US Manufacturing: Output Growth, Employment Decline
Posted Thursday, January 12 2012
Adam Davidson (of Planet Money fame) has an excellent piece on manufacturing in the Atlantic this month. In it he does an excellent job of illuminating the shifting landscape for manufacturing employees in modern factories. The piece is strong precisely because it's not primarily about aggregate numbers but individual experiences. Take a break and read the whole thing. The latest Planet Money podcast (The Past and Future of American Manufacturing) covers the same topic. If you needed a couple of charts to accompany the presentation, I whipped up a few. But don't neglect the story of the individual. The charts below are US Manufacturers' Annual Shipments, US Manufacturing Employment and Annual Manufacturers' Shipments per Employee respectively.
Sources: Manufacturers' Annual Shipments comes from the Census' M3 report, manufacturing employment is from the BLS. Inflation adjustments made using the GDP implicit price deflator.
R&D Spending by the Federal Government
Posted Monday, January 02 2012
Let me start by saying that this post is more like the beginnings of an exploration rather than something I am trying to draw conclusions with. My interests are the historical role that the government has played in funding research and development in the United States, the scale of those programs relative to overall government expenditures, and the potential for additional funding sources to complement and enhance funding for R&D in the future. I'm just trying to document these explorations a little. The data in the post comes from the OMB Historical budget tables, specifically Table 9.7 (xls) and Table 9.8 (xls).
The first chart shows the fraction of US government outlays (i.e. spending) that went to defense and non-defense research and development. The 1960s peak coincides with the Cold War Space Race that culminated with the Apollo program moon landing, a feat not repeated by humanity for 39 years now (not that China isn't working toward this goal as we speak). Both defense and non-defense spending have flattened out since then as a fraction of outlays.
The next chart breaks out funding for three Federal research agencies that operate in the non-defense category. The value is again expressed as a fraction of total Federal outlays. Anyone who has spent time in the academic world knows how critical grants from institutions like the NSF, NASA and NIH are to funding day-to-day research operations.
But perhaps looking at R&D spending as a fraction of total government spending is a bit misleading. As the country has developed over the last 60 years, we've instituted new programs and created new agencies which have increased our total expenditures. In reality R&D spending has been growing; it just hasn't been growing as fast as many other components of government spending. The next chart shows the R&D spending in 2005 constant (i.e. real, inflation adjusted) dollars.
Indeed, non-defense R&D spending was 40% higher in 2011 than 2001 on a real basis, but dipped slightly over the same period when expressed in terms of total government spending. So we continue to spend more and more money on research, it's just that we spend even more on other parts of government.
My real interest here isn't to critique the amounts we've been spending but instead goes back to that idea that Tyler Cowen put forth in the Great Stagnation, that is, to "raise the social status of scientists." Admittedly, status goes beyond financial considerations and includes a significant cultural component, but these (and other) research agencies help confer status to US scientists to the extent we define status as funding and employment within the scientist's field of expertise. We haven't done a bad job of things so far, but what could we accomplish if we were to increase funding to these agencies? What might we have already accomplished had that increase started 10, 15 or 20 years back? Can we reasonably expect that spending more in this category would yield positive results over the long term? Would it help foster a cultural appreciation of science and scientists?
With the sluggish economy and political deadlock in Washington, I doubt that we'll see significant increases in funding going to research and development organizations in the coming years — at least not from the Federal government. But I wonder if there isn't a way to leverage the institutions that we've already created. Can we build public/private partnerships that channel additional financial resources into the agencies we've already built? If so, would this be a wise thing to do?
Fraction of Unemployed Receiving Unemployment Benefits
Posted Monday, October 03 2011
How many unemployed persons receive unemployment benefits? It's a question I couldn't immediately answer, so I poked around the Department of Labor's website until I found the relevent data. The chart below tells us that about 60% of the unemployed were receiving unemployment benefits in August 2011 on a rolling 12-month basis (to remove seasonal effects). The benefits data is from a DOL spreadsheet that compiles unemployment benefits data across all programs [ xls ]. The number of unemployed persons is from the unadjusted unemployment level data published as part of the BLS Current Population Survey.
The chart is also a good reminder that not all unemployed persons can claim unemployment benefits. Of the nearly 14 million unemployed in August of 2011, a rough estimate suggests that around 5.6 million were not receiving benefits (it's rough because I'm estimating from a moving average which is inherently lagged).
The unemployment insurance program is complex and evolving, but the Center on Budget and Policy Priorities has an excellent overview of unemployment insurance if you're interested in learning more about the mechanics of the program.
