Category Archives: politics

Three Months of the COVID-19 Pandemic In The U.S.

Here is the latest update to the United States' full tower chart during the three months from 10 March 2020 through 9 June 2020, along with the nation's daily test positivity rate (the percentage of positive test results among all tests reported), and also the nation's rolling 7-day average of newly confirmed cases and deaths per day. Click on the image below to access a much larger version of the three charts together:

Progression of COVID-19 in United States, Daily Test Positivity Rate, 7-Day Total Newly Confirmed Cases and Deaths per Day, 10 March 2020 through 9 June 2020

Overall, the nation is on an improving trajectory, but one that's improving more slowly than might be expected after so much of the country was put under lockdown orders by state and local government officials. That difference between intent and result suggests that a lot of what has transpired in public policy over the last few months at the state level has been ineffective.

As a case in point, let's reflect on how things looked back in early March 2020. That was back when the state of New York only had 173 known cases of SARS-CoV-2 coronavirus infections but no recorded deaths, and the state of Washington was leading the nation with 1,178 COVID-19 cases and 37 deaths.

Three months later, thanks largely to its dysfunctional political leadership, New York would far surpass all other states and claim the top spot for the number of confirmed coronavirus cases and deaths in the United States, with over 379,000 confirmed cases and at least 24,348 deaths, or just under one-fifth of all cases and nearly a quarter of all deaths so far attributed to the viral infection in the U.S.

Much of the spread of the coronavirus pandemic within the U.S. has been traced to cases emanating from New York City, the epicenter of the epidemic in the nation. That's how New Jersey became the second most hard hit state in the U.S., followed by Massachusetts, Rhode Island, Connecticut and Delaware to round out the list of other states in the northeastern part of the U.S. that have experienced the most cases in the country.

But the state of New York didn't just spread infections around the country. It also spread deadly policies, particularly after the state's leaders panicked when considering the predictions of computer models and began forcing nursing homes and assisted living facilities to admit patients known to have coronavirus infections, where the infections would "spread like wildfire" and greatly amplify the number of cases and deaths among the state's elderly population, already known to be the most vulnerable to the coronavirus. States that copied New York's panicked policies, like New Jersey, have paid a similar and unnecessarily high price in lives.

In the following skyline tower chart, you can actually see the progression of COVID-19 in each state and territory of the United States during the past three months. Fortunately, the states that have been the hardest hit are now showing elevated but greatly slowing rates of spread, but others, which had largely evaded significant numbers of infections in the last three months, such as Arizona, Utah, and Oregon, are now seeing rising numbers of cases.

Progression of COVID-19 in the United States by State or Territory, 10 March 2020 through 9 June 2020

Each of these charts span the same period of time and the width of each corresponds to 2.0% of each state or territory's population, making it very easy to see which states and territories have been most impacted and which have been the least impacted through the first three months of the coronavirus pandemic in the U.S., especially since we've ranked them from the highest percentage of infection within the state's population to the least as you read from left-to-right, top-to-bottom.

One limitation we recognize in the skyline tower presentation is that it is difficult to get a sense of how the number of cases and deaths in each state has evolved over time and how that directly compares to other states. The following interactive chart shows the rolling 7-day average number of newly confirmed coronavirus cases for each of the 50 states and the District of Columbia from 17 March 2020 through 9 June 2020, where if you hover over a particular curve, you can highlight it and get the number of cases per 100,000 residents at individual points of time.

The next interactive chart presents the rolling 7-day average number of deaths per 100,000 state residents, where you can find which states have performed the best and the worst over the last three months, which is another way of identifying which states followed New York's bad examples and which did not.

We're also pleased that others have developed dashboards for exploring the COVID Tracking Project's time series data that we've used throughout our series of articles on the progression of COVID-19 in the U.S. In particular, the COVID Time Series dashboard makes it easy to get detailed day-by-day data for individual states and territories for a variety of measures that the COVID Tracking Project has been following. Since we're moving away from regular updates, these are good sites to go to if you need to get your coronavirus data fix!

Previously on Political Calculations

While this is the last planned article for this particular series, here are all of the articles featuring the data visualization we've developed to track the spread and severity of the coronavirus epidemic at the state level, which we've listed in reverse chronological order, starting with this very article!

Meanwhile, if you prefer your data in the form of tables presenting numbers and percentages, we also have you covered!

