Deconstructing ShadowStats. Why is it so Loved by its Followers but Scorned by Economists?
It is hard to think of a website so loved by its followers and so scorned by economists as John Williams’ ShadowStats, a widely cited source of alternative economic data on inflation and other economic indicators. Any econ blogger who has ever written a line about inflation is familiar with ShadowStats. Time and again, readers cite it in comments, not infrequently paranoid in their tone and rude in their language. Brief replies that cast doubt on some of more extreme claims made by ShadowStats fans don’t seem to have much effect. After a recent round of comments, I promised the editor of one website to undertake a thorough deconstruction of ShadownStats. Here is the result.
What ShadowStats Gets Right: The CPI is a Flawed Measure of the Cost of Living
ShadowStats is Williams’ attempt to provide an alternative to the official consumer price index (CPI), which he views as a flawed measure of what members of the general public have in mind when they think of the cost of living. Let me start by saying that although I share the skepticism of many economists about the specific numbers published on ShadowStats, I agree that the official data do not tell the whole story. I support Williams’ attempt to provide an alternative to the official consumer price index that more closely reflects pubic perceptions of inflation. Here, in his own words, is how Williams explains his undertaing:
In the last 30 years, a growing gap has been obvious between government reporting of inflation, as measured by the consumer price index (CPI), and the perceptions of actual inflation held by the general public. Anecdotal evidence and occasional surveys have indicated that the general public believes inflation is running well above official reporting . . .
Measurement of consumer inflation traditionally reflected assessing the cost of maintaining a constant standard of living, as measured by a fixed-basket of goods. Maintaining a constant standard of living, however, is a concept not popular in current economic literature, and certainly not within the thinking or the lexicon of the Bureau of Labor Statistics (BLS), the government’s statistical agency that estimates and reports on consumer inflation. . . Individuals look to the government’s CPI as a measure of the cost of maintaining a constant standard of living, as well as measuring that cost of living in terms of out-of-pocket expenses. Without meeting those parameters, an inflation measure has limited, if any, use for an individual.
Williams is right about the gap between public perceptions of inflation and official indicators. As a recent series of posts on inflation expectations on the Atlanta Fed’s Macroblog noted, “Inflation surveys of households reveal a remarkably wide range of opinion on future inflation compared to those of professional forecasters. Really, really wide.” According to Macroblog, household expectations of inflation for the coming year consistently average two percentage points higher than those of professional forecasters, and some 13 percent of household respondents report inflation expectations of 10 percent or higher even at a time when professional forecasts fall short of 2 percent.
In technical terminology, we refer to a cost of living index based on the changing cost of a fixed-proportion basket of goods that themselves remain unchanged over time as a Laspeyres index without quality adjustment. Williams is again correct when he says that the official CPI, following mainstream academic thinking, has gradually evolved away from the Laspeyres concept toward a measure of the cost of a changing basket of goods that gives equivalent satisfaction as the prices, quantities, and qualities of the goods that consumers buy change over time.
The substitution issue. One of Williams’ key objections to the CPI is that instead of holding the cost-of-living basket unchanged for long periods, the BLS allows for frequent changes in its composition. Some changes in the consumer market basket occur when goods like audio cassette players become technically obsolete and new goods like cell phones appear on the market, but those are not the ones that Williams takes issue with.
What he finds more objectionable are changes in composition of the market basket that stem directly from changes in prices, as, for example, when people eat more chicken because beef becomes unaffordably expensive. To many people, fiddling the market basket to give more weight to the goods whose prices increase least and less to those whose prices increase most sounds like cheating. They see it as if a teacher tried to impress a tenure committee with high test student scores by letting the smart kids take the test several times each while sending their slow-learning classmates home on testing day.
Mainstream economists have a standard response: If we did not account for changed consumption patterns in response to changed prices, they say, we would overstate the cost of maintaining a constant level of satisfaction. Consider an example. Last week you went to the supermarket and bought 5 pounds of chicken at $2 a pound and 5 pounds of steak at $5 a pound, $35 total. This week you go to the supermarket and find that chicken still costs $2 but steak has gone up to $10. There is no question that the new prices leave you worse off than you were the week before, but how do you react?
You would need $60 to buy the same basket of goods that you bought last week for $35. In reality, you might not have that $60 in your wallet or purse, but if I gave you a $60 coupon that you could spend only at the meat counter, you would probably not spend it on the same basket of goods you bought last week. Instead, you might buy, say, 10 pounds of chicken and 4 pounds of steak. However, since $60 would be enough to buy your previous selection if you wanted to, we could conclude that you would change the mix only if the new $60 selection gave you more satisfaction than the original one.
Experience shows that if you put a large number of consumers in this situation and average their behavior, they will shift their consumption toward chicken, even though some individuals might stick with the original mix. Those who did shift would be better off with $60 and the new prices than with $35 and the old prices, and the ones who don’t shift are no worse off. In that sense, $60 overstates the increase in income the average consumer would need to reach the same level of satisfaction as before the price change.
Your cost of living has gone up, and that hurts, but just how much has the increase in the price of steak raised your cost of living? By the ratio of 60/35, a 70 percent increase, or by less than that? It depends on what you mean by the cost of living. If you mean the cost of buying a fixed market basket (the popular conception), then the 70% is correct. If you mean the cost of maintaining a fixed level of satisfaction, then 70% is an overstatement.
The quality issue. In addition to adjusting the relative quantities of goods in the consumer market basket over time, the BLS adjusts the CPI for changes in the quality of goods. The rationale for doing so is that failure to account for quality improvements would cause a further overstatement of the increase in spending that needed to maintain a constant level of consumer satisfaction.
Consider tires for your car. In the old days, you were lucky if a set of bias-ply tires lasted 30,000 miles. Today, a decent set of radial tires will go 60,000 miles or more, and give you a better ride along the way. So, if the price of a set of tires has increased from $100 to $400, what has been the impact on your cost of living? If you calculate the cost per tire, without accounting for quality, tires are four times more expensive than they used to be. If you calculate the cost per mile, they are only twice as expensive.
Williams does not necessarily object to adjusting for quality changes when they are objectively measurable, like package size or the number of miles you get from a set of tires. However, he argues that the BLS exaggerates the importance of quality by making adjustments for changes that consumers don’t really care about. In one post, he uses the example of two computers, purchased ten years apart. Yes, the newer computer has many extra features—more memory, a faster processor, a sharper display, and so on, each of which is quantifiable. However, not all consumers care about the new features. If you just use your computer for e-mail and browsing the web, and not for running big financial spreadsheets or high-powered gaming, who cares about processor speed? The old model does the job just as well.