A New Tool for Correlation Analysis
Posted Sunday, September 04 2011
Today I'm launching the Department of Numbers Correlator, a tool for analyzing the correlation coefficients of select ETFs tracking major asset classes. Check out the results:
Correlator Results for the Period from 8/2007 to 9/2011 for Select ETFs
The chart above shows the correlation coefficients calculated from monthly returns of 9 ETFs representing the US equity market (VTI), foreign developed markets (VEA), emerging markets (VWO), US REITS (VNQ), TIPs (TIP), Gold (GLD), Cash (SHV), Intermediate-term Treasuries (IEF) and Long-term Treasuries (TLT). Right now the tool only has 13 ETFs available for correlation analysis, but I intend to add a good deal more after testing things out and hearing your suggestions (my contact info is at the bottom of the page!). Something like Mebane Faber's "expanded list" of asset classes would seem like a good next step.
So what is this correlation coefficient business? In layman's terms, the correlation coefficient describes how the returns for different assets move with respect to one another. A correlation coefficient of 1.0 implies that two assets tend to move in the same direction (though not necessarily in steps of equal magnitude). A -1.0 correlation value suggests that they move in opposite directions. Correlation values of 1.0 or -1.0 are unheard of however. What you see in reality are evolving correlations that range between -1.0 and 1.0 suggesting imperfect interrelationships where one asset tends to move with another (correlation > 0) or against another asset (correlation < 0) to a moderate degree.
I should also point out that I built this tool with R which enables this neat clustering option (enabled above) that groups like assets. For instance, VTI, VEA and VWO are all pretty similar qualitatively (they're relatively highly correlated because they're all larger cap equities). The clustering recognizes this in a quantitative manner and groups these three tickers together when creating the chart. For similar reasons IEF and TLT are placed next to one another as well as TIP and GLD. For much more on the clustering method see the R documentation for hierarchical clustering.
Go try out the correlator yourself here!
Update 1: I added a bunch more tickers.
Update 2: I changed the correlator's methodology (sorry, it's still a beta project folks). I had previously been including dividends in the correlation calculations. Other correlation tools on the web do not do this, so I've decided to remove dividends from the calculation for the time being for consistency sake. I'll add them back in as an option later. The chart above has been updated.
Update 3: More changes! Sorry folks... While the correlation tool previously agreed with the S&P sector tool I referenced in the last update, it turns out that's not really a metric that I want to agree with! The S&P site seems to be using correlation of prices rather than correlation of returns — at least that's what I infer from the results. I think investors are more interested in the correlation of returns (including dividends). I'll lay this all out in a new post eventually, and I'll try to stop flip-flopping so much.
Revenue, Expenses, Debts and Deficits
Posted Monday, July 18 2011
All the debt ceiling talk has focused the minds of bloggers and analysts everywhere it seems. Over the weekend I decided to add a new page to the site that shows a continually up-to-date record of the US federal debt, deficit, receipts and expenses as a fraction of GDP. The page is an update to two popular posts I put together outlining government revenue and expenditures. And I see that Calculated Risk was also drawn to the analysis of government receipts and outlays data over the weekend. He put together an excellent post on the components of government receipts yesterday and it sounds like a version for outlays may be forthcoming.
While the immediate motivation for these explorations (for me at least) is the looming deadline for raising the debt ceiling so that the US will avoid defaulting on its debt, it should be clear that the issue of debt and deficits (and thus taxes and spending) is not going away anytime soon. This is an issue we'll be working through for many years, and I want to be able to keep track of our progress. So then here are the current charts for US debt, receipts, expenditures, and budget deficits or surpluses all as a fraction of GDP. The latest data (including updated versions of these charts) lives here.
Total Federal Debt as a Fraction of GDP
Government Receipts and Expenditures as a Fraction of GDP
Government Budget Surplus or Deficit as a Fraction of GDP: ( Receipts - Expenditures )
Median Income Before and after Taxes
Posted Tuesday, July 05 2011
At the beginning of his book The Great Stagnation, Tyler Cowen presents a single chart that provides the central evidence for slowing growth in the standard of living for individuals and families that he will explore in the remaining pages. That chart (below) shows where median family income would have been had we continued to see income growth roughly track per capita GDP growth.
That chart of extrapolated income derives from the chart below produced by Lane Kenworthy showing median family income and per capita GDP series as indices.
I've long wondered what median income would look like after taxes were taken into account and if the structure of Lane's chart might change given the dynamic nature of tax policy. Bruce Bartlett's recent post on average tax rates for four-person families pointed out the data I needed to make such a comparison. The Tax Policy Center produces annual average tax rates for four-person families at the median income level. Using their historical data I can back out the growth of after-tax median family income since 1980 (just after GDP per capita and median family income start to diverge) and add that data to the chart that Lane produced.