We have been updating the tables in these last two articles on a near-daily basis, but we'll be updating them less frequently going forward, mainly because the incidence of new cases in the U.S. is slowing, but also because the international rankings are badly hampered by inconsistent reporting standards and, for several countries with authoritarian regimes, outright dishonest reporting of their total number of cases and deaths.

What Happened In New Jersey’s Nursing Homes?

New York has been, far and away, the worst state to be in for the coronavirus epidemic in the United States. Especially for elderly Americans with illnesses that require they live in nursing homes or long term care facilities, where one single poorly-considered policy implemented after the state's governor and public health officials began to panic when the going got tough has needlessly cost thousands of lives.

New Jersey has been the second most-impacted state or territory in the U.S. thanks to its proximity to New York City, which has been the nation's epicenter for the coronavirus epidemic. In fact, you can tell which counties of New Jersey can be considered to be part of New York City's greater metropolitan area just from a map indicating the number of confirmed COVID-19 cases have been recorded in each.

That close proximity to New York means that New Jersey has shared a very similar experience in dealing with the SARS-CoV-2 coronavirus. The following charts show the daily progression of the epidemic in New Jersey, the amount and results of medical testing in the state, and also the rolling 7-day totals for newly confirmed cases and deaths, all of which have generally tracked along with New York, although in the third, rightmost chart, you can see New Jersey was more successful in flattening its curve compared with how the spread of the coronavirus played out in New York.

New Jersey: Daily Progression of COVID-19, Daily Test Positivity Rate, and Rolling 7-Day Totals for Newly Confirmed Cases and Deaths, 10 March 2020 through 20 May 2020

While New Jersey's situation has improved significantly from what it was just three weeks ago, it too has seen an outsized number of deaths occurring in the state's nursing homes and long term care facilities, with numbers similar to New York. Unfortunately, the reason that is the case is the same: New Jersey Governor Phil Murphy and the state's public health officials copied what Governor Cuomo did in New York in mandating the state's nursing homes and long term care facilities admit patients known to be infected with the SARS-CoV-2 coronavirus, which ran rampant through the nursing homes, needlessly contributing to the premature deaths of thousands of New Jersey's most vulnerable residents. The following chart shows the results of that policy has been since it was adopted on 31 March 2020:

The question is why did New Jersey Governor Phil Murphy copy such a misguided policy? When the state of New York's Department of Health issued its infamous directive on 25 March 2020, the policy was slammed in the pages of the Wall Street Journal the next day. Less noticed however was a press release issued by the Committee to Reduce Infection Deaths on the same day, in which the anti-hospital infection public interest advocacy group also slammed New York's policy, but which cited an example from New Jersey for how the state should work to prevent the spread of coronavirus infections within the state's nursing homes.

Dear RID Friends and Healthcare Providers:

The State of New York is adopting a dangerous new policy requiring nursing homes to blindly admit patients infected with Covid-19, according to a new report in The Wall Street Journal.

Cuomo's edict, if reported correctly, dooms thousands of elderly to illness and likely death. Basic infection control says to identify and contain. Cuomo's edict does the opposite: conceal and spread. It spreads the infections to nursing homes and forcing homes to operate in the blind, not even knowing which incoming patients are coronavirus carriers.

A model of what should be done is how CareOne, an exemplary facility in New Jersey, knowingly emptied one of its locations to protect other uninfected residents and then welcomed coronavirus patients from St. Josephs to that facility.

You can find out more about the model New Jersey was setting here. Clearly, something dramatic changed, because just six days later, the Murphy administration issued a directive with the following instruction mandating that nursing homes and long term care facilities in New Jersey to admit patients with contagious coronavirus infections (the underlining is contained in the original document):

No patient/resident shall be denied re-admission or admission to the post-acute care setting solely based on a confirmed diagnosis of COVID-19 … Post-acute care facilities are prohibited from requiring a hospitalized patient/resident who is determined medically stable to be tested for COVID-19 prior to admission or readmission.

What changed? We've pieced together the story as best we can from contemporary reports, where the first indication that Governor Murphy would soon follow Governor Cuomo's bad example came on 28 March 2020, as a number of hospitals in northern New Jersey began to divert, or to not accept, new patients for 4-hour blocks of time because they were either at or near their full capacity.

The timing of these events coincides with the worst case projections the influential Institute for Health Metrics and Evaluation (IHME) has issued for New Jersey, which as in New York, was used by state policymakers to make decisions. In this case, we think those early diversions prompted New Jersey's leaders into thinking they were facing a worst-case scenario. The following chart shows what the IHME's projection for the number of hospital beds above New Jersey's available capacity looked like on 30 March 2020, just before Governor Murphy's administration implemented its policy that would allow hospitals to move as many asymptomatic or partially recovered coronavirus patients as they could to other medical facilities, which in the case of elderly patients, would mean moving them to nursing homes and long term care facilities throughout the state.