Other issues. Williams has a number of other criticisms of the CPI beyond the substitution and quality issues. In particular, he takes issue with the way the BLS measures housing prices and medical costs. Without going into detail, in both cases Williams favors an out-of-pocket approach to housing and medical costs as being more in tune with the general public’s concept of the cost of living. I think it is fair to say that mainstream economists agree that these two items, which loom large in household budgets, are particularly difficult to measure, although not everyone agrees with the way Williams would like to see them handled. I hope to deal with these issues in a future post, but this one will focus on the basics.
Where ShadowStats goes wrong: How great is the understatement?
No one really denies that the CPI, as presently calculated, understates the rate of inflation compared to a measure based on a fixed basket of unchanged goods. Rather, what many economists, myself included, find hard to accept is Williams’ estimate of the degree of understatement. The following chart, reproduced by permission and updated monthly on ShadowStats.com, claims that since the early 1980s, the CPI has been understating the true rate of inflation by an ever increasing margin that now amounts to some 7 percentage points.
To get a more complete picture, here are the Shadow stats and CPI data in tabular form. (Williams does not permit reproduction of data that he provides to subscribers only, but his inflation estimates can be derived indirectly using the publicly available inflation calculator posted online by Tom R. Halfhill.)
Column (2) of the table gives the annual inflation rate, as calculated by Williams. His numbers agree with the official CPI data in Column (3) for 1980 and before. After that, they are mostly higher. Columns (4) and (5) apply the inflation rates for each year to calculate a price index with a 1980 base-year value of 100. Based on the ShadowStats rates, the index increases from 100 in 1980 to 1235 by 2014. If you apply the official inflation data, the index increase from 100 to 287 over the same period.
Like most professional economists, I find the rates of inflation given by ShadowStats to be implausibly high, even if we accept the notion of measuring inflation based on a fixed basket of unchanged goods. Economists at the BLS itself have published a defense of their own methodology, so there is no reason to repeat what they have done. Instead, I would like to make some simple and intuitive crosschecks of the ShadowStats vs. the CPI cost of living estimates. All of these crosschecks will use information from sources outside the control of the BLS or other agencies US government.
The grocery price crosscheck. The first crosscheck focuses on the prices of common grocery items. Old newspapers ads are a good nongovernmental source of historical price information. Here is one from 1982:
Pick a selection of items from this ad, then go to your local supermarket and note today’s prices for the comparable items. Now, perform two sets of calculations: First, predict forward to calculate how much each item should cost as of February 2015, using both the ShadowStats estimate of the increase in the cost of living from 1982 to 2014 (10.5 times higher) and the CPI estimate (2.5 times higher). Second, starting with the 2015 prices, predict backward to calculate how much each item should have cost in 1982 according to each of the two cost-of-living indexes.
The following table shows what I got when I performed the experiment, using prices for February 9, 2015 from my own local supermarket, Tom’s in Northport, Michigan. I have adjusted all prices for changes in package size, if any.
For example, a can of tomato sauce that cost $.25 at Piggly Wiggly in 1982 cost $.79 at my local market in early 2015. Starting from the 1982 price, the CPI predicts that it should cost $.61 in 2015 while ShadowStats predicts that it should cost $2.64. Starting from the 2015 price and working backwards, the CPI predicts that it should have cost $.32 in 1982 while ShadowStats predicts that is should have cost $.08. Based on these calculations, we see that the CPI underestimates inflation, as measured by the Tomato Sauce Index: The ratio of the 2015 predicted price of $.61 to the 2015 actual price, $.79, is .77, an underestimate of 23 percent. The ratio of the ShadowStats prediction to the actual price is 3.32, an overstatement of 223 percent. For tuna, both indexes overestimate inflation, the CPI by 34 percent and ShadowStats by 478 percent, and so on.
Continuing in that way, we see that the average underestimate of inflation from the CPI is 9 percent while the average overestimate from ShadowStats is 292 percent. However, we might want to take into account the fact that the 1982 prices given in the ad are sale prices, while those from 2015 are everyday prices that I took right from the shelf at the store. If we were able to use higher, everyday prices from 1982 instead of sale prices as the base for our calculations, it is likely that the CPI would not underestimate food inflation at all, while the overestimate from ShadowStats would be even greater.
Some ShadowStats supporters might object that this comparison is unfair because there have been few if any quality changes in the grocery items under consideration. They would say it is only to be expected that CPI understates inflation less seriously for items that are not subject to quality adjustment. Keep in mind, though, the BLS applies its controversial hedonic quality adjustment procedure to only 3 percent of the ordinary consumer goods that enter the CPI market basket. (See this source for a list. The BLS also applies quality adjustments to housing, which constitutes another 30 percent of the CPI.) For the vast majority of consumer goods, like groceries, for which the BLS does not make quality adjustments, ShadowStats fails the crosscheck.
The catalog price crosscheck. To get an idea of the accuracy of the CPI for goods that do undergo significant quality changes, we can turn to another source of historical price information: old mail order catalogs. Full reproductions of historical catalogs from Sears, Wards, and other retailers are available online at wishbookweb.com. In a post I wrote a couple of years ago, I proposed this simple thought experiment: If you could choose between shopping on line today at today’s prices, or buying from a mail order catalog of the past at past prices but with your present disposable income, what items, if any, would you buy from the past?
For example, would you buy a 25 inch color television from the 1981 Wards Christmas Catalog (p. 344) at $688.88, or would you buy the Samsung 28 inch LED model that I found on Amazon for $219? Based on the 1981 price, ShadowStats would predict that a 25 inch TV should cost $7,712 today, whereas the CPI would predict that it should cost a mere $1,794. Both indexes wildly overstate TV price inflation even without allowing for quality changes, but the CPI does not miss by nearly as much.
Maybe it is unfair to use something high-tech like a TV, so let’s go low tech. How about a pair of ladies leather gloves, item A on p. 85 of the 1981 Ward’s catalog, for $13. Compare them to a pair I found on Amazon for $10.70. ShadowStats suggests that a pair of leather gloves should cost $145 today.
You can repeat this experiment for item after item, and you come to the same conclusion: ShadowStats seriously overstates inflation for items of changing quality just as it does for those of unchanging quality.
Physical output crosschecks Among the other charts published by ShadowStats is one that compares the growth rate of real GDP, as officially reported, with an alternate GDP series that adjusted using Williams’ higher estimates of inflation. Here is the chart, reproduced with permission, as of March 2015:
The official series shows steady growth of real GDP for the years since 2000 except during the recession years of 2008 and 2009. In contrast, Williams’ series shows negative growth for every year except 2004. Many observers find it hard to believe that real output has been falling almost constantly for the past fifteen years, especially in view of the fact that total hours worked have increased slightly over the period. However, it would be nice to have some independent data, not tainted by inflation estimates, against which we could crosscheck the real GDP numbers.
