Our first set of results concern the effects of monetary policy on disposable income. We show that softer monetary policy increases disposable income at all income levels, but that the gains are highly heterogeneous and monotonically increasing in the income level. As shown in Figure 1, a decrease in the policy rate of one percentage point raises disposable income by less than 0.5% at the bottom of the income distribution, by around 1.5% at the median income level, and by more than 5% for the top 1% over a two-year horizon.
Author(s): Asger Lau Andersen, Niels Johannesen, Mia Jørgensen, José-Luis Peydró
The SALT cap increased “taxes on hardworking families,” says the letter. That’s “untenable given the dire economic conditions caused by the pandemic.” It goes on to say, “In short, middle-class Americans are struggling under this federal tax burden, while corporations – which are still able to fully deduct SALT as business expenses – are profiting because of the same law. The negative impacts of the SALT cap on middle class families are particularly egregious when you consider that in the states most affected by this cap, the federal government already takes more in federal taxes than the states receive in federal support, effectively subsidizing federal payments to other states.”
Tax analysts on the right and the left have documented why that’s completely false. The cap on SALT deductions was a windfall for the middle class and hammered high income taxpayers. The conservative Tax Foundation explained why here, and the liberal Institute on Taxation and Economic Policy, ITEP, wrote this in an article opposing elimination of the cap:
ITEP estimated that this would cost more than $90 billion in a single year. We found that 62 percent of the benefits would go to the richest 1 percent and 86 percent would go to the richest 5 percent. There is no state where this is a primarily middle-class issue. In every state and the District of Columbia, more than half of the benefits would go to the richest 5 percent of taxpayers. In all but six states, more than half of the benefits would go to the richest 1 percent.
Given these advances in understanding the theoretical methods of evaluating multiple, related mortality data sets, it is particularly promising that the Human Mortality Database, with the SOA’s sponsorship, has recently made available mortality data for the United States at the level of the individual county. Moreover, Professor Magali Barbieri of University of California, Berkeley in January 2021 published an SOA Research Report on “Mortality by Socio-economic Category in the United States” using this data series. Professor Barbieri is one of the directors of the HMD project, which is jointly run by UC Berkeley and the Max Planck Institute for Demographic Research in Rostock, Germany and support from the Center on the Economics and Development of Aging (CEDA) and the French Institute for Demographic Studies (INED). In her paper, Barbieri studies socio-economic differences linked to mortality differentials by county, based on information available at the county level regarding education, occupation, employment, income, and housing. The gap between the highest and lowest county decile is huge and growing. In 2018, the qx-rate for 45-year-old men in counties with the lowest Socioeconomic Index Score (SIS) was 2.5 times that for men of that age in counties with the highest SIS. This gap is even greater than the difference between smokers and non-smokers. Professor Barbieri’s report shows the widening trend between the different socio-economic strata which she captures by grouping the counties into deciles by SIS. While the highest SIS score is associated with a life expectancy that matches or even beats the OECD average, people living in counties with the lowest SIS have hardly seen any improvement in their life expectancy over the last four decades. Comparing the average life expectancy at birth within the highest decile of counties to the lowest, there was a gap of 3.0 years in 1982, the first year for which consistent data was available. This gap has more than doubled since then, rolling in at 6.6 years difference in life expectancy in 2018. That is an increase of 120 percent. Worse still, the gender gap once again manifests itself in the mortality trends, with females showing an increase of the socio-economic mortality gap of 260 percent over the 36-year period, compared to 76 percent for males.
Author(s): Kai Kaufhold
Publication Date: March 2021
Publication Site: Reinsurance News at the Society of Actuaries
Those who substituted some or all of their typical in-person work for telework tended to have higher household incomes than those who did not switch to telework.
In the highest-earning households — those with annual incomes of $200,000 or more — 73.1% switched to telework (Figure 1). This is more than double the percentage (32.1%) of households with incomes between $50,000 and $74,999, a range that includes the 2019 median U.S. household income ($65,712).
