Boomer Demographics: The Shift Ahead
September 19, 2012
by Doug Short
Following up on my posts yesterday on the Census Bureau's latest data for household incomes (by quintile and age bracket), I've now updated my U.S. population pyramids based on Census Bureau's historical data and estimates.
I've been maintaining a set of these pyramids to give us snapshots at 10-year intervals spanning seven decades. My pyramids differ from the ones available at the Census Bureau website in one key respect. The CB pyramids are based on population numbers by gender for each five-year cohort whereas mine are based on percentages. Why? Because, in the absence of an overpopulation problem, the shifting ratios over time are more important than the actual numbers. By using percentages on the horizontal axis, all pyramids add up to 100%.
The chart above is a snapshot of the U.S. population 30 years ago in 1982. I've highlighted the top of the Boomer cohort, generally defined as those born during the inclusive 19-year period from 1946 to 1964. By selecting 1982 as our start date, the oldest Boomers have completely filled the age 30-34 bar in our chart and occupy the three bars below, with Generation X slipping into the bottom of the age 15-19 cohort. Actually in 1982 the oldest Boomers, those born in 1946, were beginning to creep into the 35-39 cohort.
The movement of the Boomer bulge up the pyramid is obvious, as is the fact that it diminishes in size as mortality rates increase. The pyramid goes from a significantly lateral shape in 1982 to an increasingly vertical arch six decades later. At present and for the next decade, our pyramid is more of a "house" shape. The greater female longevity is readily apparent. Less immediately conspicuous is the growth in height of the pyramid as the increasing longevity of both sexes is factored into the estimates.
But enough of the vague shape metaphors. Let's look at some comparative numbers for these seven snapshots. I've calculated the Elderly Dependency Ratios for each using the standard formula: The percentage of the population age 65 and over divided by the percentage age 15-64 multiplied by 100 (see note at bottom).
The elderly dependency ratio has major significance for U.S. budget planning. As this ratio shifts higher, the productive population is increasingly burdened by the cost of entitlement programs for the elderly. Has the U.S. government been ahead of the Boomer curve in this dependency ratio? The next graphic is a slide I presented last year at the RIIA Conference in Chicago.
From the mid-1950s to the mid-1970s, entitlements increased dramatically: early retirement options, the advent of Medicare, and cost-of-living adjustments. But in the early 1980s, in the aftermath of a fierce secular bear market and several years of runaway inflation, the entitlements trend began reversing.
Interestingly enough, it was 1982, the very year of our first pyramid chart, that President Reagan appointed the National Commission on Social Security Reform (aka the Greenspan Commission after its Chairman) to address the mounting financial problems of the Social Security system. The outcome included the gradual increase in the normal retirement age and taxation of benefits.
My annotations on the dependency ratios chart above suggest that we are approaching a threshold of increasing demographic burden on the financial system. The ratio has only increased 16.7% in the three decades from 1982 to 2012. But the rise from 20.4 to 26.8 over the next ten years is a 31.6% increase followed by a 21.8% increase over the following ten years.
Generational Distrust, Generational Accommodation, or Something Between the Two?
How will this shift in the elderly dependency ratio play out? Another presentation at last year's RIIA, Future of Retirement: 2020, by Anand Rao and Jamie Yoder of PwC, included a fascinating slide on the range of intergenerational scenarios in response to the ratio shift.
At present those of us who follow the economy and financial markets are looking for evidence that European sovereign debt issues won't trigger another international financial crisis. How that plays out remains an uncertainty. In contrast, we can be very certain that our accelerating demographic shift will continue to increase the political debate on entitlement expenditures, especially on the eve of a presidential election.
Despite the current focus in the U.S. on stimulus (aka "Quantitative Easing"), lurking in the wings are demographic changes that will ultimately take center stage in the financial planning of federal, state and local governments. The process will increasingly affect individuals as we adjust our retirement expectations and strategies for insuring income during our elder years.
In a follow-up commentary, I'll compare U.S. demographics with population pyramids and elderly dependency ratios of several major countries in Europe and Asia. If you think we have problems, just wait until you see the troubles lurking abroad.
|Note from dshort : And speaking of RIAA, I'm pleased to point out that Registration is now open for the 2012 RIIA Fall Conference in Boston (October 4-5). The conference theme is What Does it Take to be a Top Retirement Income Advisor? Highly recommended!|
Footnote on Ratios: The Elderly Dependency Ratio is an internationally used "rule of thumb" approximation that is useful for a comparative analysis of countries and for studies of individual countries across time frames. The percentage age 15-64 is the productive population, and the percentage age 65 and over is a population that draws heavily on the social capital of the country.
Of course the boundaries of the productive population are crude generalities. In developed countries, productive employment generally starts later as teenagers continue their formal education. At the other end of the 15-64 cohort, many adults continue employment beyond age 64.
The Total Dependency Ratio is an alternative way to capture the broader demographic burden on the productive years. The formula is the percentage of the age 65 and older cohort plus the percentage of the age 0 to 14 cohort divided by the percentage age 15-64 multiplied by 100.