment — both short- and longer-term. This clearly suggests getting Americans back to work and to stabilized incomes sooner rather than later. And it also depends on a much more balanced approach in planning for and executing responses to future pandemic or other emergency situations of national or global proportion in the years ahead.

SUPPLEMENTAL NOTES: ANOVA DETAIL

Summary output or analysis of variance (ANOVA) detail for the selected regression equation as calculated with Microsoft Excel is provided by the following table.

Several added technical notes are made regarding the analysis conducted to date:

• In assessing an appropriate time lag for mortality, separate regressions were run for the mortality-unemployment-war/pandemic datasets with time lags of 1, 2, 3, and 4 year periods. The 2-year lag performed best in terms of R squared, t-value and F-statistic outputs. The 3-year lag was next best, then the 1-year and 4-year regression runs.

• With the binary/dummy variable, consideration was given to a single variable (with 0.0, 0.5 and 1.0 values for no issue, minor war/conflict and major war/pandemic, respectively) versus a two-variable approach. The two-variable approach involved separate dummy variables — one for major war/conflict periods and a second for minor war/conflict years. The two-variable approach yielded a slightly lower R-square coefficient and also lower t-values than experienced with a single binary variable. Of considerable importance was the observation that the coefficient for the minor war/conflict variable was 49% of that for the major war/pandemic variable, which supported the 1.00/0.50 application with the single binary variable as the preferred model.

• This analysis was limited to a 1929 and later time period as the most readily available unemployment information starts at 1929, while NCHS age-adjusted mortality data is available back to 1900. Currently out-of-print information from the National Bureau of Economic Research (NBER) is available from 1900-54, but with some data mismatches in the overlapping time periods.

• As noted, the mortality data is age-normalized rather than reflecting the actual year-to-year mortality for populations for the dataset sampled. Future refinements might also include non-revised rather than NCHS age-normalized mortality for purpose of comparison and explanatory utility.

• As discussed by analyses such as Brenner’s work for the European Union, there are other potentially inclusive indicators of economic health than sole reliance on unemployment statistics. More encompassing indicators might include gross domestic product (GDP), various measures of income inequality, and measures of socio-economic status (SES).
  There are two problems with this approach. First, is that there can be substantial disagreement on what these more encompassing indicators should comprise. Second and perhaps of more importance for this analysis, while these SES-type indicators might be more all-encompassing, they tend to lag behind unemployment. Despite its limitations, unemployment data represent, in effect, the proverbial “canary in the coal mine” — providing the most advanced warning of what may be to come.

Additional information in response to inquiries is available on request. This blog is subject to minor edits subsequent to initial posting on May 9, 2020. For regularly updated information regarding U.S. COVID case-mortality, employment and economic recovery to date from the pandemic, click here on Economy Watch.

Update Notes (As of February 23, 2023)

Since formulating the forecast model early on in the pandemic, it has been this author’s objective to review actual outcomes with comparisons as may be possible to the forecast model. This has proved challenging for three primary reasons:

• COVID has unfolded, then receded and re-emerged in several waves to date — with mid-pandemic assessments challenged, especially as mortality associated with unemployment increases occurs well after the fact (estimated at about 2 years). This necessitates a considerable wait (till about now) to begin conducting a retrospective review.

• The forecast model applied (as described above) also uses a 3-year moving average to smooth the data, removing excess data “noise” that could render interpretation of the results more challenging. The combination of a 2-year lag between unemployment and mortality coupled with a three year moving average means that some of the data for out-years of the pandemic (as for 2022) is not yet available. NCHS-CDC compiled mortality data is currently available only through the 2nd quarter of 2022. The 3-year moving average for mortality means that results for the near tail end of the pandemic (assuming it is 2022) requires mortality data extending through 2023 which is now just underway.

• Finally, the rapid intervention of the Federal Reserve and congressionally approved stimulus meant that, while unemployment briefly spiked to nearly 15%, jobless rates were then subsequently rapidly reduced — more so than occurred with other major economic disruptions which did not include such massive stimulus. In effect, there may be economic hardship (including business closures, start/stop cycles with various lockdown measures, and related economic stresses) beyond what is fully captured by unemployment rates alone.

With these admittedly significant caveats in mind, it is nonetheless useful to review what can be discerned from COVID and unemployment experience to date. As of mid-February 2023, NCHS-CDC data indicates that there have been an estimated 1.3 million excess deaths in the U.S. since February 2020. Of this number, 320,000 (25%) of the deaths over this approximately 3-year time frame are labelled as “excess deaths — all causes excluding COVID-19”.

Using data that is available and interpolating for information not yet available, the forecast model as formulated and applied indicates that between about 225,000-300,000 deaths across the U.S. may be associated with both direct and indirect consequences of unemployment and related stresses to date. The range is based on how data points not yet available are interpolated based on information that is available to date.

Even with uncertainties of methodology and data for both CDC estimates and modeling with this analysis, initial evidence suggests that a majority (perhaps 70 to 90+ percent) of the non-COVID excess deaths since start of the pandemic may be associated with unemployment and related economic stresses through the pandemic.

An objective of this author is to update this forecast model and retrospective analysis once a more complete dataset is available — most likely at some point in the 2024-25 time period. Going forward, any value with this initial modeling exercise as occasioned by The Big Short may be subject to review and refinement in the years ahead. A more sophisticated, science-based and appropriately nuance approach ideally should enable practitioners and policy makers to better understand the linkages between major health crises and economic dislocation — for improved policy choices should events of this type and magnitude recur in the future.

Initially Copyrighted © May 9, 2020 by E. D. Hovee & Company, LLC
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WHAT PRICE CORONAVIRUS?

Note: This article has been updated from the initial post (as of April 22, 2020).

Nearly 2,000 years ago, a sage spiritual leader asked those who would take on any project of consequence to "first sit down and figure the cost so you'll know if you can complete it." Just to make sure that no one misunderstood the range of costs to be considered, this founder of what became the Christian movement offered up two illustrations : the cost of a physical construction project (building a tower), and the cost of going to war (assessing the capabilities of one's army against that of the enemy).

So, in our current pandemic, count not only the costs of those who will be directly stricken by an unseen enemy, but also those who will be affected by potential loss of livelihood and home. In 2020, the U.S. and much of the rest of the world have gone into virtual lockdown in a mad rush to avert or mitigate the mortality effects of COVID-19 virus — albeit with minimal consideration of the short and long-term cost necessary to beat this previously unknown foe.

As David Farragut, flag officer of the U.S. Navy declared in a battle of the American Civil War: "Damn the torpedoes, full speed ahead."

In 244 years of the American republic, there has never been an occasion when the U.S. and most states effectively shut down the social and economic life of the country. Not even in wartime have such radical steps been taken.

Yes, there's lip service given to mitigating the collateral damage, but no meaningful initiative to date to directly and honestly answer the threshold question: Is the cure worse than the disease? Is the price we are paying to combat this pandemic too high?

Or perhaps the question is better phrased as: What price is too high? 

COMPARING THE COST

For some perspective on the human toll of the virus, it is useful to make comparisons with other conditions affecting mortality in the U.S.

Consider this. As shown by the following graph, there were over 2.8 million deaths in the U.S. for calendar year 2018. By comparison, as of April 16, 2020, COVID-19 has claimed close to 31,000 American lives. The number of deaths attributable to coronavirus, to date, equates to about 1.4% of total annual mortality in the U.S.

New York Times figures do not include more than 4,800 people in New York City who died and are believed to have had the coronavirus. As reported by the Times, many of those patients died without being tested, a consequence of a strained medical system and a persistent lack of testing capacity.

As depicted by the graph, a few other selected indicators are of note. The number of people in the U.S. who have died of COVID-19 to date can be calculated as equivalent to:

• Approximately 5% of the number deaths of all persons age 85 and over who passed away in 2018 (for any and all reasons)

• 6% of the number of deaths for those age 75-84

• 6% of the number deaths of those who die of heart disease each year
  (the number 1 killer in the U.S.)

