Rent Prices Stickiness and the Latest CPI Data.

Fear of increasing inflation in the U.S. appear to be the trigger behind the market volatility of previous weeks. Recent gains in hourly compensation to workers have had analysts measuring the effect of wages on inflation. In turn, analysts began pondering changes in Fed’s monetary policy due to the apparent overheating path of the economy; which is believed to be mostly led by low unemployment rate and tight labor markets. Thus, within the broad measure of inflation, the piece that will help to complete the puzzle comes from housing market data. Although the item “Shelter” in Consumer Price Index was among the biggest increases for the month of January 2018, for technical definitions, its estimation does weight down the effect of housing prices over the CPI. Despite the strong argument on BLS’ imputation of Owner-Occupied Equivalent Rent, I consider relevant to take a closer look at the Shelter component of the CPI from a different perspective. That is, despite the apparent farfetched correlation between housing prices and market rents, it is worth visualizing how such correlation might hypothetically work and affect inflation. The first step in doing so is identifying the likely magnitude of the effect of house prices over the estimates and calculation of rent prices.

Given what we know so far about rent prices stickiness, Shelter cost estimation, and interest rates, the challenge in completing the puzzle consists of understanding the linking element between housing prices (which are considered capital goods instead of consumables) and inflation. Such link can be traced by looking at the relation between home prices and the price-to-rent ratio. In bridging the conceptual differences between capital goods (not measured in CPI) and consumables (measured in CPI) the Bureau of Labor Statistics forged a proxy for the amount a homeowner ought to pay if the house was rented instead: Owner-Occupied Equivalent Rent. This proxy hides the market value of the house by simply equaling nearby rent prices without controlling by house quality. Perhaps, Real Estate professional can shed light onto this matter.

The Setting Rent Prices by Brokers.

It is often said that rental prices do not move in the same direction as housing prices. Indeed, in an interview with Real Estate professional Hamilton Rodrigues from, he claimed that there is not such a relationship. Nonetheless, when asked about how he sets prices for newly rent properties, his answer hints at a link between housing prices and rent prices. Mr. Rodrigues’ estimates for rent prices equal either the average or the median of at least five “comparable” properties within a mile radius. The key word in Mr. Rodrigues statement is comparable. As a broker, he knows that rent prices go up if the value of the house goes up because of house improvements and remodeling. Those home improvements represent a deal-breaker from the observed stickiness of rent prices.

For the same reason, when a house gets an overhaul, one may expect a bump in rent price. That bump must reflect in CPI and inflation. I took Zillow’s data for December of 2017 for the fifty U.S. States, and run a simple linear OLS model. By modeling the Log of Price-to-Rent Ratio Index as a dependent outcome of housing prices -I believe- it will be feasible to infer an evident spillover of increasing house prices over current inflation expectations. The two independent variables are the Logs of House Price Index bottom tier and the Logs of House Prices Index top tier. I assume here that when a house gets an overhaul, it will switch from the bottom tier data set to the top tier data set.

Results and Conclusion.

The result table below shows the beta coefficients are consistent with what one might expect: the top tier index has a more substantial impact in the variation of the Price-to-Rent variable (estimated β₂= .12, and standardized β=.24, versus β=.06 for the Bottom tier). Hence, I would infer that overhauls might signal the link through which houses as a capital goods could affect consumption indexes (CPI and CEI). Once one has figured the effect of house prices on inflation, the picture of rising inflation nowadays will get clearer and more precise. By this means predictions on Fed tightening and accommodating policies will become more evident as well.

Unemployment √. Inflation √. So… what is the Fed worrying about?

Although the Federal Open Market Committee (hereafter FOMC) March’s meeting on monetary policy focused on what apparently was a disagreement over the timing for modifying the Federal Bonds interest rates, the minutes indicate that the disagreement is not only on timing issues but also on exchange rate challenges. Not only does the Fed struggle with when the best moment is to raise the rate, but also it grapples with the extent to which its policy decisions can reach. The FOMC current economic outlook and their consensus on the state of the U.S. economy have no room for doubts on domestic issues as it does for uncertainties on foreign markets. Thus, the minutes of the meeting held in Washington on March 15th – 16th 2016 unveils an understated intent for influencing global markets by stabilizing the U.S. currency. On one hand, both objectives of monetary policy seem accomplished regarding labor markets and inflation. On the other, the global deceleration is the only factor that concerns the Fed since it could have adverse spillovers on America. The most recent monetary policy meeting reveals a subtle attempt to stabilize the U.S dollar exchange rate at some level, thereby favoring American exports.

Unemployment rate √. Inflation rate √.

The institutional objective of the Federal Reserve Bank seems uncompromised these days. Economic activity is picking up overall, the labor market is at desired levels, and inflation seems somewhat under control. The confidence economists have right now starts by the U.S. Household Sector. Household spending looks healthy, and officials at the Bank are confident such spending will keep on buoying labor markets. As stated in the minutes, “strong expansion of household demand could result in rapid employment growth and overly tight resource utilization, particularly if productivity gains remained sluggish” (Page 6). Indeed, the labor market is showing strong gains in employment level which has made the unemployment rate to decrease down to 5.0 percent by the end of the first quarter of 2016.