So the motive for Governor Murphy's administration to adopt the same policy that Governor Cuomo had less than a week earlier is the same. For Governor Murphy however, the lack of condemnation or criticism for Governor Cuomo's disastrous move other than in the Wall Street Journal cleared a path to act with impunity, where he could safely assume his own deadly action would not be challenged by the nation's media.

To understand what happened, let's take a moment to review the chart showing the 7-day rolling total for new coronavirus cases in New Jersey. On 31 March 2020, the number of confirmed cases was rocketing upward, which would go on to peak at 25,437 new cases per week on 7 April 2020. After that point, the state's curve flattened out under that level for the next three weeks, before the incidence of newly confirmed cases began to fall rapidly after that point.

Within that period, the number of COVID-19 hospitalizations peaked on 14 April 2020, just within the state's available capacity to accommodate these patients at its hospitals, thanks in large part to efforts they had made to expand their capacity in the preceding weeks. In the following chart, we've pieced together the number of New Jersey's long term care facilities that reported housing coronavirus patients at various points throughout the state's coronavirus epidemic from various reports, along with the figures the state's Department of Health began reporting for these facilities on 20 April 2020.

Before Governor Murphy's directive forcing nursing homes to admit infected coronavirus patients on 31 May 2020, 73 long term care facilities in the state reported having infected patients. On 8 April 2020, that figure had risen to 123. Twelve days later, with the number of new coronavirus infections throughout the state reaching and holding near its peak, the state's Department of Health began reporting the number of cases within the state's nursing homes, where that number had skyrocketed to 425 facilities.

That figure has continued to grow, where though 20 May 2020, the New Jersey Department of Health reports a cumulative 529 of the state's nursing homes long term care facilities have housed coronavirus-infected patients.

That outcome did not happen by accident. Nursing homes and long term care facilities are geographically dispersed, where they can be thought of as relatively isolated islands, where an outbreak of infections at one would not travel to others on its own. It took a deliberate policy by the governor and the state's public health officials to make that happen. And once it did, with recently transferred contagious patients exposing nursing home staff members to the infection, who in turn spread it to previously uninfected, but especially vulnerable nursing home residents with fatal effect.

The spread of coronavirus infections within New Jersey nursing homes and long term care facilities resulting from the Murphy administration's policy can be seen in the following chart, where we find from the available data that the number of infections at these facilities begins to take off after 31 March 2020, where by 20 May 2020, New Jersey's nursing homes account for 19% of all confirmed coronavirus cases in the state:

Cumulative COVID-19 Confirmed Cases in New Jersey, Total Cases in State and in Long Term Care Facilities, 1 March 2020 - 20 May 2020

The deadly impact of the Murphy administration's policy can be seen in the next chart, where we find that New Jersey's nursing home residents account for over 51% of the deaths attributed to the COVID-19 coronavirus in New Jersey.

Cumulative COVID-19 Deaths in New Jersey, Total Deaths in State and in Long Term Care Facilities, 1 March 2020 through 20 May 2020

Were any of these nursing home deaths inevitable? It would be fair to assume that whatever portion of coronavirus infections that had made it into the 73 nursing homes that had them before Governor Murphy's policy went into effect on 31 March 2020 would be deadly, but only within those facilities. The Murphy administration's policy expanded the viral risk to hundreds of more facilities, where these COVID-19 attributed deaths could wholly have been avoided. If only Governor Murphy hadn't been a copycat of Governor Cuomo.


2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE. CSSE COVID-19 Time Series Data: Confirmed U.S. [CSV File]. Last updated 20 May 2020. Accessed 20 May 2020.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE. CSSE COVID-19 Time Series Data: Deaths U.S. [CSV File]. Last updated 20 May 2020. Accessed 20 May 2020.

New Jersey Department of Health. NJ Long Term Care Facilities with COVID-19 Outbreaks. [PDF Document]. Posted: 20-Apr-2020, 22-Apr-2020, 24-Apr-2020, 27-Apr-2020, 29-Apr-2020, 1-May-2020, 4-May-2020, 6-May-2020, 8-May-2020, 11-May-2020, 13-May-2020, 15-May-2020, 18-May-2020, 20-May-2020.