Lowest-earning households were less likely to switch to telework. Only 12.7% of households earning under $25,000 reported teleworking in lieu of in-person work.
Author(s): JOEY MARSHALL, CHARLYNN BURD, MICHAEL BURROWS
In case you are thinking, “Well, the rich make more, they should pay more,” the top 1 percent of taxpayers account for 20 percent of all income (AGI). So, their 40 percent share of income taxes is twice their share of the nation’s income.
Similarly, in 2018, the top 0.1 percent of taxpayers paid $311 billion in income taxes. That amounted to 20 percent of all income taxes paid, the highest level since 2001, as far back as the IRS data allows us to measure. The top 0.1 percent of taxpayers in 2018 paid a greater share of the income tax burden than the bottom 75 percent of taxpayers combined.
The 2020 life expectancy numbers also underscore longer-term health challenges that were already alarming. For two to three decades, life expectancy has been improving much more rapidly for higher earners than for lower earners, and 2020 has probably made these gaps worse. The one bright spot in the differential trends before the pandemic had been a narrowing of racial differences. These new estimates show a dramatic reversal of that hopeful pattern. From the early 1990s to 2016, the racial gap in life expectancy for males at birth shrunk from more than 8 years to about 4.5. During the first half of 2020, it widened to more than 7 years.
On the contrary, 2020 mortality data indicate that death rates from non-Covid causes rose, despite the economic recession. More Americans than expected died from diabetes, high blood pressure and pneumonia. Some of these deaths may have been misreported, and actually caused by Covid. But a large number may also reflect the challenges in providing non-Covid health care during the past year, as people have avoided hospitals, and government mandates have restricted discretionary medical procedures. The pandemic will provide hard lessons on which types of medical care truly improve health, and which ones can be safely skipped or delayed.
This is the racial wealth gap: the stark wealth difference between white and Black families in the United States. There are several ways to measure this gap, but in 2016 the median wealth for white households was $149,903, while Black households had $13,024.
There’s a myth in the United States that the racial wealth gap has somehow improved over time. This study shows that: many Americans falsely believe that the gap has improved linearly over time, when in reality, it has barely changed and has even gotten worse in some places in the United States.
Granted, other forms of racial injustive have improved since the 60’s. Black representation in politics, media, and academics have improved. Discrimination based on race in the workplace, schools, and in social life have improved. But the racial wealth gap has not improved.
Illinois households earning less than $40,000 were four-times as likely to lose their jobs from February-April 2020 and nearly 11 times as likely to still be out of work compared to those earning $75,000 or more.
As Illinois tries to rebuild after the worst year for jobs in state history, low-income Illinoisans find themselves even farther behind than others. Job losses suffered during COVID-19 and state-mandated mitigation protocols disproportionately fell on these families.
This paper investigates mortality inequality across U.S. states by modelling and forecasting mortality rates via a forecast reconciliation approach. Understanding the heterogeneity in state-level mortality experience is of fundamental importance, as it can assist decision-making for policy makers, health authorities, as well as local communities who are seeking to reduce inequalities and disparities in life expectancy. A key challenge of multi-population mortality modeling is high dimensionality, and the resulting complex dependence structures across sub-populations. Moreover, when projecting future mortality rates, it is important to ensure that the state-level forecasts are coherent with the national-level forecasts. We address these issues by first obtaining independent state-level forecasts based on classical stochastic mortality models, and then incorporating the dependence structure in the forecast reconciliation process. Both traditional bottom-up reconciliation and the cutting-edge trace minimization reconciliation methods are considered. Based on the U.S. total mortality data for the period 1969–2017, we project the 10-year-ahead mortality rates at both national-level and state-level up to 2027. We find that the geographical inequality in the longevity levels is likely to continue in the future, and the mortality improvement rates will tend to slow down in the coming decades.