• 7% of the number of deaths attributable to cancer

• 24% of the number of deaths caused by accidents of all types
  (Just over 100% of the number of deaths attributable to vehicle accidents)

• 47% of the number of deaths attributable to diabetes

• 68% of the number of deaths attributable to flu & pneumonia

• 78% of the number of deaths attributable to kidney disease

• 83% of the number of deaths attributable to suicide

While lost jobs are not a form of physical mortality, they do represent human and economic loss. As of April 11, the increased joblessness of more than 22 million means that well over 500 jobs have been lost for every coronavirus death, to date. And like COVID-19 deaths, the number of unemployed has yet further to go on its upward trajectory.

Bottom line and while tragic, the number of deaths attributable to COVID-19 is only a small fraction of all mortality — only a small fraction of deaths attributable to the major causes of death in the U.S. Why this undue focus on an unseen killer which has, so far, added only marginally to the on-going death toll associated with the everyday cycle of life and death across America?

Would we lock America down like this to go all out to stamp out the causes of diabetes or cancer? What about to eliminate all car accidents by shutting down all motor vehicle transportation? Or to prevent all suicides?

What is it about COVID-19 that gives the fight to take on this pandemic a higher priority than addressing any other substantial form of mortality? Is this a battle worth impoverishing large segments of the American population for years to come?

As of mid-April, the chief economist of the International Monetary Fund (IMF) has stated that:

As countries implement necessary quarantines and social distancing practices to contain the pandemic, the world has been put in a Great Lockdown. The magnitude and speed of collapse in activity that has followed in unlike anything experienced in our lifetimes.

Do we care about the cost to America? Do we care about what the IMF now says will be the worst downturn since the Great Depression of nearly a century ago? Or is our answer to be that of the medical bureaucrats who, like Farragut, would command: "Damn the torpedoes, full speed ahead."

Damn the cost, damn the livelihoods lost. Damn the kids whose educations are disrupted. Damn the increased disparity between the haves and have nots. Damn the loss to public revenues essential to provide public services. Damn the death of small businesses and gig workers into the hands of an engorged  corporate America. Damn the deplorables to strengthen the self-proclaimed rule of the medical-bureaucratic elites.

When will anyone have the guts to answer these questions?

REJOINDERS

There are those who would undoubtedly say this is over-the-top hyperbole. Even if there has never been an explicit policy pronouncement that this fight is worth any cost, there seems to be some implied social contract to make this effort, no matter what price it takes.

And there are technical issues, like:

• This is a disease of unknown proportions unlike other diseases for which risk can be more readily measured and calibrated — so it's worth going all out to beat the unknown (unlike such known maladies as cancer, diabetes, car accidents or suicides for which risks are now well defined).

• What we do know is that the more than 40,000 deaths (as of April 21) will grow larger by the time this is over — maybe now to 100,000 or 200,000 or if we relax too much off measures like social distancing, conceivably increasing to less likely worst case scenarios of perhaps 1 - 2 million.

• And there may be recurrences, flare-ups in the infection rate, as a start-stop stutter process that continues indefinitely — at least until a vaccine is found.

There are counters to these likely responses. No choice of this magnitude should occur merely as part of some implied social construct. If cost be damned is to be the order of the day, that should occur via informed and explicit legislative actions at federal, state and local levels including a policy commitment to hold the rest of society harmless, not impoverished — no matter what it takes, whether short or long term.

And regarding the technical issues. While this is a disease with many unanswered questions, the unknowables have been pared back as the health care community learns more day-by-day. We certainly know that the major variables to managing the risks going forward involve slow and measured ease-off of social distancing, widespread testing for the virus and for antibodies, getting therapeutic drugs and vaccines quickly to market (to reduce and ultimately stop the ravages of this disease), and (quite possibly) contact tracing using the tracking powers of ubiquitous smart phones.

In instances where the private market is not responding quickly enough — whether with masks or testing equipment — the powers of the presidency could be more actively applied to compel production and distribution. Now, not later.

We even have learned enough from disease modeling to better understand the potential range of outcomes and how the key variables likely influence these outcomes. And the monitoring tools related both to COVID-19 and economic recovery are there to gauge what is happening in real time — then scale the regulatory mechanisms to ease-off or tighten accordingly.

But there's one step that is essential to make all this work. There needs to be some general and explicitly communicated consensus of what a reasonable mortality target should be. It's not good enough to say that we aim to bring the rate down as much as possible. That approach suggests that our resources are infinite and that the cost imposed to get that one extra life saved is worth the universe.

Rather, aim for realistic targets. Based on what is known today, it now appears reasonable to aim for a goal of less than 100,000 deaths before this is over - but accept the possibility of going as high as 200,000 (as within the range of variability). Note: Even if there were 200,000 COVID-19 deaths this year, annual deaths in the U.S. would increase by only about 7% — going from an underlying rate of about 2.8 - 2.9 million per year to perhaps 3.0 - 3.1 million.

Coronavirus mortality targets should ideally exclude estimates of co-morbidity where an elderly or immune-compromised individual is likely to experience near term death anyway, with or without the virus. The medical profession needs to come clean and quantify the extent to which co-morbidity is or is not occurring.

WHICH WAY FORWARD?

Maybe it’s time to pay a bit more attention to sage advice — historically proven. Count the cost before going into battle. Do it before continuing to spend extraordinary sums of funds while impairing business and household incomes with minimal regard to both foreseen and potentially unforeseen consequences. Not just the cost from one perspective, but from all relevant viewpoints before making decisions as to the most viable course of action.

Putting this in today's context, this could mean continuing to follow the course of continued lockdown if the cost to the rest of humanity is widely viewed as worthwhile to save a small percentage of deaths, including at least some who are likely to die anyway. Alternatively, tack toward a new course of COVID-19, social and economic recovery if this is the more acceptable price.

Either way, make the choice consciously and with the consent of the governed. The worst of all worlds will be to attempt to muddle through — putting the cart ahead of the horse. The interests of the self-anointed over those doing the work — on whom the future of our republic ultimately rests.

THE CHANGING LINK BETWEEN COVID-19 & JOBLESSNESS

What a difference a week makes! And not for the better.

In one week from March 29 - April 4, the U.S. coronavirus mortality rate quadrupled from a cumulative total of 7 to 28 deaths per million U.S. residents. The number of new unemployment claimants has gone from over 10 million to nearly 17 million jobless. Claimants in the last three weeks now equate to about 10.4% of the normalized base of 145+ million U.S. workers covered by unemployment insurance.

This blog post updates statistical information posted on April 4 regarding the state-by-state relationship between COVID-19 and joblessness. And this week, our update also looks at the breathtaking changes in both metrics as have occurred in just one week’s time.

Note: For this review, information is as of an April 4 weekly report (the latest for unemployment claims). In contrast, COVID-19 death data is posted daily. As of April 11, the nationwide COVID-19 mortality rate had jumped from 28 deaths per million population (on April 4) to over 65 deaths (April 11), led by dramatic increases in New York mortality (as shown by the map on our website home page). This linkage review will be updated for April 11 jobless data when this updated employment data is next made available Thursday, April 16.

EARLY APRIL UPDATE

The following graph compares cumulative unemployment claims filed during the weeks ending March 21, March 28 and April 4 with COVID-19 mortality as of April 4.

As with last week’s chart, cumulative unemployment claims over a 3-week period are calculated as a % of total covered employment, by state. Mortality is calculated in terms of the number of COVID-19 related deaths per million residents, by state.

If you were to compare this week’s scatterplot with that of last week, the most obvious change is that the variability of outcomes has greatly widened. As of March 29, New York’s cumulative mortality rate was 50 coronavirus deaths per million residents. One week later, the mortality rate had more than quadrupled — to about 214 deaths per million population as of April 4 (a number that has again more than doubled to 482 deaths per million as of April 11).

And the span of joblessness has widened. As of March 28, Pennsylvania and Rhode Island had experienced unemployment claims equaling 13.8% of total base employment. As of April 4, Rhode Island’s rate has jumped to 19.4%.

Several other items are of note in with this update:

• There is much greater spread of mortality rates than before. In addition to New York, the states of New Jersey and Louisiana have mortality rates at just over 100 deaths per million residents. Michigan now tops 50. All other states and territories have mortality rates of less than 50 deaths per million.