GDP per Capita, Real Median Family Income and Real Median Family Income After Taxes
The results, though not earth shattering, are interesting. Prior to 2000, both real (i.e. inflation adjusted) median family income and real median family income after taxes grew at about the same rate. Real median family income has actually declined since 2000, but when you look at after-tax dollars received by households it's been relatively flat. In other words, the median family has been able to avoid a more substantial decline in income by paying somewhat less in taxes. It's not a huge difference, but treading water certainly feels better than sinking. And part of me wonders if this phenomenon doesn't play some small role in the toxic policy debate surrounding taxes. If tax rates on the median family rise now, it's likely that the after-tax income of those families will fall from the plateau it has been riding along for the last decade. If real median income were growing, however, I suspect there would be at least somewhat less resistance for increasing tax rates as it would only slow growth in after-tax income as opposed to causing its outright decline.
Q1 2011 Housing Affordability Update & Buy vs. Rent Analysis
Posted Wednesday, May 11 2011
David Leonhardt, Calculated Risk and myself have updated our respective home affordability metrics in the last day or so. We're all using slightly different numbers (the housing world is awash in data attempting to measure the same thing), but I'd say we all have about the same conclusions to draw. Housing is definitely becoming more affordable. CR notes that the national price-to-rent index from the CoreLogic home price series has now returned to 1999 levels. As the 90s were generally a non-boomy time for housing, this is real progress.
Looking at the data from a metro perspective, David Leonhardt sees more price-to-rent ratios under 15 (his threshold for affordability). He mentions Atlanta, Los Angeles, Miami, Minneapolis, St. Louis, Las Vegas, Cleveland, Detroit, Phoenix, Pittsburgh and Tampa as places where the rent-ratios on homes are looking good these days. I agree with the assessment for all of those cities with the exception of Los Angeles. I'm not sure how Moody's Analytics (Leonhardt's source) arrived at the low rent-ratio they did, but I'm not finding comparable figures. The home affordability numbers I've calculated show considerably higher price-to-rent and price-to-income ratios for LA.
So let's look at the LA data a little closer as an example of how we arrive at these ratios. According to Realtor metro price statistics, the median home in Los Angeles sold for $292,700 in the first quarter of 2011. The Census reports that the median contract rent for the city in 2009 (the latest data available) was $1,111. This yields a price-to-annual rent ratio of 21.95 $292,700/(12*$1,111). If you adjust the median contract rent by the household size (as described here), the adjusted median contract rent for LA increases to $1,206 per month and this reduces the rent ratio to 20.22. This still seems fairly high to me; it's certainly not in the realm of Atlanta, Las Vegas or Tampa affordability at least.
More importantly though, I think David answers the buy vs. rent question exactly right. In many places it's not a bad time to buy a home assuming relative affordability, access to credit, a steady income and (perhaps most significantly) no need to sell for at least five years.
I've appended the metro affordability ratio table below so you can compare cities yourself. In addition to the price-to-rent and price-to-income ratios there is also the rent-to-mortgage ratio. It behaves just like the price-to-rent ratio when sorted, but the value of this ratio actually has a bit more intuitive meaning. A value of 1.0 means that the rent is equal to the payment on a 100% loan-to-value 30 year fixed-rate mortgage at prevailing rates. Values greater than 1.0 mean that renting costs more and values less than 1.0 mean the mortgage payment costs more (under these particular mortgage terms). As a potential buyer or investor, you're looking for rent-to-mortgage ratios of greater than 1.0 because it means you could conceivably pay less to own than to rent. Click on the heading name to sort the cities. It's fun — unless you're thinking about buying in Honolulu.
Note: The latest metro affordability data is always available here.