Arco, Matt. Number of coronavirus patients at N.J. hospitals drops to 3-week low with 5th straight day of declines. [Online Article]. 26 April 2020.

Associated Press. New Jersey reports 9 coronavirus deaths; elections postponed. Dayton 24/7 Now. [Online Article]. 19 March 2020.

Associated Press. NJ COVID-19 deaths climb by 17 to 44 in biggest jump yet. [Online Article]. 24 March 2020.

Associated Press. Coronavirus in 43 NJ nursing homes — death toll now at 81. New Jersey 101.5. [Online Article]. 27 March 2020.

Associated Press. 8 nursing home residents die of COVID-19, N.J. mayor says. Philadelphia Tribune. [Online Article]. 30 March 2020.

Broadt, Lisa. 3 deaths, 10 coronavirus cases ID’d at Mount Laurel nursing home. Burlington County Times. [Online Article]. 25 March 2020.

Institute for Health Metrics and Evaluation (IHME). COVID-19 Estimate Downloads. IHME. [Zip File]. 30 March 2020.

Washburn, Lindy. An unseen crisis: Coronavirus deaths mount at NJ nursing homes as virus spreads, staff dwindles. [Online Article]. 8 April 2020.

Signs Of Slowing COVID-19 Spread Among U.S. States

The fifth week of the coronavirus pandemic within the United States has seen signs that the spread of the viral infection is slowing within several states that have previous reported a high percentage of cases, while other states and territories are seeing very slow growth in their number of confirmed cases.

Here's what the daily progression of cumulative COVID-19 testing, confirmations, hospitalizations, and deaths look like when tracked at the national level in a tower chart over the four weeks from 10 March 2020 through 14 April 2020.

Daily Progression of COVID-19 in the United States, 10 March 2020 through 14 April 2020

The national tower chart of the daily progression of COVID-19 in the U.S. shows a greater level of detail than we've previously presented, where we can now break out COVID-19 patients who have been discharged from hospitals, which reveals the number of hospitalizations for serious cases has begun to decline. At the same time, the overall number of less serious cases evidenced by the number who have tested positive continues to rise at a slower pace, while the number who have tested negative is growing rapidly with the increased volume of testing.

The number of deaths attributed to COVID-19 infections has also increased, although there are quality issues affecting how these deaths are being recorded.

The spread of the COVID-19 has been particularly concentrated within several regions of the United States, which becomes very evident when we present the daily progression of COVID-19 over the first five weeks for which we have numbers for confirmed cases, hospitalizations, discharges, and deaths at the state or territory level in the following skyline tower charts, where you can start to see some of the improvements that have begun to take place. Each of these charts span the same period of time and the width of each corresponds to 1.5% of each state or territory's population, making it very easy to see which states and territories have been most impacted and which have been the least impacted through the first five weeks of the coronavirus epidemic in the U.S., especially since we've ranked them from the highest percentage of infection within the state's population to the least as you read from left-to-right, top-to-bottom.

Progression of COVID-19 in the United States by State or Territory, 10 March 2020 through 14 April 2020

For each state or territory shown above, if you add the number of deaths, discharges and hospitalizations listed for 14 April 2020, you'll have the cumulative hospitalizations through that date.

If you look at the top row of the chart, you can see the growth in cases in New York is showing some signs of beginning to slow, which has capture media attention, but not anywhere near as impressively as Louisiana.

Louisiana's chart reveals the trend we want to see develop, where instead of building a pyramid with a widening base of confirmed coronavirus infections, each state's tower chart instead looks like a skyscraper whose sides are both narrow and vertical. The states that achieve the corresponding very slow rate of spread that goes with this pattern will be among the first that may lift any statewide restrictions they may have placed on activities and businesses.

Of all the regions that have been hardest hit within the U.S., the state of New York continues to stand apart as having experienced the greatest incidence of coronavirus infections. Here's a more detailed look at its coronavirus-related data:

Progression of COVID-19 in New York State, 10 March 2020 through 14 April 2020

Within the state, New York City and the surrounding counties that make up its greater metropolitan area have the greatest concentration of confirmed coronavirus infections within the state.

Confirmed COVID-19 Cases in New York State, Top 10 Regions or Counties, 14 April 2020

The region of New York City includes Bronx, Kings, New York, Richmond, and Queens counties. The most affected counties are adjacent to New York City. On the opposite end of the inspection, New York has four counties that through 14 April 2020, have only reported single digit numbers of confirmed coronavirus infections.