• There is also a much wider range of unemployment claims — from 4% to nearly 20% of the total workforce covered by unemployment insurance. At the upper end of the spectrum are Rhode Island, Michigan, Pennsylvania, Nevada and Hawaii, all in the 16-20% range. The lowest rate is noted for South Dakota at 4%. Despite having almost off the chart mortality, New York’s 8.4% rate of unemployment claims is below the nationwide 3-week cumulative rate of 10.4%.

• And there is now a circled group of about 25 states (with Washington D.C. included) that so far have experienced a combination of relatively low mortality (below 45 deaths per million population) and low rates of unemployment claims (below 10%). This group includes some very rural as well as urban states - with strong representation from the sunbelt and mid-America. These are the states that, so far, might be considered as either best practice examples or just plain lucky as compared to the other half of states.

Focusing more on what is working well for half the states may help in determining a path out for further curve flattening and for economic recovery. But before jumping too quickly to conclusions, we shift the analysis to also consider changes of the last week - both in mortality and joblessness.

CHANGES IN COVID-19 DEATHS VS JOB LAYOFFS

An added feature of this update involves comparing outcomes of the period ending March 28 with April 4, one week later. The following graph displays much of the same information as the prior illustrative depicting trends in coronavirus morality versus job layoffs. The difference is that the prior graph showed the pattern of deaths versus layoffs as of a single point in time (April 4) — while the following graph shows the change from one week to the next (from March 28/29 - April 4).

As shown by the graph:

• Just 8 states experienced an increase of COVID-19 mortality of at least 20 deaths per million residents in the most recent week ending April 4. Led by New York (with a week-over-week increase of nearly 165 deaths per million, other places with mortality increases of 20 or more per million residents were New Jersey, Louisiana, Michigan, Connecticut, the District of Columbia, Massachusetts and Rhode Island. The increase in mortality rates nationwide averaged 21 deaths per million residents.

• All the remaining states experienced mortality rate increases of 20 added deaths per million residents or less. The extent to which these slower rates of increased mortality is due to good public policy, lower population density, and/or just plain luck varies widely across these states.

• There also is wide disparity in the job layoff patterns across these states with slower growth in mortality rates. Georgia’s unemployment claims increased by 9% of the employed work force in the one week ended April 4. At the other end of the spectrum, Colorado’s unemployment claims increased by less than 2% points. In effect, there appears to be no guaranteed employment reward for those states that have contained the death toll, to date.

A PATH TO ECONOMIC RECOVERY?

At long last, serious discussion about re-starting the American economy is beginning to get underway. At this point, there is as yet no certainty as to the appropriate mechanisms or timeline for Americans going back to work. This is likely to be a subject of intense debate from health care and economic perspectives, not to mention anticipated differences across the political spectrum

Are there any observations from this coronavirus and joblessness discussion worth considering as part of this emerging policy debate? Three observations are suggested:

• First, as has been previously suggested, this updated review further reinforces the observation that national and state-level policy should not be framed around the assumption that “one size fits all.” New York’s experience (together with that of adjoining states) is well beyond the pale of what has been or is likely to be experienced across much of the rest of the country. This is made abundantly clear by the very different curve flattening trajectory that the nation’s most populous state — California — has followed. And the experience of a California is yet different from that of a Wyoming or South Dakota — where mortality rates increased at 0 and 1 deaths per million residents over this most recent week.

• Second, this analysis does suggest that continuous monitoring of changing mortality may serve as a useful guidepost for determining which states are best positioned to ease away form their business shutdowns — and how quickly. States like New York, New Jersey, Louisiana, and Michigan appear to warrant continued lockdown until the mortality growth rates ease back to the national average (or to below zero). Other places like DC, Connecticut and Pennsylvania that have been relatively calm so far may be erupting as data over the next one to two weeks may demonstrate — warranting more intensive measures at least temporarily.

  Conversely, states that have consistently held mortality increases to well below the national average gain of 21 deaths per million this last week (to a gain of, say, no more than 10 deaths per added million), appear to be the best candidates right now for potential relaxation of shutdown requirements. Of 52 states and territories, 36 appear to be in this category right now. Of these, 20 are below a mortality gain of 5 per million for the week ended April 4.

  As the country reaches the peak of mortality in the days or weeks ahead, the weekly change in national COViD-19 should go to zero and then negative. As the weekly mortality threshold for America declines, states remaining below the national curve offer the best case to be the most quickly rewarded with economic re-boot. This type of monitoring and economic adjustment may be required not only in the weeks ahead but over an extended period to limit the risk of re-infections longer term.

• Third, a similar but more nuanced approach might be taken to states experiencing unemployment claims well above the national average. To what extent are higher rates due to earlier shutdowns, different industry mix (as with a high proportion of businesses dependent on face-to-face contact), unusually draconian business closure requirements, or other factors? Encourage states with unduly high rates but without demonstrable mortality benefit to begin opening sectors posing the least risks of virus transmission.

ANY LINK BETWEEN COVID19 & JOBLESSNESS?

As follow-up to a blog of April 2 on The Jobless Trajectory: State-by-State, E. D. Hovee has done further analysis to compare recent job layoffs with coronavirus related mortality — on a state-level basis. The following graphic is a plot comparing cumulative jobless claims filed during the weeks ending March 14, 21 and 28 with COVID-19 mortality as of March 29.

In the chart, cumulative unemployment claims filed over this three week period are calculated as a % of total covered employment, by state. Mortality is calculated in terms of deaths per million residents, by state.

At first glance, there would appear to be little evidence of any clear statistical relationship between coronavirus deaths and job layoff experience, by state. Even if there were to be a clear statistical relationship, no direct causality is asserted at this point. In other words, no assertion that high mortality necessarily causes a state to exact massive job shutdowns, and no assertion that job shutdowns affect mortality one way or the other.

However, there may yet be a story to tell. As suggested by the graph, four potential clusters of activity are noted:

• First is the experience of the 6 high mortality states — New York, Louisiana, Washington, Vermont, New Jersey and Michigan — with mortality to date ranging between 10-50 deaths per million residents. While each has a slightly different story to tell, all but Vermont are states with relatively high population densities. Vermont is a low population state but in the outer orbit of the Boston and New York spheres of influence — as is obviously the case for New Jersey. Washington state got it early on (clustered in one facility) and Louisiana suffered in the aftermath of Mardi Gras. Michigan is in the early stage of a significant increase in virus cases in the Detroit area. And New York is both highly dense and internationally focused, both of which put the city and metro area at greater risk.

• Second is the experience to date of three states experiencing high job loss of more than 12 to nearly 14% — Pennsylvania, Rhode Island, Nevada — albeit with minimal coronavirus mortality experience to date. One could argue that these states are bearing an unnecessarily high economic burden relative to virus-posed risk, so far.

• Third is South Dakota clustered with 10 other states that have experienced less than 4% job loss and less than 10 deaths per million population. Several of these states, in addition to South Dakota — Arkansas, Colorado, Mississippi, Utah, West Virginia and Wyoming — have below average population densities. Four states — Connecticut, Florida, Georgia and Texas — are more densely populated but with the southeastern states politically resistant to early job shutdown.

• Finally, there are another 30 states that have experienced job losses ranging from 4% to less than 10% but also with relatively low mortality rates. These are critical swing states that may be experiencing somewhat disproportionate job loss relative to reduced mortality — but perhaps investing now in shutdown to better stave off potentially harder pandemic-related times to come.

Currently, there is considerable debate as to whether more stringent national guidelines should be extended (or mandated) across all 50 states uniformly. The alternative is to continue providing state-by-state discretion to adapt to changing conditions when and if warranted.

This analysis, while preliminary, suggests that when it comes down to determining the right tradeoff between human and economic survival, the right choice is not necessarily “one size fits all.” Neither the circumstances nor the policy prescriptions appropriate for New York are necessarily well suited for South Dakota or Wyoming.

In the weeks (and perhaps months) ahead, the death toll will inevitably increase. So will the loss of gainful employment and business opportunity — some quickly recoverable and some not. The pivotal and ever more painful decision will be how to make the trade-off between potential lives lost versus impoverishment for many of the more numerous survivors. .