During the Great Depression and earlier, before the concept of real GDP was invented, policymakers used to resort to purely physical indexes of output to gauge the strength of the economy—things like boxcar loadings and steel output. We can do the same. The next chart shows three such indicators: the number of new cars sold annually, an index of ton-miles of freight transportation, and an index of kilowatt hours of electricity generated.
All three show positive growth on average since 2000 (3.8 percent for autos, 1.4 percent for freight transportation, and 1.1 percent for electricity). It is hard to believe such growth would have been possible during a period when real GDP was falling at an average rate of 2 percent per year, as it has according to the ShadowStats series. Why would people be buying more and more cars if their incomes were falling? Why would railroads and barges be hauling more and more tons of freight if mines and factories were producing less and less output? Why would kWh of electricity per dollar of real GDP be rising by 3 percent per year despite the widespread adoption of more efficient lighting, more efficient electric motors, and more efficient buildings?
The physical output indexes are much more plausible if we accept the official GDP series: A rise in car sales roughly in line with the growth of real incomes, freight transportation lagging real GDP somewhat because service sectors are growing faster than goods-producing sectors, and electricity output growing but lagging real GDP because of more efficient technology.
The interest rate crosscheck. One final crosscheck uses data from financial markets to compare nominal and real interest rates. Nominal interest rates are those that are stated in the ordinary way, in dollars of interest that must be paid annually per dollar borrowed. Real interest rates are adjusted for changes in the purchasing power of money by subtracting the rate of inflation from the nominal rate of interest.
According to a well-established principle of financial economics known as the Fisher effect, nominal interest rates tend to rise as inflation accelerates and fall as it slows. It is easy to understand why. Suppose that a bank would be willing to loan you $1,000 for a year at 3 percent interest if it expected zero inflation during the year, and that you were willing to borrow at that rate. Now suppose that, instead, both you and the bank expected the price level to rise by 5 percent over the year. The bank would no longer be satisfied with a 3 percent nominal rate. The $1,030 you would pay back at the end of the year would be worth less than the $1,000 you borrowed in the first place. The bank’s real rate of return would be 3 percent minus 5 percent = -2 percent. However, if you agreed to pay an inflation premium of 5 percent, bringing the nominal rate up to 8 percent, the bank would get its desired 3 percent real return even taking inflation into account. From your point of view, the loan would still be worth it, especially if you expected your salary to rise with inflation. In that case, coming up with $1,080 at the end of the year would be no harder than it would have been to pay $1,030 if there had been no inflation and no wage increase. (See here for a more detailed explanation of the Fisher effect.)
The Fisher effect doesn’t hold exactly every year. Market conditions change and expectations do not always turn out to be accurate. However, the principle does hold well enough so that the real rate varies less over time than the nominal rate. It also ensures that periods of negative real interest rates on ordinary consumer loans are brief and rare.
Let’s take a look at interest rates over the past 25 years, then, to see what they tell us about ShadowStats vs. the CPI. The next chart uses the interest rate on 30-year conventional mortgages, but almost any other interest rate would give a similar picture. The chart shows the nominal 30-year mortgage rate and two real rates, one calculated by subtracting the inflation rate according to the CPI and the other according to ShadowStats.
What we see is that as inflation slows from the 1980s to the present, the nominal interest rate gradually falls. The real interest rate based on the official CPI remains roughly constant during the 1990s, and then decreases in the 2000s, but decreases less rapidly than the nominal rate and remains positive in every year. That pattern is consistent with the Fisher effect.
The real interest rate based on the ShadowStats inflation rate shows a very different pattern. It decreases more rapidly than the nominal rate and is negative in every year after 1995. This pattern is not consistent with the Fisher effect, nor is it consistent with common sense. In order to believe the ShadowStats real interest rate, we have to believe that banks have been losing money on every dollar of mortgage loans for the past 20 years. Furthermore, far from learning from their mistakes, their loans have become more and more unprofitable as time has gone by. It is simply not credible. ShadowStats fails another crosscheck.
Has Williams Simply Made a Mistake?
The fact that the ShadowStats inflation rate fails every crosscheck makes one wonder whether Williams has simply made some kind of mistake in his calculations. I believe that he has done just that. The mistake, I think, can be found in a table given in a post that represents Williams’ most complete explanation of his methodology. Here is the table in question, reproduced from his website, with the addition of my own lettered column headings for easier reference and the correction of a minor typographical error in Williams’ heading for Column E:
This table is of critical importance because it is the source of 5.1 points of the 7 percentage point gap between the official BLS inflation rate and the ShadowStats inflation rate. Here is how Williams himself summarizes his findings:
The substitution-related alterations to inflation methodologies were made beginning in the mid-1990s. The introduction of major hedonic concepts began in the 1980s. The aggregate impact of the reporting changes since 1980 has been to reduce the reported level of annual CPI inflation by roughly seven percentage points, where 5.1 percentage points come from the BLS’s published estimates of the effects of the individual methodological changes on inflation, shown in the preceding table. The balance comes from ShadowStats estimates of the changes not formally estimated by the BLS. The effects are cumulative going forward in time.
The intention of the table is to estimate the effect on the inflation rate of BLS methodological changes by comparing two different versions of the CPI. The series shown in Column D is the official CPI-U as originally published. The BLS does not revise these official CPI-U data after release even when it later makes methodological changes that would result in a different reported rate. The series in Column D is an experimental “research series” called CPI-U-RS that was devised by BLS economists to show what the index for urban consumers (CPI-U) would have looked if all of the methodological changes it made in the 1980s and 1990s had been in force from 1980. Columns C and E show the year-on-year inflation rate based on CPI-U-RS and CPI-U, respectively.
I agree with Williams that it is possible, in principle, to estimate the impact of the methodological changes in question by comparing CPI-U and CPI-U-RS. For example, imagine that a methodological change made in 1985 reduced the reported inflation rate by 0.8 percentage points per year and another change in 1990 reduced it by an additional 0.5 percentage points. The CPI-U-RS inflation rate would then be 1.3 percentage points slower than CPI-U inflation for CPI for each year from 1980 through 1984, when neither change had yet taken effect. From 1985 through 1989, we would expect the CPI-U-RS inflation rate to be 0.5 percentage points lower, because CPI-U for those years would incorporate the 1985 change but not the 1990 change. For years after 1990, when both changes were in effect, there would be no difference between the two inflation rates.
In practice, the situation a little more complicated, because a given methodological change does not necessarily change the annual inflation rate by a fixed number of basis points in every year. Suppose, for example, that the change in question affects only housing costs. The exact amount by which the change slowed or increased reported inflation could vary as the weight assigned to the housing sector changes over time, and also as the rate of housing inflation increases or decreases relative to the average rate of inflation for all goods and services. Still, it seems reasonable to expect that any such sector-specific effects would average out over time. If so, Williams’ assumption that we can calculate the impact of methodological changes by comparing the difference between the inflation rate for CPI-U and CPI-U-RS would, over a number of years, give a reasonable approximation.