Why is New York's situation with the coronavirus so bad? The New York Times points to the poor judgment of government officials, including Governor Andrew Cuomo and particularly New York City Mayor Bill De Blasio. Their grave mistakes greatly amplified the poor judgment and sluggishness of bureaucrats in the Centers for Disease Control and the Food and Drug Administration, who the New York Post reports failed to treat the rapidly developing crisis like a rapidly developing crisis.

The very low concentration of cases in other parts of the state make the statewide restrictions imposed by Governor Cuomo highly questionable in that they are producing relatively little benefit at great cost to those subject to them. For example, the inequality in the geographical incidence of infection has allowed Governor Cuomo to divert medical resources from them to the New York City metropolitan area, in part to compensate for Mayor De Blasio's fecklessness, but with no compensating benefit, in that the residents of these regions of the state are still subject to the governor's statewide stay-at-home orders, while businesses in these regions are still subject to the governor's statewide closure orders.

The longer that situation continues, the greater the abuse of power becomes.

Previously on Political Calculations

Here's a very short guide to some of the data visualization we've featured for tracking the spread of the coronavirus epidemic in the United States:

Meanwhile, if you prefer your data in the form of tables presenting numbers and percentages, we also have you covered!

The Survival Function of Roman Emperors

How long could a Roman emperor expect to survive after taking power?

Sculpture of Roman Emperor Tiberius Claudius Caesar Augustus Germanicus in Vatican City - Source: Unsplash -

That's a challenging question to answer because the majority of Roman emperors met violent ends. However, new research suggests such results weren't as unpredictable or as random as you might first think.

In modern engineering, the concept of reliability describes the probability that an item will still be operational at some point of time in the future.

Usually, reliability is applied to things like electrical circuits and mechanical devices, but Joseph Saleh has applied the concept to politics, and more specifically, to mathematically describe the survival function of Roman emperors.

"It's interesting that a seemingly random process as unconventional and perilous as the violent death of a Roman emperor--over a four-century period and across a vastly changed world--appears to have a systematic structure remarkably well captured by a statistical model widely used in engineering. Although they may appear as random events when taken singularly, these results indicate that there may have been underlying processes governing the length of each rule until death."

The following chart from Saleh's paper shows the mixture Weibull survivor function he was able to map to the available empirical data for how long ancient Rome's emperors lived after they assumed the purple.

Mixture Weibull survivor (reliability) function of Roman emperors, and the nonparametric results, Figure 4, Saleh 2019

Looking at the nonparametric estimation of the average remaining lifespans of Roman emperors, shown as the heavy black line in the chart above, Saleh offers several observations:

  1. Emperors faced a significantly high risk of violent death in the first year of their rule. This risk remained high but progressively dropped over the next 7 years. This is reminiscent of infant mortality in reliability engineering, a phase during which weak components fail early on after they have been put into service, often because of design or manufacturing defects for example. Roman emperors therefore experienced a form of infant mortality;
  2. The reliability or survivor function stabilizes by the 8th year of rule. The emperors could lower their guard a bit if they made it to 8 years...
  3. ... but not for long: the risk of violent death picks up again after 12 years of rule. This suggests that new mechanisms or processes drove another round of murder. This is reminiscent of wear-out period in reliability engineering, a phase during which the failure rate of components, especially mechanical items, increases because of fatigue, corrosion, or wear-out. Roman emperors therefore also experienced wear-out mortality.

We've built the following tool to estimate the likely survival potential of a generic Roman emperor from Saleh's math. If you're accessing this article on a site that republishes our RSS news feed, please click through to our site to access a working version.

Time After Assuming Power
Input Data Values
Elapsed Time after becoming a Roman Emperor [years]

Probability of Survival
Calculated Results Values
Survivor Function (Probability of Lasting "X" Years)

In the tool, we've arbitrarily capped the maximum number of years a Roman emperor might survive to 45 years, which corresponds to the Emperor Augustus' reign, the longest on record.

Saleh's paper also provides a chart showing the hazard function, or failure rate, for Roman emperors, which reveals a unique pattern.