If there is to be a national mandate, let it be a prescription that sets certain threshold targets, which when violated, will become mandatory at the state level. Otherwise, let state-by-state discretion prevail. And let both the national and state-local level decisions be guided by both real-time heath and economic data — each in tension and balance against the other.

THE JOBLESS TRAJECTORY: STATE-BY-STATE

As of this morning (Thursday, April 2), the U.S. Department of Labor has released the latest week’s unemployment insurance claims. In the week ending March 28, more than 6.6 million Americans filed new initial unemployment claims. This more than doubled the 3.3 million claims filed last week - which itself was briefly the largest single week of jobless claims in U.S. history.

This most recent week’s claims far surpassed economists projections of what the continuing (or escalating) economic damage would be. As the Wall Street Journal went to press late April 1, it was citing a survey of economists who predicted about 3.1 million filed claims — about the same as the week before. The actual result was more than twice the prediction.

The blog begins with a look at national unemployment filings — followed by a state-by-state overview.

National Experience Summarized

Up until the week of March 21, the U.S. had been experiencing an average of just about 250,000 initial unemployment filings per week. As illustrated by the following graph, if one adds the seasonally adjusted figure of 282,000 for the week of March 14 to the 3.3 million filed on March 21 to the 6.6+ million of March 28, the cumulative total claims filed over the last three weeks is about 10.2 million (as a seasonally adjusted figure).

If considered in terms of data that is not seasonally adjusted, the cumulative 3-week claims figure is 9.0 million - as the winter is usually a period of slower economic activity than other times of the year.

This last three-week experience accounts 7.0% of the nation’s 145 million workers covered by unemployment insurance (on a seasonally adjusted basis), or a somewhat lower figure of 6.2% (in raw unadjusted terms). Prior to mid-March, typical weekly filings accounted for less than 0.2% of the nation’s covered job base.

Note: Weekly claims data are not the same as the nation’s unemployment rate — which stood at 3.5% as of February 2020, increased to 4.4% as of March. This is based on survey data which includes unemployed not covered by insurance, as of about March 12 (just before significant layoffs got underway). Due to timing of the survey process, monthly reports likely will understate the actual rate of real-time unemployment over the period that layoffs continue.

STATE-BY-STATE REVIEW

State-level data is summarized on a basis similar to that of the national data — albeit with two caveats:

• State-wide data is only available on a basis that is not seasonally adjusted; and

• Instead of providing raw numbers of unemployment claim, the analysis normalizes the data across large and small states by discussing unemployment claims as a percentage of each state’s covered employment base.

So, let’s take a look. The following graph shows the experience of each of the 50 states plus territories over the weekly unemployment claim periods ending March 14, 21 and 28. Total claims as a % of the state’s total employment base are depicted in rank order, from the most to least impacted.

On initial review, a few items are noted about this rank ordering:

• Pennsylvania and Rhode Island appear to the be most impacted states with unemployment filings through March 28 — with 3-week filings at 13.8% of their respective states’ employment base.

• The top 5 most impacted are rounded out by Nevada, Michigan, and Washington — each at about 10+%. This top five grouping includes three industrial states plus Washington affected early with the virus outbreak and Stay Home requirements, and Nevada which is heavily reliant on tourism.

• In total, 23 states are more impacted than the national average of 6.2%. This is an interesting mix of states — with representation from both coasts plus some states (such as Michigan, Ohio, Kentucky and Indiana) from the industrial heartland.

• Somewhat surprisingly, California, Illinois and New York rank near the middle in terms of layoffs to date. While intensely urban in parts, other significant portions of these states consist of smaller communities that may not yet be as impacted by COVID-19 and associated business curtailment.

• Least impacted to date are the Virgin Islands with only 1% of jobs affected — followed by South Dakota. The lesser impacted states appear to be more rural and/or slower to put in place state-wide social distancing, shelter-in-place, business closure, or other lockdown requirements.

All-in-all, this listing indicates a disparate range of employment displacement experience (with anywhere from 1% to 14% of statewide employment affected through March). A logical question is whether this diversity of experience warrants different, custom-made policy and regulatory initiatives, rather than “one size fits all.” The counter-argument is that states with minimal impact are likely to catch up, leveling the playing field, as virus impacts inevitably widen and/or national policy transitions from guidance to mandates.

Look for more to come - with a deeper dive into the linkage between job displacement and COVID-19 experience.

CORONAVIRUS: COMPUTING THE DEATH TOLL

On March 13, the New York Times reported that the U.S. Centers for Disease Control and Prevention (CDC) had prepared four scenarios of the potential medical impact of COVID-19 (details of which have not been fully published), indicating that anywhere between 200,000 to 1.7 million U.S. residents could die over the duration of the pandemic. And this could involve hospitalization of anywhere from 2.4 million to 21 million people. The hospitalization scenario is particularity concerning to CDC and the public considering that the U.S. only has a little more than 924,000 staffed beds.

What has been released by the CDC for publication are age-specific hospitalization, intensive care (ICU) and mortality rates of the U.S. population based on experience from February 12 to March 16 of this year. While this initial sample is relatively small (at 2,449 cases), it begins to provide a window into potential case-fatality, estimated to range between 1.8% to 3.4% of all persons infected.

The combination of these two studies make it possible to assess potential mortality scenarios relative to more normalized patterns of age-specific deaths in the U.S. That is the purpose of this blog.

We walk through this analysis in several steps:

• First, considering age-specific mortality for the U.S. in a typical year — in this case 2018 as the most recent year with data available from the National Center for Health Statistics (NCHS).

• Second, looking at what is publicly known, so far, about the CDC scenarios of potential U.S. deaths from the pandemic.

• Third, combining the CDC scenarios with recent case-fatality data as generated by the CDC to estimate the percentage distribution of deaths by age.

• Fourth, bringing all of these datasets together to compare how COVID-19 mortality projections compare to underlying existing (or normalized) death rates for the U.S.

As will be evident with the data presented, there is still a considerable range of estimates for many of the variables discussed. With more case experience, it hopefully will also become possible to refine and tighten the range of estimate.

Consequently, this blog may be updated to reflect new information as it becomes available. Questions and comments about the methodology used with this somewhat simplified but potentially informative review are also appreciated.

Age-Specific Mortality

This discussion begins with a review of age-specific mortality rates for the U.S. population (age 15+) as of 2018. As illustrated by the following chart, the annual death rate per 100,000 population ranges from just over 70 deaths per 100,000 persons age 15-24 to about 13,450 per 100,000 who are age 85 and over.

This data serves as a baseline as to the normalized pattern of mortality - independent of effects of a major pandemic such as coronavirus.

CDC Mortality Scenarios

As noted, the New York Times on March 13 headlined an article as: “Worst-Case Estimates for U.S. Coronavirus Deaths.” While details do not appear to have been publicly released of this CDC-sponsored teleconference with 50 experts from around the world, the Times reports that it obtained screenshots of the CDC presentation from someone not involved in the meetings. The newspaper then verified the data with scientists who did participate.

The discussion was aimed to address the question of how many people might be infected, need hospitalization, and/or die as the virus takeshold in the U.S. As reported by the Times:

One of the agency’s top disease modelers, Matthew Biggerstaff, presented the group on the phone call with four possible scenarios — A, B, C and D — based on characteristics of the virus, including estimates of how transmissible it is and the severity of the illness it can cause. The assumptions, reviewed by The New York Times, were shared with about 50 expert teams to model how the virus could tear through the population — and what might stop it.

While details were not provided for all four scenarios, the March 13 article brackets the range of scenarios with low and high estimates for an epidemic lasting for months or even over a year:

• The low estimate indicates that 160 million could be infected with 200,000 deaths.

• The high estimate involves up to 214 million U.S. residents infected with as many as 1.7 million deaths (essentially with a much higher mortality rate for those infested).

It would be useful to have more detail regarding all four scenarios and the assumptions that stand behind each alternative projection. However, even with these summary numbers, it is possible, on a preliminary basis, to model the age-specific implications of these low-to-high mortality estimates.