Home Income and Rent Ratios Using Realtor Prices by City - Q1 2011
| City, State |
Price-to-Income (Dollar Ratio) |
Adj. Price-to-Rent (Dollar Ratio) |
Adj. Rent-to-Mortgage (Dollar Ratio) |
|---|---|---|---|
| US | 3.43 | 18.53 | 0.87 |
| Birmingham, Alabama | 2.95 | 18.43 | 0.86 |
| Mobile, Alabama | 2.67 | 15.09 | 1.05 |
| Montgomery, Alabama | 2.70 | 17.18 | 0.92 |
| Phoenix, Arizona | 2.40 | 13.69 | 1.15 |
| Tucson, Arizona | 3.17 | 17.48 | 0.90 |
| Little Rock, Arkansas | 2.78 | 16.89 | 0.94 |
| Los Angeles, California | 5.00 | 20.22 | 0.78 |
| Riverside, California | 3.22 | 15.02 | 1.05 |
| Sacramento, California | 2.95 | 15.43 | 1.02 |
| San Diego, California | 6.22 | 25.96 | 0.61 |
| San Francisco, California | 6.31 | 27.86 | 0.57 |
| San Jose, California | 6.45 | 30.53 | 0.52 |
| Boulder, Colorado | 5.57 | 28.51 | 0.55 |
| Colorado Springs, Colorado | 3.34 | 19.91 | 0.79 |
| Denver, Colorado | 3.79 | 21.39 | 0.74 |
| Bridgeport, Connecticut | 4.33 | 23.20 | 0.68 |
| Hartford, Connecticut | 3.25 | 19.20 | 0.82 |
| New Haven, Connecticut | 3.49 | 17.22 | 0.92 |
| Norwich, Connecticut | 2.76 | 15.01 | 1.05 |
| Dover, Delaware | 3.23 | 17.76 | 0.89 |
| Washington, District of Columbia | 3.46 | 17.95 | 0.88 |
| Cape Coral, Florida | 2.02 | 10.64 | 1.49 |
| Deltona, Florida | 2.66 | 12.93 | 1.22 |
| Gainesville, Florida | 3.89 | 16.66 | 0.95 |
| Jacksonville, Florida | 2.55 | 13.07 | 1.21 |
| Miami, Florida | 3.34 | 13.39 | 1.18 |
| Ocala, Florida | 1.93 | 10.33 | 1.53 |
| Orlando, Florida | 2.55 | 11.76 | 1.34 |
| Palm Bay, Florida | 1.97 | 9.71 | 1.63 |
| Pensacola, Florida | 2.88 | 15.58 | 1.01 |
| Tallahassee, Florida | 3.34 | 15.08 | 1.05 |
| Tampa, Florida | 2.58 | 11.93 | 1.33 |
| Atlanta, Georgia | 1.80 | 10.58 | 1.49 |
| Honolulu, Hawaii | 8.55 | 33.01 | 0.48 |
| Boise City, Idaho | 2.64 | 15.70 | 1.01 |
| Bloomington, Illinois | 2.66 | 17.81 | 0.89 |
| Champaign, Illinois | 3.21 | 15.28 | 1.03 |
| Chicago, Illinois | 2.64 | 13.97 | 1.13 |
| Decatur, Illinois | 1.84 | 13.41 | 1.18 |
| Kankakee, Illinois | 2.16 | 13.67 | 1.16 |
| Peoria, Illinois | 2.02 | 14.24 | 1.11 |
| Rockford, Illinois | 2.02 | 12.02 | 1.31 |
| Springfield, Illinois | 2.23 | 16.46 | 0.96 |
| Fort Wayne, Indiana | 1.75 | 10.99 | 1.44 |
| Indianapolis, Indiana | 2.18 | 13.41 | 1.18 |
| South Bend, Indiana | 1.58 | 8.94 | 1.77 |
| Cedar Rapids, Iowa | 2.43 | 16.33 | 0.97 |
| Davenport, Iowa | 1.88 | 12.78 | 1.24 |
| Des Moines, Iowa | 2.36 | 14.73 | 1.07 |
| Waterloo, Iowa | 2.21 | 14.87 | 1.06 |
| Topeka, Kansas | 1.92 | 14.12 | 1.12 |
| Wichita, Kansas | 2.19 | 15.82 | 1.00 |
| Lexington, Kentucky | 2.85 | 18.05 | 0.88 |
| Louisville, Kentucky | 2.67 | 16.95 | 0.93 |
| Baton Rouge, Louisiana | 3.36 | 20.14 | 0.78 |
| New Orleans, Louisiana | 3.20 | 15.45 | 1.02 |
| Shreveport, Louisiana | 3.81 | 21.02 | 0.75 |
| Portland, Maine | 3.71 | 17.52 | 0.90 |
| Baltimore, Maryland | 3.25 | 17.92 | 0.88 |
| Cumberland, Maryland | 2.23 | 15.56 | 1.02 |
| Hagerstown, Maryland | 2.49 | 16.03 | 0.99 |
| Barnstable Town, Massachusetts | 5.16 | 25.85 | 0.61 |
| Boston, Massachusetts | 4.65 | 21.49 | 0.74 |
| Pittsfield, Massachusetts | 4.08 | 17.50 | 0.90 |
| Springfield, Massachusetts | 3.44 | 18.65 | 0.85 |