Williams labels Column F “Change in annual inflation.” (I prefer to call it the “inflation differential,” since it is not a change from one year to the next, but rather, the difference between the inflation rates derived from CPI-U-RS and CPI-U, but terminology is not the central issue.) A negative entry in this column indicates that CPI-U-RS shows slower inflation than CPI-U. As we would expect, that is the case for most of the early years in the table. After 1999, when all of the controversial methodological changes are fully reflected in the CPI, there is essentially no difference between the two series, which is also what we would expect.
The trouble comes in Column G of the table. That column gives the running totals of the inflation differentials from Column F, which, by 1999, reach 5.1 percentage points. Williams’ mistake is to misinterpret the figures in this column as what he calls the “cumulative annual inflation shortfall,” that is, as a measure of the amount by which the official CPI-U underestimates the rate of inflation that would have been reported if the controversial methodological changes had never been made. However, even by Williams’ own assumptions, that interpretation is incorrect, as we can see by working through a few lines of the table.
Start with the line for 1981. In that year, the officially reported inflation rate, based on CPI-U, was 10.3 percent and the rate based on CPI-U-RS, which assumes that all future methodological changes were already in effect, was 9.5 percent, or 0.8 percentage points lower. Another way to put it is to say that the 0.8 percent difference measures the consolidated effects of all methodological changes made in 1982, 1983, 1984, and so on through 1999, when the last of the changes in question came into force.
Next look at the line for 1982, when CPI-U inflation was 6.2 percent and CPI-U-RS inflation was 6 percent. (Because of the way in which he handles rounding errors, Williams records that as a difference of -0.1 percent rather than -0.2 percent. Rounding errors do not play any significant role in my story, but if you want, you can work out the unrounded numbers based on Columns B and D.) That difference reflects the impact of all methodological changes made in 1983, 1984, and so on, that is, the effect of all changes except those that came into effect in the year 1982 itself. If we add the -0.1 from Column F of 1982 to the -0.8 of the same column for 1981 to get -0.9, as Williams does, we are double counting the effects of 1983 and later, which, by assumption, were already included in the -0.8 for 1982 and are included again in the -.01 for 1983.
The problem of double counting is even more obvious if we look at a pair of years like 1988 and 1989, when the Column F differential between CPI-U-RS inflation and CPI-U inflation does not change. (Again, beware of rounding errors in Williams’ table.) By assumption, the differential changes each time there is a change in methodology. The fact that it does not change from 1988 to 1989 implies that no new methodological changes came into effect in 1989. If not, why does Williams increase his estimate of the cumulative impact of the changes (Column G) from -1.0 percentage points in 1988 to -1.5 percentage points in 1989? To do so double counts the effects of changes subsequent to 1989, which were already included in the numbers for 1988.
Williams’ error becomes more obvious still if we replace the numbers in his table with the simplified numerical example that I gave earlier. In that example, there are just two methodological changes, one in 1985, which reduces the reported rate of inflation by 0.8 percentage points, and another in 1990, which reduces the reported inflation rate by a further 0.5 percentage points. I also add two columns to the table. Column H gives the hypothetical unadjusted rate of inflation, that is, the rate that would be reported if none of the methodological changes had been made. For simplicity, we assume that rate to be a steady 4 percent. Column I gives the true cumulative inflation differential, that is, the difference between the officially reported CPI-U rate (Column E) and the assumed adjusted rate (Column H). Here is my the full numerical example:
Several things that the complexity of Williams original table obscures become crystal clear in this example:
- Each methodological change has the effect of reducing the inflation rate by a given number of percentage points in the year when it first comes into use, and for all years going forward.
- The total permanent reduction in the inflation rate (true cumulative inflation shortfall) is equal to the sum of the effects of the individual methodological changes, with each change counted only once. (In this example, there are two changes, which slow inflation by 0.8 and 0.5 percentage points, respectively, making a total reduction in the reported inflation rate of -1.3 percentage points.
- The cumulative inflation reduction can never be greater than the differential given in Column F for years before any of the methodological changes come into use. The simplest way to determine the impact of the entire series of methodological changes is to look at the difference between CPI-U-RS and CPI-U for such a year . It this example, that would mean any year before 1985, when the differential is 1.3 percentage points.
- The method that Williams uses in his original table, a running total of the difference between the CPI-U-RS and CPI-U inflation rates (Column G), seriously overstates the true cumulative inflation shortfall because it counts each change more than once.
The Bottom Line
The bottom line here is that Williams’ use of a running total of inflation differentials to compute a “cumulative inflation shortfall” of 5.1 percentage points exaggerates the true impact of the methodological changes made by the BLS. A better way to estimate the cumulative inflation shortfall would be to look at the differences between CPI-U-RS and CPI-U before 1983, the year when the BLS implemented the first of the changes that it incorporates in the CPI-U-RS series. That approach is not quite as precise when we use real-world numbers, as Williams does in his original table. As explained earlier, the actual data include statistical noise caused by changes in weighting and in relative price changes among sectors. However, we can approximate the true inflation shortfall by averaging the numbers for 1981 and 1982 from Williams’ table, giving an estimate of -0.45 percentage points.
As mentioned above, Williams’ ShadowStats inflation series incorporates an additional 2.0 percentage point correction to reflect methodological changes that are not captured in the CPI-U-RS series. I would like to examine that number more carefully in a future post, but for the sake of discussion, we can let it stand. If so, it appears to me that, based entirely on Williams’ own data, methods, and assumptions, the adjustment for the ShadowStats inflation series should be about 2.45 percentage points below CPI-U, rather than the 7 percentage points he uses.
In my view, Williams alternative measure of inflation would be more convincing if he were to make this correction. It would also be less likely to feed the anti-government paranoia of some of his followers, who allege that the BLS is falsifies source data and manipulates reported indicators in the way that Argentina and some other countries appear to do.
It is worth noting that Williams himself makes no such claim. He is a fierce critic of BLS methodology, but he acknowledges that the agency follows its own published methods. He argues that the BLS has adopted methods that produce low inflation indicators, but not for motives of short-term partisan politics. Rather, he sees the choice of methodology as driven by a longstanding, bipartisan desire to reduce the cost of Social Security and other inflation-indexed transfer payments. It would be hard to deny that he is at least partly right about that motivation.
Finally, in closing, I would like to thank Williams for taking the time to make detailed comments on an earlier draft of this post. Our private dialog has not yet led to a complete resolution of the issues I have raised here, but I hope that he will address them in future public comments. The search for alternative inflation indicators goes on.
First of two parts. Follow this linkfor Part 2, which discusses the ShadowStats alternate unemployment indicator.
50 Responses to “Deconstructing ShadowStats. Why is it so Loved by its Followers but Scorned by Economists?”
Very nice, thank you. I have people throwing Shadowstats at me almost every time I try to have a serious discussion about the economy. Now I can just point them here.