CDC Age-Specific Case-Fatality Analysis

Subsequent to CDC’s base mortality scenarios, the agency has released age-specific hospital, ICU admission and case-fatality information — covering U.S. cases over the period of February 12 to March 16. As detailed by the following chart, the resulting database comprises 2,449 cases, disaggregated by age group and providing range estimates for each of these three indicators of medical need and result:

• The lower bound of the range is estimated by CDC using all cases within each age group as denominators to calculating each incidence rate.

• The upper bound is estimated by using only cases with known information on each outcome as denominators.

The columns under the blue banners are directly from the CDC data base. The last three columns under the red banner involve supplemental calculations by E. D. Hovee:

• The first red column rate by age reflects the mid-point between the low and high estimates by CDC.

• The second column provides an imputed estimate of deaths in the CDC data base (not directly stated by CDC but calculated from the case rates divided by the mid-range case-fatality rate).

• The third column provides a distribution of the number of age-specific deaths as a percent of the total (and is applied with the next and final step to the analysis which now follows).

U.S Deaths by Age & Coronavirus Scenario

This final step combines the baseline historical mortality data with the death rate scenarios as consistent with the CDC datasets. The following table provides a summary of the results of these calculations:

• The first column provides the age group categories by which the data has been compiled - with the 0-14 age group excluded because it is not shown as part of the NCHS dataset and because no fatalities are indicated with the CDC coronavirus dataset as available, to date.

• The second column shows the number of deaths as of 2018 from the NCHS dataset as the baseline expectation of mortality that might be expected in the absence of COVID-19.

• The third column adds in the low estimate of coronavirus deaths totaling the control total of 200,000 deaths, assuming that there is no overlap of coronavirus deaths with baseline mortality (a topic described further below).

• The fourth column adds in the added increment of high estimate coronavirus deaths, assuming a hypothetical 50% overlap between baseline mortality and coronavirus deaths (also described below).

• The fifth column indicates the cumulative total of existing baseline + low estimate + high estimate added coronavirus related deaths.

• The final column calculates the ratio of cumulative deaths divided by existing baseline mortality.

As indicated by the above chart, the high estimate scenario is associated with an overall mortality rate for persons 15+ that is 130% of (or 30% greater than) the baseline of existing 2018 U.S. deaths:

• For persons age 15-14, the high estimate of death is only 7% greater than existing baseline conditions.

• Conversely, the mortality rate is 36% greater for those those over age 35 than with baseline conditons.

• Generally speaking and consistent with press reports, the mortality rate effect of COVID-19 increases for older age cohorts than younger. The exception is at the 75-84 year age bracket, perhaps a statistical anomaly due to the as-yet relatively small sample size of the CDC database.

These comparisons can also be made in graphic terms, as illustrated by the following graph.

With the low scenario, the addition to existing death rates is much smaller — adding only 1.6% to the death rate for 14-44 year olds and 8.5% to morality for 85+ year old seniors. Over all 15+ age groups, mortality increases by just over 7%.

A critical (and not yet known) variable included with these hypothetical scenarios is the degree of overlap between existing deaths (that would happen anyway) versus deaths that might be attributable solely to COVID-19. In between is a middle category of deaths that might have happened this year without coronavirus but for which the virus was another contributing factor. In some cases, coronavirus would accelerate the time of death, in others it might be a minor factor due to the seriousness of other underlying conditions.

No data has been made available from CDC that provides guidance as to how this may be attribution can best be made. And to a large extent, this may be an unknowable, for example, trying to ascertain to what extent alcoholism or heart disease or diabetes may have each contributed to a person’s demise.

As noted above, for sake of illustration and discussion, this analysis hypothesizes:

• No overlap with the low coronavirus estimate - assuming that coronavirus is the primary death factor due to its relatively low incidence relative to baseline mortality.

• 50% overlap with the high coronavirus estimate - assuming that a higher rate of infection and serious complication will inevitably involve a higher proportion of people with existing underlying issues.

If the 50% overlap estimate is off the mark so that there is little or no overlap with the high coronavirus death estimate, then we would be faced with a worst-worst scenario. In effect this could increase the total annual U.S. death toll from 3.7 million per year with coronavirus to as many as 4.5 million. This means that instead of COVID-19 accounting for a death rate increase of 30%, the increase in death rate would increase by 60%. and for persons 85+, the death rate attributable to coronavirus could increase by as much as 72% rather than 36%.

Conversely, it may be that the overlap is even greater than 50%. This possibility is supported by the observation from cases to date that most fatalities have involved individuals with existing (generally multiple) underlying health issues.

In effect, it may be the COVID-19 is not the sole cause of death but a contributing factor — in some cases the final tipping point, in others perhaps not. A more granular examination of mortality data might be useful to attempt to quantify how much a person’s life span, on average, is shortened as a result of this virus. Shortening a life by 1 month is much different than reducing the life span of a senior citizen by, say, five years. The appropriate policy choices may also differ substantially based on this type of determination.

IMPLICATIONS

The question of how much America’s coronavirus will increase deaths will have an obvious influence on the inherently no-win task of determining the appropriate trade-off between saving lives and salvaging the country’s economic and social well-being.

This blog intentionally avoids the question of what set of policies best provide the best balance of countering or mitigating two pressing and conflicting catastrophes. Rather, the implications of most immediate interest involve improvements to the base data so that the decisions made will be more informed. From a data perspective, the following suggestions are noted:

1. CDC should be more transparent by fully and publicly disclosing the research methodology and conclusions regarding the four scenarios of potential U.S. death toll already prepared. This information is essential to better understand what is required to achieve each scenario from medical, economic and societal perspectives.

2. Over the next 1-2 months as the virus reaches exponentially more people, it will be important for CDC to continuously and publicly update its databases — each time providing for a larger and clearer window into who will be most affected and how. And as more testing resources become available, conduct random sample monitoring to better benchmark infection and mortality rates.

3. Finally, to the extent possible, it would be extremely useful for more robust CDC datasets to parse out the degree to which death rates at specific age levels overlap with deaths likely due to existing age and underlying health conditions. This will be essential to gaining a better understanding of the true net effect of coronavirus on mortality going forward.

In the weeks ahead, E. D. Hovee will continue to monitor progress on the data side of the coronavirus challenge. And due to a clear economic and development bent, I may offer observations aimed to contribute to ongoing public policy discussion. So, look for updates and feel free to question or critique as the occasion arises.

Take care and be well,
Eric Hovee - Principal

INDEPENDENT FEASIBILITY REVIEW: A VALUE PROPOSITION

Working at the interface between public and private interest is part of E. D. Hovee’s DNA. Prior to forming a consulting firm, principal Eric Hovee managed public-private initiatives for economic development on behalf of two city agencies – first in Portland OR, then Vancouver WA.

One of the roles that the consulting firm has taken on has been to conduct independent feasibility reviews of development projects and their feasibility – for public sector clients. This blog focuses on major public and private developments in the two leading cities of the Pacific Northwest cities – Portland and Seattle – together with resulting observations and lessons learned.

Portland

In Portland, E. D. Hovee was in at the early conception and then gestation of two public-private redevelopment initiatives that have reshaped and revitalized the Central City – as early forerunners of the move back to the city now reaching across the U.S. Portland’s Pearl District extended downtown to the north, the South Waterfront (SoWa) district expanded the Central City to the south.

Pearl District: The firm’s work in what is now known as the Pearl District started in 1992 with a market/feasibility review – leading to a target of 5,000 new housing units with mixed-use development in what was a declining industrial/warehouse area. And all this at a time when there was virtually no new housing development occurring in the greater Central City area.

Four years later, the City of Portland was presented with its first major redevelopment opportunity – a 41 acre former Burlington Northern railroad yard that had been acquired by private investors. The new owners were set to begin development on about half of the acreage subject to City infrastructure investment – re-introducing the street grid and funding of a new streetcar line to connect the Pearl with downtown.

The City wanted to know: a) whether private Central City development was feasible; and b) under what conditions would there be public as well as private return on investment (ROI). The answer provided by the E. D. Hovee analysis was “it depends.”