Failure rate of Roman emperors (parametric fit of the time-to-violent-death), Figure 5, Saleh 2019

This pattern is the familiar "bathtub curve" that characterizes how many real world components behave in reliability analysis. Saleh provides an interesting interpretation of how this pattern applies to the lives of Roman emperors:

  1. The decreasing failure rate early on, the signature of infant mortality, reflects as noted previously a prevalence of weak emperors who were incapable at the onset of their rule to handle the demands of their environment and circumstances. The fact that the failure rate was decreasing though suggests a competition between antagonistic processes, on the one hand those that sought to violently eliminate emperors (elimination), and on the other hand those that reflected the emperors learning curve to better protect themselves and perhaps eliminate their opponents (preservation). Examples abound in Roman history of this competition. Up to the first 12 years of one's rule, the preservation processes steadily improved their performance, and the situation can be casually summarized as "whatever didn't kill them [the Roman emperors] made them stronger" or less likely to meet a violent death;
  2. The increasing failure rate after 12 years of rule, the signature of wear-out failures, reflects as noted previously an uptake in failures through degradation with time, fatigue, or increased harshness in their circumstances. A growing mismatch between capabilities and demands under changing (geo-)political circumstances. This can be due to a number of reasons discussed previously. The fact that the failure rate was increasing after this 12-year mark suggests again a competition between the same antagonistic processes noted in (i), and this time the preservation ones were on the losing end of this competition. This result can be causally summarized as "whatever didn't kill them made them weaker" after a 12-year rule.

The existence of the pattern means that the probability of how long a Roman emperor might last is the result of both random chance and deterministic factors, rather than just chance alone, as perhaps best imagined by the "roll of the dice" Julius Caesar figuratively cast before crossing the Rubicon on his way to taking power as Rome's age of emperors began.

Part of what makes Saleh's analysis so intriguing is that the same concept can be applied to other nations, or forms of government, that have developed in the centuries since the fall of the Roman empire. It will be interesting to discover what patterns they might share with the Roman emperors.

Image Credit: unsplash-logoiam_os


Saleh, J.H. Statistical reliability analysis for a most dangerous occupation: Roman emperor. Palgrave Communications 5, 155 (2019). doi: 10.1057/s41599-019-0366-y.

The Zero Deficit Line, Nine Years Later

In 2010, we introduced the Zero Deficit Line, which visualizes the trajectory of U.S. government spending per household with respect to U.S. median household income. The zero deficit line is simply a straight line on that chart, which happens to represent the amount of government spending that the typical U.S. household can actually afford.

Here's what an updated version of that chart looks like, nine years later.

U.S. Total Federal Government Spending per Household vs Median Household Income, 1967-2018

The chart covers the period from 1967 through 2018, where we won't be able to update it to includes 2019's data until September 2020, when the U.S. Census Bureau will publish its estimate of the number of households in the U.S. for 2019.

Back in 2010, we only had data through 2009, which turned out to be the peak year for U.S. government's annual budget deficits when framed in this context. 2010 and 2011's government spending per household remained near 2009's peak value, where it wasn't until 2012 that spending level dropped to become relatively more affordable.

That was largely due to the Budget Control Act of 2011, which reined in excessive government spending. For a while anyway. Government spending per household levels fell until 2014, after which, they've followed a steady upward trajectory through 2018, largely paralleling but well above the zero deficit line.

The U.S. government's fiscal situation is worse than that appears however. In the following chart, we've presented the U.S. government's spending and tax collections per U.S. household with respect to U.S. median household income from 1967 through 2018.

U.S. Government Spending and Tax Collections per Household, 1967-2018

What stands out in this chart is the most recent years, from 2015 through 2018, where a yawning gap opens up between the U.S. government's spending per household and its tax collections per household. While the U.S. government's spending rises steadily, over this period, the U.S. government's tax collections hold steady.

This is largely due to the Bipartisan Budget Act of 2015, which coupled increased spending with tax cuts and was signed into law by President Obama in December 2015. Although the data for 2018 reflects the effects of the Tax Cuts and Jobs Act of 2017, signed into law by President Trump in December of that year, we can see it didn't materially alter the trend established since 2015 under President Obama's administration.

All in all, it doesn't look like the U.S. government is on a sustainable fiscal path.


White House Office of Management and Budget. Historical Tables, Budget of the U.S. Government, Fiscal Year 2020. Table 1.1 - Summary of Receipts, Outlays, and Surpluses or Deficits (-): 1789-2024. [PDF Document]. Issued 11 March 2019. Accessed 11 March 2019.

U.S. Census Bureau. Current Population Survey. Annual Social and Economic Supplement. Historical Income Tables. Table H-5. Race and Hispanic Origin of Householder -- Households by Median and Mean Income. [Excel Spreadsheet]. Issued 10 September 2019. Accessed 19 December 2019.