One point though: BLS doesn't assume people will substitute chicken for steak. See their FAQ number 3:
What about housing – How do CPI and ShadowStats compare with respect to housing ? In the markets where the jobs are – east and west coast the housing costs are really high, costing upto half of the average person’s paycheck. California (12-14% of GDP) and Northeastern megalopolis (20% of GDP) together account for a third of the GDP, and the housing in these areas is among the most expensive in the country. Do the averages in the CPI mask the variance we see in terms of the “pain” in housing an average consumer might feel in these areas vs the low cost housing areas of the country ? Based on these would ShadowStats be a closer approximation for these two areas vs. the rest of the country ?
Yes, you are right, I used chicken-steak because it is common in popular discussions. I should have mentioned explicitly that the BLS does not assume you can substitute chicken for steak without affecting your standard of living. However, when it comes time to introduce a new set of base-year quantity weights, as the BLS periodically does for the CPI, then any shift from chicken to steak, or from land lines to cell phones, or anything else, would come into play.
Housing in the CPI will have to be the subject of a whole post. However, with regard to the regional issue, the BLS does publish regional indexes, which do vary in their rates of inflation, not least because of the relative costs of housing.
[…] EconoMonitor : Ed Dolan's Econ Blog – Deconstructing ShadowStats. Why is it so Loved by i… […]
ED Dolan´s work goes to show how the two sides of critique (methodology and/or extent of result thereof) interact. It is amusing, however, that old ads and catalogues help test and prove the numbers.
And as for the intrinsic fault of Williams – I reckon he simply messed up on his Excel sheet (not adjusting the “base figure” for each year) and therefore double counts. I am a real layman on all these computer programs, but I often have a talent of detecting the causes of flaws, the experts do not consider, because they are “below” them. Nothing like good old common sense.
[…] economic data on inflation and other economic indicators. So begins economist Ed Dolan in "Deconstructing ShadowStats. Why is it so Loved by its Followers but Scorned by Economists?" I've blogged about this before, but I've never done close to as thorough a job as Ed has done. His […]
[…] economic data on inflation and other economic indicators. So begins economist Ed Dolan in “Deconstructing ShadowStats. Why is it so Loved by its Followers but Scorned by Economists?… I’ve blogged about this before, but I’ve never done close to as thorough a job as Ed […]
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Shadowstats is popular among skeptics of government (and everyone should be at least somewhat skeptical of government) because
(1) it seems like every change to how CPI or any other government-issue statistic is calculated, has the immediate effect of making the government look better;
(2) the government has a history of discontinuing data series when they look particularly bad, which of course has the immediate effect of making the government look better;
(3) nobody else is generally known to be systematically producing a similarly wide selection of alternative statistics.
Unfortunately none of those factors relate to whether Shadowstats does a good job.
Not a single mention of MIT's Billion Prices Project, which checks actual retail price changes over time with NO heuristic substitutions, and which shows the government CPI numbers tracking almost identically? ( http://bpp.mit.edu/usa/ ) PS: The subscription price for Shadowstats is unchanged over many years. Maybe there's a message there?
You raise a good point. The BPP is often cited as a crosscheck against ShadowStats, but there is a problem with that. Williams' claim is that the official CPI understates inflation because of the way it revises weights (substitution adjustment) and the way it handles quality changes. BPP is a little hazy about their exact methodology on these points, but if I understand correctly, they use the same approach to weighting and quality adjustment as the BLS. So, although I view BPP very positively, it is not really a good cross check on those aspects of methodology.
What BPP is good for is checking the BLS for outright falsification or manipulation or source data or final statistics, as is done Argentina. If you look at BPP for Argentina, you see a dramatic divergence between the official and BPP price indexes, confirming the widespread belief in data fraud. Williams himself does *not* claim that BPP engages in outright fraud of the Argentine kind, but some of his followers do make that claim. BPP shows they are wrong.
The bottom line here: I included a section on BPP vs. ShadowStats in an earlier draft, but I took it out because I wanted to focus on the methodological issues, not the issue of alleged fraud.
«MIT’s Billion Prices Project, which checks actual retail price changes over time with NO heuristic substitutions, and which shows the government CPI numbers tracking almost identically?»
The BPP is instead the best and most striking evidence that the ordinary CPI does indeed significantly understate “inflation” as it is understood by ordinary people as the change in the price of a constant standard of living.
Because the BPP measures online prices it is overweight a lot of prices that are for imported “gadgets”, and underweight for the prices of stuff that is a significant component of the living standard of ordinary people, say those on median wage, for example a number of services that impact the cost of living of ordinary people.
The BPP in effect measures price changes in imports of manufactures from China and other low cost countries. That the CPI tracks it closely means that the CPI is also skewed towards the price of imported manufactures, rather than the prices of goods and services consumed in the USA.
«I reckon he simply messed up on his Excel sheet (not adjusting the “base figure” for each year) and therefore double counts.»
I also have the impression that he has cumulated the distortion in the CPI, and I tend to agree with our blogger that the arguments used by ShadowStats are realistic, but that the CPI understates inflation by more like 1.5-3% points than 5-7% points.
Still 1.5-3% points over time is pretty huge.
BTW sometimes more realistic estimates of inflation do “by mistake” appear, for example in the UK:
«Sir Stuart Rose, who as chairman of M&S could be described as the voice of Middle England, says that the real inflation rate is around eight per cent, while pay rises are closer to three.»
when “official” inflation in the UK around 2008 was more like 5%.
«Keep in mind, though, the BLS applies its controversial hedonic quality adjustment procedure to only 3 percent of the ordinary consumer goods that enter the CPI market basket.»
The weights of those would be interesting, as the estimates in the Boskin commission report were that hedonics would make a pretty significant difference of close to 1%, and in the decades since they have been “improved”.
«Among the other charts published by ShadowStats is one that compares the growth rate of real GDP, as officially reported, with an alternate GDP series that adjusted using Williams’ higher estimates of inflation.»
Well, it turns out that the BEA who publish GDP numbers also apply hedonic adjustments to a large chunk of “real” GDP:
The “real” GDP numbers are significantly boosted by the use of the “Gerschenkron effect”, which in the 1950-1960s was used perhaps inadvertently by the Soviet Union, and is now used to the same effect by the USA:
Again the total effect does not see huge, perhaps being worth 1-2% points of reported “real” GDP growth, but that is a large fraction of yearly “real” GDP growth.
– You make a number of excellent points. Too many people don't like Shadowstats at all and trash everything Williams brings up. (BTW. His calls for Hyper-Inflation are outright silly. Deflation will be the name of the game going forward).
– Regarding the TV set & the ladies gloves. Where these articles made in 1981 & 2015 ? Outsourcing of production to low wage countries (e.g. TVs) in the 1980s & 1990s has kept US CPI inflation much lower than if those products were still made in the US.