A moderate density development of about 40 units per acre appeared to be financially feasible given then-current market conditions – even in the absence of major public infrastructure investment. Getting to the City’s density objective of 80-100 units per acre appeared more challenging, most likely not feasible without public investment. Based on sensitivity analysis involving five different market and public incentive scenarios, the higher density options appeared to be feasible if accompanied by a combination of incentives including street infrastructure, public funding of a Central City streetcar, below market rate financing for 25% of units as affordable, and reduction of residential construction and soft costs.

On paper, these measures supported a pro forma with a rate of return sufficient to attract private investment capital and also provide a healthy return on the public investment. Development proceeded, exceeding both public and private expectations. Portland’s first major mixed-use area captured much more of the market at higher increases in rents and values than anyone thought possible. And the public sector then further raised the ante – most notably with added commitments for park and open space as part of the infrastructure package.

The Pearl (pictured to the left) has woven together adaptive reuse and renovation of older industrial structures layered in with new higher density residential and mixed use construction. SoWa (to the right) has taken urban density to new heights.

South Waterfront: Planning for Portland’s South Waterfront began about the time that Pearl District investments were beginning to materialize on the ground. In 1998, E. D. Hovee completed a market and development capacity analysis for this new urban district. Five years later, in 2003 the firm conducted and independent feasibility review for the City’s development commission to assess the feasibility of developing high-rise apartments just south of Portland’s downtown.

The condo market had been proven in the Pearl District – though SoWa units were projected to come in at yet higher costs – requiring top of market sales pricing. The condo market was also noted as potentially vulnerable to an economic downturn.

Conversely, market rate apartments represented a form of high-rise development for which there was “little Portland metro experience in recent years.” Financial feasibility would need to exceed top of market rents to achieve any reasonable ROI – although upside potential was noted if this district were to capture “market rate rental clientele heretofore not experienced in the Portland metro area.”

The City of Portland was planning major investments in supportive street and utility improvements, riverfront public space, streetcar extension and an aerial tram to the Oregon Health & Science University (OHSU) as the city’s largest employer – funded in large part through tax increment financing (TIF). From the City’s perspective, the major risk identified by E. D. Hovee would be a developer slow down or default – best mitigated by conservative tax increment projections and with phasing of public improvements in synch with development activity and resulting TIF cash flow.  

With a development agreement in hand, the developer began initial phase investments in facilities for OHSU and riverfront condos. The condo market came to an abrupt halt with the 2007 advent of the Great Recession. As in other major metro areas, unit prices plummeted. With post-2010 economic recovery, development activity started back slowly then accelerated – this time led by market rate and affordable apartment units – with supportable rents at new higher levels not previously experienced.

Seattle

In addition to the two Portland master plan projects referenced, E. D. Hovee has conducted numerous additional independent feasibility reviews for a range of development projects both in and outside the Pacific Northwest. But I now skip to two more recent publicly owned Seattle facilities for which feasibility reviews have been completed for the Washington State Department of Commerce in 2018 – expansion of the Washington State Convention Center (WSCC) and a new lease agreement for Safeco Field (now T-Mobile Park) with the Seattle Mariners major league baseball team.

WSCC Expansion: The Washington State Convention Public Facilities District is now underway with an approximate $1.7 billion expansion of the largest convention center in the Pacific Northwest. When completed in 2021, the nearly 1.5 million square foot facility will add 434,000 square feet of new event space – more than doubling the event capacity of the existing WSCC convention facility situated one-half block away.

As a public facilities district, state legislation requires the WSCC to have an independent feasibility study conducted prior to issuance of added indebtedness. In accordance with state statute, the purpose of the independent review is to “examine the potential costs to be incurred by the public facility (facilities) district and the adequacy of revenues or expected revenues to meet these costs.”

This review was conducted by E. D. Hovee in two phases – a 2015 Phase 1 review of land acquisition and a 2018 Phase 2 review addressing build-out and operating feasibility. E.D. Hovee’s report identified and evaluated four risk factors and options for mitigation as might affect WSCC build-out and operating feasibility:

• Capital cost risks – as the potential for construction delay/cost overruns and uncertainty regarding non-debt related funding during construction

• Financing & debt repayment risks – related to adequacy of projected lodging revenues as a primary source of debt repayment and capital market requirements

• Operating budget risks – including the possibility of a short-term operating loss, unbalanced revenue mix, and potential transition from operating surplus to deficit

• Institutional risks – related to agreements with King County and capacity to fulfill priority WSCC obligations

Base case revenues as projected by WSCC and advisors appear to be more than adequate to cover bonded debt repayment to 2058. As part of the independent review, E. D. Hovee also prepared a stress test scenario to address the question: What might be the financial implications if a sudden and severe downturn similar to the 2007-09 Great Recession and ensuing 2008-10 lodging tax downturn were to be re-experienced?

The conclusion of the review was that: “If there is a financial risk, it likely emerges at the time of the remaining WSCC Addition bond financing planned for 2020. While adverse conditions are currently not expected near term, WSCC could be vulnerable to a potential economic downturn in the 2019-25 time frame ….”

However, even with what might be perceived as unlikely but clearly adverse debt repayment and operating scenarios, there are multiple financial and operating options that could be considered as possible WSCC mitigation measures. In effect, available financial and operating measures should enable the facility and the public facilities district to weather any reasonably conceivable economic storm.

The Washington State Convention Center Addition (pictured to the left as designed by LMN) will effectively double the combined event capacity of this new $1.7 billion investment - planned for opening in 2017. The independent feasibility study prepared for the Safeco Field (now renamed T-Mobile Park) addresses risks and mitigation for the public owner in conjunction with a now executed new 25-year lease in conjunction with major planned capital improvements.

Mariners Lease for T-Mobile Park: The 47,000 seat Safeco Field ballpark was constructed by the Washington State MLB Stadium Public Facilities District from 1997-99 at a cost of $517 million – with funding from general obligation bonds issued by King County and capital contributions from the Mariners Club. The Mariners initial 20-year lease for the stadium expired at the end of 2018 and was proposed to be replaced by a new lease consistent with a Lease Renewal Term Sheet reached in May 2018.

As with the initial lease, the Club would be responsible for 100 percent of ballpark operations and maintenance (O&M) and all capital improvements (CapEx) work – to an applicable standard to be set at the top one-third of all MLB ballparks. Provisions of the new agreement are also intended to provide a source of funding for an estimated $565 million in ballpark and related capital improvements over the next 25 years.

An independent feasibility review by E. D. Hovee identified several potential risk factors to the public facilities district (as ballpark owner) – related to potential variability of King County funding resources, prospective future Club default or termination, market/competitive factors, and risk of becoming a stranded asset if MLB use were to be precluded over an extended period. These risk factors were all addressed with final agreements in place by year-end 2018. Of perhaps greatest significance, the district and the Mariners Club reached a non-relocation agreement that included a schedule of liquidated damages to be paid by the team in the event of future default on the new lease agreement.

Liquidated damages start at close to $505 million, declining each year over the lease term to a figure of $200 million from the 18th to 25th year of the lease. This provision provides greater financial protection to the district in the event of any currently unforeseen event that, if not addressed, could run the risk of the ballpark being vacated by the team in the future and, in effect, becoming a stranded asset.

Concluding Notes

Independent feasibility review of major economic development projects involving significant public expenditure is useful to public agency participants for two primary reasons:

• Providing a disinterested 3rd party assessment of public sector financial commitments and associated risks,
  together with

• Identification of reasonable options for getting out of financial trouble in the event that a worst-case scenario materializes – whether short- or long-term

For the private developer or facility operator, the benefits of 3rd party review may not be quite as readily apparent – but no less significant. The major benefit is that all parties know they are entering into a transaction with eyes wide open. Potential problems can be better avoided if identified in advance – saving both taxpayers and investors the public embarrassment if not the financial pain of a deal gone awry.

Different Projects, Distinctive Risks: For the four projects profiled, while each involved multiple risks – one always stood out as most pivotal:

• For Portland’s Pearl District, the greatest risk was that the market would not be attracted to urban living. By bolstering the public commitment, the development proved to be a home run – enabling both public and private investments to further up the ante for one of the finest urban neighborhoods in America.

