There has been much talk lately about how a period of modest inflation will benefit the economy. This makes sense to me given the broadest definition of inflation. In an economy overburdened by debt, decreasing the real value of those debts via inflation will improve the debt load on the borrower.

Unfortunately, I don't understand how we get broad inflation, by which I mean simultaneous consumer price inflation and wage inflation. It makes sense to me that the Fed, by lowering interest rates, can push asset prices higher and provide corporations and borrowers an incentive to refinance debt thereby improving balance sheets and increasing cash flows. It also makes sense to me how this could put upward pressure on consumer prices in multiple ways including marginally lowering the value of the dollar and increasing costs for imported goods. And of course lower interest rates make saving less attractive and consumption (theoretically) more attractive. In our current economic environment there is also growing pressure on rents which make up a large portion of consumer price inflation measures. This, however, seems more to do with an increase of rental demand after a housing bust more than anything else. I also understand that these are only a subset of the numerous other mechanisms through which lower interest rates support the general price level.

What I really don't get is how increased inflation supports wage inflation as directly, specifically employment income. Income from investment, sure, that makes sense. With lower interest rates supporting higher asset prices and refinancing activity decreasing debt finance costs, the mechanism here seems pretty direct. But how does this translate to support for income earners whose primary income derives from labor? That seems far less straight forward.

Given their improved balance sheets, employers will likely have increased room to hire if demand picks up. But to the extent that demand is dependent on consumption (and it is significantly dependent on it in America today), it seems we have a chicken or the egg type problem. Until income grows, demand is unlikely to grow. But until demand grows, income is unlikely to grow. Will consumption really be meaningfully stimulated for people with low savings and debts that were too large to begin with? Should it be? And with a relatively high unemployment rate, average wages per employee are not going to see significant pressure to rise either (though clearly, growing employment increases aggregate income even at a constant wage level).

I don't know how to quantify this, but my sense is that the median income derived from capital across all people earning an income from labor is small. Another way of saying that is that I think most people in the labor force don't have significant investment income. So I'm guessing that income from capital will not be a huge stimulus for the large majority of the labor force. Perhaps aggregate income will increase inline with inflation; that is, total personal income in nominal terms will grow at or above the rate of general inflation. But will income derived solely from labor grow at that rate?

I'm curious about this because we've seen the best measure of the middle of the income distribution, real median household income, decline over the last couple of years. So are these folks falling behind in real terms because monetary policy has a less direct (or perhaps slower) impact on wages? That the median household income in the US declined by 6.2% from 2007 to 2010 suggests that they at least were falling behind up to that point. Will they catch up?

Unfortunately, median household income numbers are published only annually and with a considerable lag (the current data only goes through 2010). In order to try to get a glimpse of recent trends for both total income and labor income versus inflation I offer the following chart.

Inflation vs. Employment Income and Personal Income since 2007

The two income lines on the above chart are in nominal terms and indexed to 100 in January 2007. Along with the two measures of income, I also show consumer price (CPI-U) changes. The first income measure is Personal Income, all income for all persons from all sources — a pretty complete measure I would say. The second measure of income is what I call Total Employment Income. I define that to be (Average Hourly Earnings of All Private Employees) X (Average Weekly Hours of All Private Employees) X (Total Nonfarm Private Payroll Employment). It's my attempt to measure employment income separate from other sources of income. If anyone knows a better or more direct measure, please let me know.

The chart confirms to me what the median household income trends show, that middle income earners have fallen behind relative to inflation because of the recession. While Personal Income has kept up and now exceeds inflation growth since 2007, Total Employment Income still has some distance to make up. In short, I can now make the exceedingly dumb observation that the recession hurt the real income of the average labor force participant.

But on a potentially positive note, if you re-index the three series to the technical end of the recession (June 2009), it looks like employment income is doing better than inflation to date (although not as good as Personal Income).

Inflation vs. Employment Income and Personal Income since the end of the Recession

So perhaps the median household income measure for 2011 will reveal the bottom for this cycle was in 2010 and households will gain on real basis when 2011 statistics are released. If that's the case, I won't argue too much with the temporary pursuit of modestly higher inflation. I may not understand all of its mechanisms, but as long as inflation affects wages as well as consumer prices I am comfortable with it.

First off, I've started using the BLS' new Smooth Seasonally Adjusted metro area estimates for metro unemployment data. This is nice because it allows us to compare metro unemployment rates to national and state unemployment rates as well as to each other. Previously the metro data was distributed solely in a non-seasonally adjusted form which made anything but year-over-year comparisons difficult.

In addition to the new unemployment data, I've also updated the income section of the site. I'm now using the American Community Survey's 1-year data to report real median household income (and soon other income metrics) for metros, states and the nation as a whole. Combined with the metro unemployment and metro jobs data, I think this relatively new ACS data will be extremely telling as we observe the recovery from a local and household-based perspective.

Update: More commentary on inventory and asking price levels here, here and here.

25th, Median (50th) and 75th Percentile Asking Prices for US Homes

US Home Inventory

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.

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?

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.

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.

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 )

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.

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