In that regard I think you're comparing apples to oranges. Perhaps it's better to focus on products that are still made in the US.
– Based on another metric both CPI & Shadowstats are wrong. I know that gold does well in an environment with negative real interest rates. That's why the chart with the real 30 year mortgage is so interesting. Gold started to go higher from the year 2000/2001. That would assume that e.g. the REAL 30 year rate would have gone negative around 2000/2001 and not in say 1995. (Shadowstats).
But based on the "Real By CPI" rates gold never would have gone higher after 2000/2001.
– Wouldn't the REAL 30 year T-bond rate have been a much better gauge for real interest rates ?
"In order to believe the ShadowStats real interest rate, we have to believe that banks have been losing money on every dollar of mortgage loans for the past 20 years. Furthermore, far from learning from their mistakes, their loans have become more and more unprofitable as time has gone by. It is simply not credible. ShadowStats fails another crosscheck."
I disagree because the main business of banks is lending money, not buying/trading TVs or Ladies gloves.
– Nonetheless, a very thoughtful & interesting article.
– In one other regard CPI understates inflation. Healthcare expenses account for about 17% of GDP but have only a weighting of 4% in the inflation calculation. What's the Shadowstat's weighting for Healthcare costs ? Does he disclose that ?
«the BEA who publish GDP numbers also apply hedonic adjustments to a large chunk of “real” GDP:»
Some insiders understand that, I forgot to add this quote from J Stiglitz:
«Likewise, quality improvements – better cars rather than just more cars – account for much of the increase in GDP nowadays. But assessing quality improvements is difficult. Health care exemplifies this problem: Much of medicine is publicly provided, and much of the advances are in quality.»
The “methodology improvements” used to boost reported nominal GDP and nominal GDP growth however are a somewhat different topics from those used to shrink reported inflation rates and boost “real” GDP.
«If we did not account for changed consumption patterns in response to changed prices, they say, we would overstate the cost of maintaining a constant level of satisfaction.»
This is completely laughable! Obviously if someone buys more chicken and less steak if the price of meat goes up does so because they they have a budget constraint, not because more expensive steak makes it less satisfying than the same meat with a higher price.
What remains constant is the budget, not the level of satisfaction, forcing the consumer to buy less satisfying chicken because they can no longer afford more satisfying steak.
In any case, “satisfaction” is a nebulous concept which is hard to measure, and inflation should be the change in the cost of a constant standard of living, not of a constant level of “satisfaction”, if it has a meaning that people can recognize, instead of sophistry use to tell people that when prices go up their level of satisfaction can remain constant if they buy less satisfying stuff instead :-).
«Perhaps it’s better to focus on products that are still made in the US»
That’s absurd: the price of a constant living standard in the USA includes the cost of good no matter where they are produced. If the same gloves cost less when imported from China than when manufactured in the USA, the price of the constant living standard goes down.
But perhaps the gloves etc. are not the same, because maybe the quality has gone down. But curiously as Shadowstats reports the BLS almost never applies reverse hedonic adjustments when the quality of some goods or services has obviously gone down over the decades, for example where well-built USA products have been replaced by cheaper but poorly-built imports.
«Regarding the TV set & the ladies gloves.»
«Healthcare expenses account for about 17% of GDP but have only a weighting of 4% in the inflation calculation.»
This post has a table produced by L Summers that shows for some products like TVs and also for some services how different the BLS notion of “inflation” is:
«Television sets at five stand out. That is obviously a reflection of a rather energetic hedonic effort by the Bureau of Labor Statistics. One suspects that equally energetic hedonic efforts are not applied to every consumer price. But nonetheless, the simple fact is that the relative price of toys and a college education has changed by a factor of ten in a generation. The relative price of durable goods or clothing as a category and all goods has changed by a factor of almost two in a generation. This table provides a somewhat different perspective on the common and valid observation that real wages have stagnated in the United States.»
Quite funny, or perhaps not.
I need to look into how SS handles healthcare and housing as separate issues.
I think you have it wrong with the chicken-steak example. The issue has nothing to do with whether chicken or steak is inherently more satisfying. Even when steak goes up, a person has the opportunity to buy just as much steak as before. After all, even a real beefeater spends only a tiny part of his total budget on steak, so you could cut back on something somewhere if you preferred to–beer, movies, house repairs, whatever. So the alternatives have to do not with inherent satisfaction, but with whether you have diminishing marginal utility for all goods, including both steak and chicken. It would be just the same if a person personally really liked chicken a lot and would eat mostly chicken even if they price was the same, ., or whether they ate chicken only in desperation. And it would work the same if chicken started at $10/lb and steak at $2, then steak went to $4.
Or where badly built US products like cars have been replaced by better built imports from Japan and Germany, eventually driving US companies to improve. Not all imports are shoddy.
[…] Deconstructing ShadowStats. Why is it so Loved by its Followers but Scorned by Economists? (economonitor) […]
[…] From my perspective the Shadowstats CPI never appeared to be doing a better job than the official number of reflecting the dollar’s change in purchasing power. I therefore never paid any attention to it and never bothered to analyse why, given that the only differences between the Shadowstats calculation and the official calculation were the changes in calculation methodology that were implemented by the BLS (Bureau of Labor Statistics) since the early-1980s, there would be such a big difference between the official and the Shadowstats numbers. However, Ed Dolan has recently taken the time to analyse and explain the difference in a 31st March article at EconoMonitor.com. […]
I'm not a big believer in Shadowstats, though I do worry about the chicken-steak argument eventually turning into a corn meal-acorn meal argument where we are arguing that switching from subsistence food to starving time food is hedonically justified, as either one will keep body and soul together.
My impression is that modern inflation is a bit different from historic inflation, at least in European civilization. Since the late Middle Ages there has been a roughly 200 year inflationary cycle in Europe. Medieval economists distinguished fames, or times of famine, from cares, times of inflationary pressure without an obvious shortage. Modern European historians have studied this cycle (see Hackett-Fisher's The Great Wave for a better discussion) in which during times of "dearness" fuel, rent and food went up in cost while the price of labor and manufactured goods fell. The difference more recently is that our industrial technology now lets us manufacture food and fuel. This seems to have greatly mitigated the usual inflationary pressures.
" the CPI understates inflation "
My argument is not exactly that the CPI understates "true" inflation, whatever that is. My argument is that the CPI understates *perceived* inflation. For various reasons, both those mentioned here and behavioral reasons mentioned in my earlier post, perceived inflation is higher than the CPI. But that does NOT mean that an index of perceived inflation that was 1.5-3% higher than the CPI would be a better indicator to use for policy purposes and for purposes of indexing wages, pensions, and other payments. I would not want to see the fed targeting 2% inflation on such an indicator, that is, to target CPI deflation, nor would I want to see constant relative enrichment of people receiving indexed payments.