• For Portland’s South Waterfront, the risk was that the market would not step up to the increased prices required for high-rise residential development feasibility. The market did step-up. Then hiccupped – but recovered by switching from condos to apartments in the aftermath of the Great Recession.

• With the Washington State Convention Center’s expansion, the greatest risk going forward relates to the sustained financial capacity to make good on debt repayment — even in the event of a future replay of the Great Recession. And the answer is – it would take an economic event more severe and longer lasting to jeopardize the facility’s financial viability going forward.   

• And for the Seattle Mariners and T-Mobile Field, the #1 risk of concern was the potential (not matter how small) that an MLB team might leave the City before the termination of the new 25-year lease – and before repayment of facility improvements. That concern is largely addressed by the agreement for liquidated damages in the event of future default – a provision added to the initially agreed lease terms.

No transaction can address every potentially conceivable event that may or may not occur in the future. However, good deals are those that cover the types of adverse events that have happened before and might reasonably be expected to occur again. Understanding the risks and the solutions in advance lets everyone sleep easier at night. And offers the best prospect for a win-win that withstands the test of time.

California's Central Valley: Guideposts to Economic & Community Vitality

This blog installment provides a comparative analysis of eight strongly agricultural communities in California's Central Valley. The purpose has been to assess successes and challenges - from the perspectives of community and economic vitality. There are differences. What can we learn from their experiences?

Note: This blog post was made July 25, 2018. This initial 2018 post has since been updated as of August 18, 2020 and may be viewed by clicking the link: Economic Vitality Update

The Communities

As shown by the map below, eight cites and their respective counties in the San Joaquin or Central Valley were chosen for analysis. Starting northwest and heading southeast, the communities are:

• Woodland, Yolo County (population 59,000)

• Stockton, San Joaquin County (310,000)

• Modesto, Stanislaus County (210,000)

• Merced, Merced County (83,000)

• Fresno, Fresno County (526,000)

• Hanford, Kings County (55,000)

• Visalia, Tulare County (133,000)

• Bakersfield, Kern County (378,000)

Central Valley cities range in population from 55,000 (Hanford) to over ½ million (Fresno). County-wide populations go from 150,000 (Kings County) to nearly 1 million (Kern County). Note: Population and other data sets are as of 2017.

Across all 8 cities, about 40-55% of the population is Hispanic/Latino. Latino incomes equate to 84% of the incomes for all households in these communities.

As of 2017, median household incomes of these Central Valley communities ranged from less than $43,000 per year (in Fresno) to over $59,000 (Bakersfield). By comparison, the median household income for the entire state of California was substantially higher at over $66,000 per year. 

Depending on the community, 43-62% of households own their own home. The highest reported home values are in Bakersfield, the lowest in Merced.

Indicators of Community & Economic Vitality

After an initial review of potential data metrics, the following 11 factors were applied as indicators of community and economic vitality:

• City population growth rate – with higher growth rates as a key indicator of local vitality

• County population growth rate – reflecting regional as well as local community growth

• Median age of population – with younger cohorts offering added workforce opportunities for large and small employers

• % of adults with bachelor’s degree or better – higher education vital for workforce skills

• % of Latino adults with bachelor’s degree or better – for better workforce opportunity

• Median household income – a key indicator of household purchasing power

• Hispanic % of all household median income – the lower the gap, the better

• % of families below poverty level – with less poverty evidence of more economic health

• % white collar jobs – as these jobs tend to pay more and offer greater career mobility

• % of homes owner occupied – with home ownership important for community participation and building resident equity

• Median home values – with higher value homes indicative of higher incomes (but also raising potential issues of housing affordability)

Scoring Community Performance

For this analysis, communities were classified as high performing if they met 8 or more of the 11 factors considered. Other communities are noted as mid-tier and lower performing. 

High Performing Communities

Communities scoring the highest for the indicator criteria selected were: 

• Bakersfield – above average on 10 of 11 factors – all but Hispanic incomes relative to incomes for all households.

• Visalia – scored above average on 9 factors – all but median age and home valuation.

• Woodland – above average on 8 factors – all but city growth rate, median age of residents, and proportion of Latino adults with a bachelor’s degree or better.

Mid-Tier & Lower Performing Communities

Characteristics of cities that rated lower with this scoring matrix are briefly summarized as follows: 

• Fresno – as the largest city in the San Joaquin Valley scored above average on only 4 of 11 factors – young median age, proportions of total and Latino population with bachelor’s degree or better, and % of workers with white collar jobs.

• Stockton – scoring lowest with only 2 factors indicated as above average – county-wide population growth rate and Hispanic incomes as a % of all household median income.

• Modesto – noted as above average for 6 of 11 factors – Latino college graduation rates and incomes as a % of all household median income, low proportion of households below poverty level, white collar jobs, proportion of homes owner occupied and with higher home values.

• Merced – above average on four factors – city and county population growth rates, low median age of residents, and Hispanic incomes as a % of all household median income.

• Hanford – above average on 5 factors – city population growth, low median age, Latino bachelor’s degrees or better, median household income, and owner-occupied housing.

As the largest and third largest cities in the Central Valley, respectively, Fresno and Stockton demonstrate that historically agricultural cities do not necessarily grow their way into greater economic vitality and opportunity. While relatively large, both communities have experienced sub-par population growth since 2000, have relatively low median household incomes, high proportions of families below poverty level, low homeownership rates and home values.

Observations for the Three High Performers

A more detailed assessment was made for the three high performing communities of Bakersfield, Visalia and Woodland -- involving qualitative as well as quantitative factors. So, what findings can be drawn from a review of the three high performers? 

Five overall observations are noted:

1.   Cities that have prospered economically have done so by extending from and diversifying beyond their initial agriculture strengths – albeit in varied ways. Bakersfield has diversified by taking advantage of opportunities in energy production, diversified manufacturing, federal installations, construction jobs and proximity to the Los Angeles market. While more isolated, Visalia has become the retail, business service and governmental center for the vast agricultural region of Tulare County as well as for non-ag basic industrial firms. In contrast, Woodland has diversified beyond agriculture by taking advantage of proximity to two major interstates (I-5/I-80), the state capitol (Sacramento) and university (UC-Davis).

2.   Where available, economically successful communities have taken advantage of their proximity to larger urban centers and/or regional transportation networks. Of the three case study cities, this is particularly the case for Bakersfield and Woodland – facilitating their rise as major distribution centers serving the LA, San Francisco, Central Valley and broader west coast markets. These two cities also are proximate to the two major airports of the Central Valley and are receiving spillover residential growth from nearby major metro markets.  As a community more removed from major metro areas, Visalia focuses on internal connections to maintain a role as central hub within its vast agricultural hinterland.

3.   Effective, adaptive local governance, infrastructure and cultural amenities should be considered as elements integral to community and economic vitality. All three of the profiled cities have active heritage programs and a local arts/cultural scene with a downtown area focus. Two have minor league baseball teams. Visalia is transitioning from at-large to district-based City Council positions. To more proactively address statewide drought issues, the Water Association of Kern County is embarking on a public-private partnership effort to educate the general public – including extensive use of social media.

4.   Improving adult educational attainment in these traditionally ag-based communities represents a continuing challenge, particularly for the local Hispanic population. Latino education levels continue to lag well behind attainment for the full population of all the California Central Valley comparables considered. This suggests that education alone is not necessarily the best or only community development strategy. Rather, the critical need appears to be to better tie education services from K-12 to college to real-time job opportunities and needs of the local community. 

5.   Similarly, narrowing the income gap between the Anglo and Latino population comes across as a factor relevant to continued economic momentum and prosperity.  For the California high achievers, the common thread appears to be white collar jobs – offering higher pay and opportunities for career advancement. 

Note: This blog post was made July 25, 2018. An update is now available as of August 18, 2020. This updated analysis may be viewed by clicking the link: Economic Vitality Update

RETAIL PERFORMANCE - THE PORTLAND LOOK

Sometimes, the best of intentions can go awry. On behalf of a private sector initiated Retail Task Force (RTF), E. D. Hovee & Company, LLC has recently evaluated sub-city shopping patterns in Portland, Oregon. With this analysis, the RTF submitted testimony to City Council members expressing concern that current proposals for commercial areas of the city could render "access to affordable goods and services, including healthy food, more difficult.” 