| Worcester, Massachusetts | 3.14 | 18.15 | 0.87 |
| Grand Rapids, Michigan | 1.72 | 9.99 | 1.58 |
| Lansing, Michigan | 1.35 | 7.28 | 2.17 |
| Minneapolis, Minnesota | 2.23 | 12.30 | 1.29 |
| Gulfport, Mississippi | 2.31 | 10.98 | 1.44 |
| Jackson, Mississippi | 3.04 | 18.49 | 0.86 |
| Columbia, Missouri | 3.21 | 17.59 | 0.90 |
| Kansas City, Missouri | 2.30 | 14.68 | 1.08 |
| Springfield, Missouri | 2.80 | 15.58 | 1.01 |
| St Louis, Missouri | 2.08 | 13.09 | 1.21 |
| Lincoln, Nebraska | 2.77 | 16.23 | 0.97 |
| Omaha, Nebraska | 2.46 | 14.59 | 1.08 |
| Las Vegas, Nevada | 2.40 | 11.84 | 1.33 |
| Reno, Nevada | 3.15 | 16.95 | 0.93 |
| Manchester, New Hampshire | 3.16 | 16.35 | 0.97 |
| Atlantic City, New Jersey | 4.02 | 19.72 | 0.80 |
| Trenton, New Jersey | 3.05 | 17.76 | 0.89 |
| Albuquerque, New Mexico | 3.58 | 19.20 | 0.82 |
| Farmington, New Mexico | 3.80 | 27.48 | 0.58 |
| Binghamton, New York | 2.41 | 14.51 | 1.09 |
| Buffalo, New York | 2.58 | 15.18 | 1.04 |
| Elmira, New York | 2.02 | 11.09 | 1.43 |
| Glens Falls, New York | 2.77 | 16.56 | 0.95 |
| Kingston, New York | 3.56 | 18.64 | 0.85 |
| New York, New York | 5.98 | 26.55 | 0.60 |
| Rochester, New York | 2.27 | 12.34 | 1.28 |
| Syracuse, New York | 2.31 | 14.01 | 1.13 |
| Charlotte, North Carolina | 3.81 | 21.84 | 0.72 |
| Durham, North Carolina | 3.17 | 17.70 | 0.89 |
| Greensboro, North Carolina | 2.80 | 16.30 | 0.97 |
| Raleigh, North Carolina | 3.88 | 25.10 | 0.63 |
| Bismarck, North Dakota | 2.86 | 18.19 | 0.87 |
| Fargo, North Dakota | 3.06 | 14.82 | 1.07 |
| Canton, Ohio | 1.98 | 13.07 | 1.21 |
| Cincinnati, Ohio | 2.18 | 14.39 | 1.10 |
| Cleveland, Ohio | 1.92 | 10.40 | 1.52 |
| Columbus, Ohio | 2.25 | 13.87 | 1.14 |
| Dayton, Ohio | 1.73 | 11.07 | 1.43 |
| Toledo, Ohio | 1.50 | 9.30 | 1.70 |
| Youngstown, Ohio | 1.35 | 8.72 | 1.81 |
| Oklahoma City, Oklahoma | 2.87 | 18.55 | 0.85 |
| Tulsa, Oklahoma | 2.65 | 17.08 | 0.93 |
| Portland, Oregon | 3.84 | 20.44 | 0.77 |
| Salem, Oregon | 3.42 | 20.17 | 0.78 |
| Allentown, Pennsylvania | 3.28 | 17.90 | 0.88 |
| Erie, Pennsylvania | 2.20 | 12.76 | 1.24 |
| Philadelphia, Pennsylvania | 3.31 | 17.68 | 0.89 |
| Reading, Pennsylvania | 2.62 | 14.92 | 1.06 |
| Providence, Rhode Island | 3.85 | 19.69 | 0.80 |
| Charleston, South Carolina | 3.80 | 19.24 | 0.82 |
| Columbia, South Carolina | 2.85 | 18.17 | 0.87 |
| Florence, South Carolina | 2.81 | 19.77 | 0.80 |
| Greenville, South Carolina | 3.17 | 19.93 | 0.79 |
| Spartanburg, South Carolina | 2.72 | 17.74 | 0.89 |
| Sioux Falls, South Dakota | 2.92 | 17.21 | 0.92 |
| Chattanooga, Tennessee | 2.88 | 17.33 | 0.91 |
| Knoxville, Tennessee | 3.09 | 18.53 | 0.85 |
| Memphis, Tennessee | 2.39 | 14.24 | 1.11 |
| Amarillo, Texas | 2.78 | 15.84 | 1.00 |
| Austin, Texas | 3.35 | 17.84 | 0.89 |
| Beaumont, Texas | 2.86 | 17.24 | 0.92 |
| Corpus Christi, Texas | 3.16 | 17.35 | 0.91 |
| Dallas, Texas | 2.62 | 15.04 | 1.05 |
| El Paso, Texas | 3.64 | 18.96 | 0.83 |
| Houston, Texas | 2.74 | 16.20 | 0.98 |
| San Antonio, Texas | 3.10 | 16.94 | 0.93 |
| Salt Lake City, Utah | 3.33 | 17.28 | 0.91 |
| Burlington, Vermont | 4.66 | 20.71 | 0.76 |
| Virginia Beach, Virginia | 3.22 | 16.21 | 0.98 |