[…] Bernanke, Summers, Krugman – the high priests of Macro-Strology: Part I – Asia Times Why Shadowstats is Loved by its Followers but Scorned by Economists – EconoMonitor Why the South Is the Region With the Fewest Breweries – Atlantic Survey: […]
[…] Deconstructing ShadowStats. Why is it so Loved by its Followers but Scorned by Economists? (EconoMonitor) […]
You say BPP is "a little hazy about their exact methodology", but I find them refreshingly exact: "our data is collected every day from online retailers using a software that scans the underlying code in public webpages and stores the relevant price information in a database. The resulting dataset contains daily prices on the full population of products sold by individual retailers, allowing us to observe every single price change that takes place. Our data includes information on product descriptions, package sizes, brands, special characteristics (e.g. “organic”), and whether the item is on sale or price control. For more details on the collection methodology, see Cavallo (2010) “Scraped Data and Sticky Prices”.”
You see? Every price, every day, no brand substitutions, no heuristic substitutions ever, and even controls for "on sale thru Friday!" The focus on 'retail' (which they can find and scape so easily using web crawlers) misses 'services' which are not so easily identified (nor once found so comfortably reliable, as so much of that is vulnerable to local and after-the-fact negotiation).
The BPP may not be perfect, nor is the BLS CPI, but either is a lot better than Shadowstats, who, if we are to believe it, would have us paying $10 for a potato and $75,000 for a Toyota, if we took those products back in time and then moved them back to present day using the absurd inflation numbers that Shadowstats insists are real.
A nice little paper from one of the BPP foundeers on methodology and data scraping: http://www.mit.edu/%7Eafc/papers/Cavallo-Scraped….
Thanks for the link. I hope to read more on BPP and its methodology, and to comment on them in a future post. When I say they are hazy about their methodology, perhaps I should better say I am hazy about it. In particular, I need to know more about how they handle changes in weights for product categories over time, how they handle the introduction of new product categories, and how they handle the replacement of old models of a product with new models having different features.
Wow, you really put a lot of work into this. I'll admit it was so long I had to skim parts, but all I read was well done.
I'll be blunt: Shadowstats is an outright fraud. Williams does not attempt as he claims to calculate what the CPI would be if old-fashioned methodologies were used. He simply adds an arbitrary adjustment to the official CPI index. And that adjustment is ridiculously too big to justify with any sane argument.
Where Williams has nonetheless done something of some value is getting the word out that inflation measurements are inherently subjective. The only price changes that can be truly objectively measured are those of goods that are perfectly standardized and don't change. Even your tomato sauce index isn't entirely objective because the 1982 tomato sauce isn't the same as the 2015 tomato sauce and people will have varying opinions on which one is how much better.
Of course Williams has gone much farther and weaved this paranoid vision of statisticians endlessly manipulating every single one of those little subjective decisions in order to manipulate the masses into wrongly believing they are living in a time of progress when actually everything's been going downhill for decades. Well, you've made a great effort here, but those who fall for that kind of nonsense maybe can't be helped.
PS Personally I think the problem with hedonics is that they aren't used enough. It's silly to suggest as Williams does that when producers introduce improved products at higher prices the entire price increase should be attributed to inflation and none to real growth. That said there's an expediency issue and statisticians also tend to not apply hedonics enough when the quality of products is reduced.
@middedonne – I have to support Ed's original comment: the full methodology used by BPP is I think not clear. The description you give isn't much help. I just checked their site and most of the tabs aren't even working.
It's a great project, but don't kid yourself that there's any way around the subjective decisions that must be made when calculating an aggregate inflation rate. Someone has to decide how to weight the various products measured. Someone has to decide how to treat changes in products and what relative real values to place on newly introduced products. There's never a single obviously correct answer to any of the many methodology questions that come up.
You seem to be suggesting that the BPP only compares the prices of each branded product to exactly that brand and product over time. And you seem to think that's a good method of calculating inflation. I don't know if that's what BPP does, but it would be a very silly way to calculate inflation for any products in any technology-related industry. Their prices would always trend down, often dramatically so, simply because new products are typically sold at a premium, discounted some after a short time on the market, and discounted a lot when a newer model is introduced. All you would do is track the discounting trend from the moment a product is introduced till it's discontinued. With items like cars that would be a soft downward trend; for mobile phones a drastic downward trend.
If you want to compare last year's top model to this year's top model, then you'll either have to apply hedonics or accept some overstatement of inflation of tech goods prices.
I don't know what BPP is doing, but they can't get around such problems.
Thanks for the comment. I do differ from you on one point: From my private correspondence and telephone discussions with Williams, I do think he really does attempt to calculate what the CPI would be under older methodologies. He just doesn't do it right, so the (roughly) constant 7 percent he adds to recent inflation is way too high.
Cheers but I'll stick to my harsh view. Adding a fixed adjustment to the current-methodology CPI does not count in my book as an attempt to estimate old-methodology CPI. To estimate a current CPI by old methodologies would require digging into the CPI's underlying data and differently recalculating it. He makes no effort to do that. He simply makes up a number that he claims represents the amount that new methodology reduces the inflation rate. The number is totally arbitrary, unsupported by and unsupportable by any science. You've done a great job laying out in detail what he does. Others have done the same before. Everyone can read over it and draw their own conclusions. My conclusion is: fraud, sham, joke. You're a nice man for using kinder words but honestly I don't think he deserves it.
I'll close out here with a little story to demonstrate just how puzzling inflation methodology decisions can be.
When sliced bread was introduced in the 1920s it was a luxury product that required expensive machinery to produce and sold for a premium over traditional bread.
Over time, sliced bread production technology got cheaper, sliced bread became the standard and traditional bread practically disappeared from mainstream grocery stores. Breadmakers also learned to cut costs with lower quality ingredients and methods that puffed bread with more air.
Then from the 1980s traditional bread made a revival. Since it uses more, higher-quality ingredients and requires expensive hand labor, it sells for a premium over sliced bread.
So how do you calculate the inflation of bread prices since the 1920s? Do you compare 1920s traditional bread prices to 2015 traditional bread prices, since they're essentially the same product? Or to 2015 sliced bread prices since they occupy the same mass-market niche? What do you think standard methodology would do?
Oh, and do you think 1970s Wonderbread should be treated as essentially the same product as 1920s sliced bread, or should a negative hedonic adjustment be made to account for its lower quality?
Your example raises several important points about hedonic quality adjustment. First of all, as I understand the method, it works only for products where there are quantitatively measurable characteristics like diagonal measure of a TV display or the age of a house, that can be regressed against price.Partly for that reason it is in principle inapplicable to a product like bread Another point is the subjectivity of quality–some people actually like Wonderbread and think the new artisan loaves are too crusty. A real-life example there would be beer. I think the new craft brews are a quality improvement, but my son-in-law continues to drink Miller's even if have paid for and stocked the fridge with both. A third point–and I don't know the answer to this–is whether the BLS ever makes adjustments for quality deterioration. Air travel comfort and convenience is an example Williams has raised.