To understand the rationale for these concerns, in-depth evaluation of current patterns of retail sales and commercial real estate rental space city-wide. Principal findings are that: 

• Portland is already under-retailed – especially lacking in meeting the day-to-day needs of city residents as for grocery retail.

• Disparities are greatest for areas removed from the city core – to the east where commercial space rents are lowest and least adequate to support high costs of new development and to the west where viable retail sites are limited due to topography and proximity to retail in the neighboring suburban jurisdiction of Beaverton.

• City of Portland consideration of standards that may be workable in higher density areas with good transit service risk even greater shortfalls in retail availability for residents who already have the poorest access to quality, healthy and affordable retail services.

Retail Sales & Leakage

Retail sales leakage occurs when sales volumes of retail stores are below what residential demand would support - meaning that in-city residents are traveling outside the city to shop. For this city of over 613,000 residents, an evaluation of 2015 retail sales patterns using nationally recognized Nielsen data showed that: 

• City-wide retail demand and supply are roughly in balance - with relatively nominal levels of overall sales leakage experienced but with substantial sales leakage for grocery stores as anchors for vital neighborhood business districts.

• Portland's vibrant Central City is experiencing retail sales levels nearly double what the purchasing power of Central City residents alone would support - not surprising given strong attraction of the downtown and Pearl districts to suburban residents and tourists.

• Other Portland geographies are substantially under-retailed. Even trendy Inner Area neighborhood districts experience net sales leakage at about 38% of resident demand, including grocery leakage at 15%.

• Socio-economically challenged Eastern areas (east of about the I-205 beltway) are indicated as having sales leakage approaching 50% of resident demand - with limited grocery options offset by extraordinarily high rates of purchases at less healthy convenience stores.

• The topographically challenged Western area with its hilly terrain experiences even higher rates of leakage, with many residents traveling by auto over the hill to the neighboring jurisdiction of Beaverton.

A surprisingly high 18% of all in-city retail sales occurs at the northern edge of the city near industrial and airport areas - especially for large format stores that can not find appropriate sites closer to residential neighborhoods. 

City Action

In May 2016, the Portland City Council approved amendments as proposed jointly by RTF and planning staff to the updated Comprehensive Plan that better address the widest range of retail needs across the city with policies aimed to:

• Provide for a competitive supply of retail sites that support the wide range of consumer needs for convenience, affordability, and diversity of goods and services, especially in under-served areas of Portland.

• Provide adequate land supply to accommodate a full spectrum of grocery stores catering to all socioeconomic groups and providing groceries at all levels of affordability.

• Consider short-term market conditions and how area development patterns will transition over time when creating new development regulations.

A final City Council vote on the Comprehensive Plan is scheduled for June 2016. As a related implementation activity, the City's Planning and Sustainability Commission is receiving testimony through July 12, 2016 on a Mixed Use Zones Project and zoning code amendments affecting current commercially zoned districts of the City - after which a public hearing will be held on a proposed composite zoning map. 

Pacific Northwest Economic Resurgence

In our last blog (of March 16), the focus was on changes in U.S. employment from when job counts during the Great Recession bottomed out in 2010 to the most recent Q3 2015 data on nationwide jobs and payrolls. With this second phase analysis, the scope narrows to the Pacific Northwest (PNW) states of Washington, Oregon and Idaho. 

Despite severe job losses thru the heart of the recession, the PNW has rebounded smartly in the last 4-5 years since 2010. Employment in the 3-state region has increased by 13%, well above the 9% job increase nationwide. Currently the PNW now has a based of 5.7 million jobs (covered by unemployment insurance), representing about 4% of the 140 million workers across the U.S. 

As with the U.S. analysis, these changes are depicted by the graph below which shows: 

• Current PNW job base (measured in relative terms by the size of the circles in the graph)

• Change in # of jobs in the last 4-5 years (horizontal axis)

• Change in annual wages by job sector (vertical axis)

In several respects, the PNW graph closely mirrors national experience (see graph with earlier blog below). The same sectors that dominate the national landscape also tend to represent the greatest concentrations of employment in the Pacific Northwest. The #1 sector for net job growth nationally and regionally post-recession has been with professional and business services. While a large sector, government represents the only sector of the economy for which net job loss is indicated post-2010. And overall, wages have increased at about the same pace averaging 1.7% per year across both the U.S. and PNW. 

However, clear differences are of note as well:

• While manufacturing employment has picked up across the U.S., the rate of PNW job growth has been twice that of the nation. Similar outsized performance is noted for the smaller job sectors of natural resources (including agriculture) and information.

• Average size of PNW firms is increasing more than three times as fast as for the U.S. (not directly shown by the graph).

• And, most astoundingly, the information sector (ranging from publishing to software) is associated with a nearly $77,000 per year average increase in compensation per year - well above the still remarkable $18,000 per year gain experienced over the last 4-5 years nationwide. State and federal employment data indicates that PNW payroll per information now averages almost $172,000 per year - largely driven by outsize compensation for high performers, particularly in the Seattle metro area.

Looking ahead, obvious questions relate to sustainability of information sector payrolls as well as to continued resurgence of manufacturing and national resources employment (benefiting rural and smaller metro areas of the PNW recently). Other sectors of importance to continued balanced regional job growth will be construction, professional and business services, education and health, leisure and hospitality, and trade transportation and utilities (TTU) - together with stabilizing government sector employment. 

Post-Recession Winners & Losers - Nationwide

Early Returns Are In ...

With the Great Recession officially ended in 2009, U. S. employment actually bottomed out in 2010 at less than 198 million jobs. by the 3rd quarter of 2015, employment covered by unemployment insurance had worked its way up to nearly 140 million jobs, a 9% gain of 11-12 million jobs nationwide. 

In this first blog, I look at the mix of the changing U.S. job base. In future blogs, the focus will shift to economic and development trends in varied regions of the U.S. - starting with my backyard of the Pacific Northwest. 

As depicted by the graph below, this review of national experience since 2010 focuses on: 

• Current job base (shown by circles)

• Change in # of jobs in the last 4-5 years (horizontal axis)

• Change in annual wages by job sector (vertical axis)

This overview offers some mixed and perhaps surprising messages about the rapidly changing mix of the U.S. job base.

A few observations to help put all this in perspective:

• Of nearly 140 million jobs in the U.S. the single largest sector is trade, transportation and utilities (TTU) - with 27 million jobs in a rather unwieldy sector including both retail and wholesale trade as well as transportation and utilities.

• Next largest are education/health services, government, and professional/business services - at about 20 million jobs each.

• In the last 4-5 years professional/business services have added the most jobs - up by about 3 million (for 26% of all net job growth nationally). Other major job producers are in TTU, education/health, and leisure/hospitality - each up by 2+ million.

• Government employment is actually down by nearly 1.5 million, an apparent reflection of the end of stimulus combined with state/local layoffs in (delayed) response to recession related revenue declines.

• With 12 million employees, domestic manufacturing is ever more streamlined. However, in a major reversal from decades of declining employment, U.S. manufacturers added nearly 900,000 jobs in the last 4-5 years.

• Average firm size is generally on the increase - except in the sectors of information, education/health and government.

• The star performer in terms of wage gains is information. The total jobs count is stagnant, but a dramatic shift is taking place as the sector transitions from printing and publishing to software and internet related applications. Average wage increased by over $18,000 per year - a reflection of this shift and the strong market demand for techies.

• For other sectors, wage gains since 2010 have been more modest, up by between $1,000 (in financial services) to $6,800 per year (with construction rebound) as of Q3 2015. Across all sectors combined, wages have increased by less than 2% per year.

Looking ahead, items of importance to monitor include translation of job growth to also support stronger wage growth, opportunities for high demand sectors like information to start adding net new jobs as the transition from older sub-sectors like printing runs its course, sustainability of domestic manufacturing competitiveness (amid a stronger dollar), and potential government job rebound.