| Kennewick, Washington | 3.25 | 21.61 | 0.73 |
| Seattle, Washington | 4.48 | 22.54 | 0.70 |
| Spokane, Washington | 3.57 | 17.88 | 0.88 |
| Yakima, Washington | 3.43 | 22.22 | 0.71 |
| Charleston, West Virginia | 2.90 | 20.22 | 0.78 |
| Appleton, Wisconsin | 2.07 | 13.58 | 1.16 |
| Green Bay, Wisconsin | 2.55 | 16.27 | 0.97 |
| Madison, Wisconsin | 3.81 | 19.75 | 0.80 |
| Milwaukee, Wisconsin | 3.49 | 19.76 | 0.80 |
US Greenhouse Gas Emissions
Posted Sunday, May 01 2011
Last month I pointed out that the EPA's annual US greenhouse gas (GHG) emissions inventory report showed that US emissions in 2009 fell by 15% since 2000. I finally got around to digging into the report a little more and it turns out the decline is actually 14% and only on a net basis. Net emissions are total emissions less carbon sinks from changes in things like land use (e.g. uncut or unburned forests). The table below shows net emissions since 1990 as well as the total change since 1990 and 2000.
Net Emissions: Sources - Sinks
| Year | Net Emissions (Tg)1 | Total Change Since 1990 | Total Change Since 2000 |
|---|---|---|---|
| 1990 | 5,320.3 | ||
| 2000 | 6,536.1 | 22.85% | |
| 2005 | 6,157.1 | 15.73% | -5.80% |
| 2006 | 6,102.6 | 14.70% | -6.63% |
| 2007 | 6,202.5 | 16.58% | -5.10% |
| 2008 | 6,020.7 | 13.16% | -7.89% |
| 2009 | 5,618.2 | 5.60% | -14.04% |
While net emissions were down 14% since 2000, total emissions (i.e. emissions with no adjustments for carbon sinks) were down only 6.75%. I admit I'm not terribly informed in the ways of carbon accounting, but it seems like total or gross emissions are a more direct measure of resource use in the economy. As the table below shows, most of this more modest decline came in 2008 and 2009 while the economy was in recession.
Total Emissions: Sources Alone
| Year | Total GHG Emissions (Tg) | 1 Year Change | Total Change Since 1990 | Total Change Since 2000 |
|---|---|---|---|---|
| 1990 | 6,182 | |||
| 1991 | 6,142 | -0.65% | -0.65% | |
| 1992 | 6,244 | 1.66% | 1.00% | |
| 1993 | 6,367 | 1.97% | 2.99% | |
| 1994 | 6,466 | 1.55% | 4.59% | |
| 1995 | 6,551 | 1.31% | 5.97% | |
| 1996 | 6,767 | 3.30% | 9.46% | |
| 1997 | 6,807 | 0.59% | 10.11% | |
| 1998 | 6,850 | 0.63% | 10.81% | |
| 1999 | 6,916 | 0.96% | 11.87% | |
| 2000 | 7,113 | 2.85% | 15.06% | |
| 2001 | 6,999 | -1.60% | 13.22% | -1.60% |
| 2002 | 7,039 | 0.57% | 13.86% | -1.04% |
| 2003 | 7,065 | 0.37% | 14.28% | -0.67% |
| 2004 | 7,175 | 1.56% | 16.06% | 0.87% |
| 2005 | 7,214 | 0.54% | 16.69% | 1.42% |
| 2006 | 7,167 | -0.65% | 15.93% | 0.76% |
| 2007 | 7,263 | 1.34% | 17.49% | 2.11% |
| 2008 | 7,061 | -2.78% | 14.22% | -0.73% |
| 2009 | 6,633 | -6.06% | 7.30% | -6.75% |
And as you can see, in terms of total emissions, 2007 was the most recent peak. So clearly the recession reduced the emissions rate of the US and we can expect it to pick up in 2010 and 2011 as the economy recovered. Still, it's somewhat impressive that while the economy grew 63% (in real terms) since 1990 and the population by almost 23%, emissions have only increased by 7.3%. We're clearly getting better at emissions per GDP and emissions per capita, but we're not yet at the point of unambiguous declines in total emissions.