Exactly. Conceptually, hedonic adjustment could and I think ideally should be applied to any changes in products, better or worse. But in practice that's both inexpedient and sometimes completely subjective (the more air of Wonderbread vs 1920s sliced bread seems to me in principle objective, though). You're right that as a matter of practice hedonic adjustments are applied to readily counted changes, such as screen measure or processor speed, or added standard features on cars that were previously paid options. Generally, mainly as a matter of expediency, hedonics are applied far less than they should be to truly accurately capture the inflation rate, unless you accept the crazy-net-troll notion that hedonics are fundamentally wrong and 1950s cars are just as good as today's.
The other point my story is making is a about the use of quality classes in inflation and real growth calculations. Again for expediency reasons, statisticians group products into basic and premium classes, and compare the prices of products within those classes to each other over time. A 1920s loaf of traditional bread, made with ingredients and methods that today we would consider premium, is treated as a basic class product. Sliced bread was treated as premium until its pricing became more or less indistinguishable from unsliced, and then it became the basic class of bread. No hedonic adjustments were applied to bread when ingredient quality was reduced. So modern inflation and real growth data compares 1920s traditional bread to modern cheap sliced bread, not to its real equivalent, which today is a premium product. The story is similar for much of food – including beer, as your premium microbrew is objectively very much like 1920s standard beer, but inflation data compares the latter to Miller. So long-term food inflation is very understated.
But there are also many ways that inflation overstates inflation by not making hedonic adjustments. In general it's a very complicated story. Inflation and real growth data are by nature very rough estimates. That's not because statisticians are sinister manipulators, it's because the concept of real value is flawed. All value is subjective.
In practice what we call real value is a jumbled mix of social conventions, sometimes representing the average subjective value, aka price, and sometimes trying to roughly represent a real value by looking at the contents of products and estimating how much of price difference is due to inflation and how much is due to product change.
Subjectively most people value microbrews and traditional bread more than Wonderbread and Miller, hence the latter are cheaper. Inflation data doesn't second-guess that, it treats them as different products. And inflation and real growth data relatively weight them according to the average subjective valuation aka price. For those kinds of comparisons we have objective data – prices – telling us what the average relative subjective valuations are.
But inflation and real growth data are in theory supposed to distinguish also between price changes over time and the relative values of product changes over time. And there is no objective data telling which is which. As you explain, the practice is actually to ignore the vast majority of minor product changes and assume the vast majority of price changes of similar products over time are entirely price changes with no real value change component. That lack of attempt to distinguish between real growth and inflation for most minor product changes is not something to celebrate.
I'm not saying inflation or real growth data are "wrong." I'm saying there is no correct way to measure them. Products and prices are always changing at the same time, and there is no practical way to distinguish most price changes and product quality changes from each other.
You have essentially written a nice guest post here. Thanks. Well said.
I see you make a wide spread assumption that doesn't hold water. i.e. that inflation drives interest rates. Sheer nonsense. And I can prove it.
– Everyone points to the 1970s to prove that inflation drives interest rates. But even in those days that story can be discarded. In the 1970s the USD weakened against the german mark. It meant that US inflation was (much) higher than german inflation. Based on that dynamic one would have expected that US interest rates went higher at a much higher pace than german rates. But that didn't happen. Long term rates in BOTH countries went up (more or less) in lockstep.
– From 2001 up to mid 2008 oil prices went up sevenfold and the CRB index tripled. But contrary to what happened in the 1970s long term rates US rates went down from ~6% to ~4.5% in the same timeframe.
– In the 2nd half of 2008 both oilprices & the CRB index went down by some 70%. If inflation/deflation drives interest rates then I would have expected that rates/yields came crashing down as well. Yes, rates went down in the 2nd half of 2008 but I use the words "drifted lower", NOT "crashed lower".
Based on the "30 year mortgage rate" versus "gold assumption" (see above) one could argue that CPI understates inflation and ShadowStats overstates inflation.
To be exact, the usual premise is that *expected* inflation drives interest rates. To refer to an example you use, from Dec 2000 to Dec 2008, 10-year expected inflation in the US fell from 2.8 percent to 1.5 percent, which is consistent with the decrease in rates that you note. You can find good estimates of inflation expectations over various time horizons here: https://www.clevelandfed.org/en/Our%20Research/In…
An excellent piece by the Esteemed, Mr Dolan!
I am sorry to go off topic but hear is a question to all..
Since the CPI is "supposed" to account for most of all consumers
cost of living, why are not governmental unit(s) taxes included
within the index?
After all, taxes consist of either the second or third largest part
of household budgets! Many of yous do know and have the answer
Thank you for any replies!
"For example, a can of tomato sauce that cost $.25 at Piggly Wiggly in 1982 cost $.79 at my local market in early 2015. Starting from the 1982 price, the CPI predicts that it should cost $.61 in 2015 while ShadowStats predicts that it should cost $2.64"
To clarify the above point, ShadowStats may, in fact, be correct about the grocery price "inflation" depending on where, in the country, one lives. To cite an example,the cost of living in Southern California, from groceries and gas to auto insurance and housing costs, tends to run higher than elsewhere in the country. So that can of tomato paste may very well cost ~$2 here and half that elsewhere. The cost of living doesn't impact all areas of the country equally, which is why it is possible to arrive at differing conclusions and yet both observations are, in essence, correct when one accounts for regional cost-of-living differences.
In the example provided, the CPI is low-balling the estimate (even a no-name can of tomato paste at a dollar store will be a dollar), the 79 cent figure provided in the example as "proof" that ShadowStats over-estimates the cost is wrong (for a lot of areas of the country that will not cover your "average" can of tomato paste) and ShadowStats, meanwhile, over-estimates the cost. In reality, an average of all three would be a better estimation: $1.35. A more useful measure of what it costs to live would account for regional differences, too.
First of all, it is not a “proof” but a common sense self-check. Do it yourself in your area. It is sort of fun. You can google old newspapers on line and look at the ads for any city in any year.
Second, the BLS does publish regional CPI figures every month, so those can be compared with Shadowstats. As far as I know there is no region where the rate of inflation is as fast as claimed by Shadowstats.
Third, be careful not to confuse differences in the regional cost of living with differences in regional inflation. Let’s say that you are right, that groceries, gas, and housing all cost 2X as much in CA as in KY. Five years later, groceries, gas, and housing probably will still cost 2X as high in CA as in KY. That is a difference in the price level. There would be no difference in the rate of inflation–that is, in the percentage annual rate of change of the cost of living.
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