How Much Have Americans Saved for Retirement
Posted Monday, March 21 2011
The Employee Benefit Research Institute released its annual Retirement Confidence Survey last week which, as the name suggests, measures Americans' confidence in their retirement prospects. It also provides the best annual look at how much American households have saved for retirement excluding the value of their homes and any defined benefit plans. In essence, the survey reflects what Americans have amassed in 401Ks, IRAs and savings accounts.
We've long heard about the poor job Americans are doing saving for retirement, so perhaps these mediocre numbers shouldn't come as a surprise. Nevertheless, it's clear that Americans' ambitions for retirement savings continue to fall well short of the reality of those savings, and understanding the disconnect is worth the effort. But let's stop speaking in general terms here and get specific.
Workers' Household Savings
The chart below comes directly from the report and shows total household savings and investments accumulated by people still working. In the table directly below the chart I've re-binned the data to show the percentage of American workers who have saved less than $25K, $100K and $250K. I think it's a little more telling in that form.
RCS Total Household Savings and Investments for Workers (inferred from chart)
| Total Savings | 2002 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 |
|---|---|---|---|---|---|---|---|
| Less than $25,000 | 50% | 53% | 48% | 49% | 52% | 54% | 56% |
| Less than $100,000 | 78% | 77% | 71% | 73% | 75% | 77% | 76% |
| Less than $250,000 | 93% | 88% | 86% | 88% | 87% | 88% | 90% |
As the report and chart note, 56% of workers had savings of less than $25,000 in 2011 and only 10% had savings of $250,000 or more. The survey also found that 58% workers say they will need more than $250,000 in retirement, so there's quite a gap to close.
Another way of thinking about this is to consider the median household savings for workers. The report doesn't show this, but it would be equivalent to the 50% point for savings. Since 56% of workers have less than $25K saved, the median household (which 50% of households are above and 50% are below) must have slightly less than $25,000 saved.
Retirees' Household Savings
The chart and table below tell the story from the perspective of retirees. We saw above that 56% of workers had less than $25K saved, but it's even more shocking to see that 54% of retired households have less than $25K in savings.
RCS Household Savings and Investments for Retirees (inferred from chart)
| Total Savings | 2002 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 |
|---|---|---|---|---|---|---|---|
| Less than $25,000 | 45% | 42% | 45% | 60% | 56% | 56% | 54% |
| Less than $100,000 | 66% | 67% | 67% | 75% | 78% | 73% | 71% |
| Less than $250,000 | 85% | 80% | 87% | 88% | 88% | 88% | 83% |
One of the warnings the report mentions is that while more people expect to work in retirement, few actually do. Making up for inadequate savings with part-time work in retirement just may not be an option:
The RCS has consistently found that workers are far more likely to expect to work for pay in retirement than retirees are to have actually worked. The percentage of workers planning to work for pay in retirement now stands at 74 percent (up from 70 percent in 2010), compared with just 23 percent of retirees who report they worked for pay in retirement.
To add insult to injury, the option of working longer is not always achievable either:
The RCS has consistently found that a large percentage of retirees leave the work force earlier than planned...some workers are likely to find themselves vulnerable to an unplanned early retirement.
Cheery. On an optimistic note, the report speculates that workers are becoming more realistic about retirement savings needs and that falling optimism may actually result in better savings habits and retirement outcomes. Check back next year to judge the progress towards that goal.
It's also worth repeating that the survey doesn't measure the value of defined benefit plans (like pensions), home equity or Social Security. All three of these have traditionally played a very large role in retirement finances and will continue to play a significant role in the future as well. Increasingly, however, Americans have to supplement these traditional retirement income sources with their own savings. The Retirement Confidence Survey does a very good job of measuring that component.
Other Report Highlights
- Instead of making fundamental adjustments to their spending and saving patterns in response to the decline in [retirement] confidence, workers continue to change their expectations about how they will transition from work to retirement.
- The age at which workers expect to retire continues its slow, upward trend. In particular, the percentage of workers who expect to retire after age 65 has increased over time, from 11 percent in 1991 and 1996 to 20 percent in 2001, 25 percent in 2006, and 36 percent in 2011.
- While $25 a week may not seem to be a significant amount of money, someone who managed to save this amount for a year would accumulate $1,300, which is more than 29 percent of workers report having accumulated in total.
- Debt is also a problem for a significant number of workers. Twenty-two percent report their level of debt is a major problem.
The full report, while long, is well worth the read.
The WSJ Offers Some Confusing Home Affordability Numbers
Posted Sunday, February 27 2011
I don't want to call these conclusions wrong because I have great respect for both the WSJ and Moody's Analytics (and Mark Zandi in particular), but the evidence provided in the articles Home Affordability Returns to Pre-Bubble Levels and Why 2011 May Be the End of the Housing Crash is very questionable.
Look at this claim made in the latter article for instance:
Housing is the most affordable it has been in decades, according to analysts at Moody's Analytics. They don't just look at house prices. They also look at incomes.
Nationally, the cost of a house is the equivalent of about 19 months of total pay for an average family, the lowest level in 35 years.
This is a pretty astounding claim and one that I'll suggest is at least misleading. First off, "19 months of total pay" translates into a price-to-income ratio of 1.58. On an absolute basis, that is a very low number (lower numbers implying greater affordability) and suggests that the typical home costs only 1.58 times what the average family earns annually. If we're trying to get at what's typical, does that sound right to anybody? The question to ask then is how they arrived at that number. Since I don't have the Moody's report and since the author of the piece hasn't said, all we've got to go on is the reference in the sentence "the cost of a house is the equivalent of about 19 months of total pay for an average family."
Home prices are notoriously hard to track, so the time series you use to measure them is important. Similarly, income can vary widely depending on what demographic you measure and what aggregate statistic you choose. I can't even speculate on the home price series that went into this analysis, but the income data appears to be for the average family (although what survey it's from is not clear). Average family income skews high (i.e. average income does not represent the middle of the income distribution), and it disregards households that are not part of families (which typically don't have as many wage earners). Why would they choose to report based on this metric? Did they then compare average household or family income to a median price? What was the rationale for this? I suspect Moody's has a good reason for choosing these measures, but that reasoning doesn't appear to have made it into either article.
Though there's no perfect way to measure home affordability, median income and median home prices provide a much better measure of the middle than averages do. So let's look at the price-to-income ratio using those figures. The median sales price in Q4 of 2010, according to NAR's sales figures for existing homes, was $170,600. The Census CPS shows the median income for a household was $49,777 in 2009, the latest data available. Using those two numbers we can easily calculate a price-to-income ratio of 3.43 — more than twice what the WSJ reported. I'm not sure where they got the average family income data from, but using the NAR median price implies the average family income they used was roughly $107,000 — quite high and certainly beyond typical.
Using FHFA price statistics we can see the price-to-income ratio back to 2000 (expressed in dollar-to-dollar terms). The current Realtor ratio I calculated above is marked in red. As you can see, these two home price series compare favorably.
Price to Median Household Income Using: FHFA Home Prices, NAR Home Prices
The other claim that I take issue with is that the price-to-income ratio has not been lower for 35 years. Maybe when you use the price-to-income ratio as they've constructed it, this is true. When you instead use median household income with a long term index from Case-Shiller or the FHFA, the ratios were definitely lower in the late 90s up to 2000 when they began to take off. Today the price-to-income ratio is about 20% higher than 1998. There's no doubt that things are significantly more affordable today than they were 5 years ago. The claim that they are the most affordable in a generation seems to be going too far however.
Price to Median Household Income Index Using: FHFA Home Prices, Case Shiller Home Prices, Core Logic/First American Home Prices
Note: The chart above expresses the ratio as an index set to equal 100 in 2000.
The tone of both of these articles reminds me of one of the elements of the bubble that we don't talk about much these days. During the 2003-2005 time period there was a real feeling that you were perhaps witnessing the only opportunity you might ever have to own a home. A lot of people who bought back then were called greedy because they wanted to own an asset that would appreciate at 10-15% annually forever. I've always believed that a huge number of people bought because they asked themselves if they'd ever be able to afford a home in the future if they didn't buy then. With prices appreciating so fast, homes were bound to be out of reach forever sooner or later — or at least that was the logic. The way these articles talk about real estate reminds me of that kind of "get in while you still can" mentality that reigned 10 years ago and supported that anxiety.
In the end I don't dispute the point that homes are considerably more affordable than they have been or that it makes more sense to buy a home you can afford today than it has for some time. Perhaps this all just exposes my preference that affordability analysis not get mixed up with speculative assessments of price direction. But I could easily forgive that if it was just a bit clearer how and why particular data sources were used to provide evidence for these claims. What is very clear to me is that you can't just state a price-to-income ratio (or any affordability ratio for that matter) without also defining a methodology.