Furthermore, FOMC understands the high levels of consumer confidence as a warranty for a sustained path for growth. The committee also pointed out that low gasoline prices are stimulating not only higher level of consumption but also motor vehicles sales. They know of the excellent situation of the relative high household wealth to income ratio. Otherwise, members of the Committee recognize that regions affected by oil prices are starting to struggle while business fixed investment shows signs of weakening. Nevertheless, the consensus among members of the Committee reflects an overall optimism in the resilience of the economy rather than a worrisome situation about the outlook.

By Catherine De Las Salas

By Catherine De Las Salas

The fear comes from overseas.

The transcripts, which were released on April 6th, 2016, show that  Fed officials the concerns stem from global economic and financial developments. The FOMC “saw foreign economic growth as likely to run at a somewhat slower pace than previously expected, a development that probably would further restrain growth in U.S. exports and tend to damp overall aggregate demand” (Pag. 8). They also flagged warnings on wider credit spreads on riskier corporate bonds. In sum, policymakers at the FOMC interpret the current lackluster global situation as a threat to the economic growth of the United States.

To discard choices.

Therefore, the fact that those two conditions overlap has made the Committee anxious to intervene in an arena that perhaps could be out of its reach. By keeping unmoved the interest rate of the federal bonds during March -and perhaps doing so until June-, the FOMC does not aim at stimulating investment domestically. Nor does it at controlling inflation. In fact, the policy choice reveals a subtle attempt for keeping the U.S dollar exchange rate stable overseas, thereby favoring American exports. The latter statement could be inferred from the minutes based on the Committee’s consensus on the state of the economy. First, U.S. labor markets are strong, and the Fed considers that the actual unemployment rate corresponds to the longer-run estimated rate. Second, inflation –either headline or core- are projected and expected to be on target. And third, domestic conditions are in general satisfactory. The only factor that remains risky is the rest of the world. Therefore, whatever action they took last March meeting could be interpreted as intended for influencing global markets.


A set of possible negative US economic shocks.

The puzzling aspect of recent data on inflation has been the deflation trajectory forged by oil prices. The index on energy by itself has fallen 28.7 percent over the year. Just in January 2016, the energy index declined 2.8 percent as gasoline index did so by 4.8 percent during the same month. The energy index has been dragging down the computational results of inflation severely to the extent that it makes the entire index hard to interpret. The truth of the matter is that oil prices’ downward trend has started, at least, to cast doubts on whether the offset in the overall inflation measure represents a relocation of resources within industries, or the index is masking a worrisome situation of an entire economic sector. In other words, with the decline in energy prices, could energy-related companies lead the US economy toward a slowdown?

By Catherine De Las Salas

By Catherine De Las Salas

Could energy-related companies lead the US economy toward a slowdown?

Current conditions and economic outlook in the United States have economists looking for signs of economic overheating by looking into the theoretical relation between unemployment and inflation. However, following the economic theory may work as a perilous distraction under the present situation. In theory, when the unemployment rate becomes very small, employers increase their salaries which in turn augments consumer spending. Such an increase in consumer spending leads to higher level of prices as the demand for goods surges. Then, given that news of unemployment have been certainly positive for the last six months, economists are cautiously focusing on inflation to determine whether or not the economy is overheating. This logic of analysis might generate bias as it derives conclusions from an arithmetic average on the consumer price index.

We are left with Monetary shocks, oil shocks, or a deterioration of global economic conditions:

More precisely, the fact that energy index offsets currently core inflation keeps economists in their theory comfort zone by ignoring oil sector volatility. On one hand, they see households in a proper position as their liabilities have declined by 12 percent during the so-called “Great Deleveraging” period. Specifically, economists at the Federal Reserve Bank of New York claim that this very fact makes the household sector more resilient to absorb shocks, which seems reasonable. Also, they stress that the financial sector appears strong as the sector counts with larger liquidity buffer now than in preceding years. Further, Fed’s officials see good news in regards to the labor market and unemployment rate, which has dropped to a national average of 4.9 percent –also positive. On the fiscal front, it seems clear to most of the people that events such as the sequester of 2013 are unlikely to happen in the foreseeable future. Technology shock-wise, no negative shocks appear to linger in the horizon. Therefore, by discarding the set of possibilities on surprising negative economic shocks, the only ones lingering are either monetary shocks, oil shocks, or a deterioration of global economic conditions.

Now, if America trusts their monetary authorities, then the only standing threats are oil shocks and an international economic slowdown. Red flags have been waved during the last six months stressing the levels of debt of petroleum companies. Some estimates coming from point to numbers of around U$200 billion debt that may be approaching default soon. It is worth remembering that in the midst of the Great Recession in 2008 losses on mortgages were around U$300 billion. Although acknowledging the difference between housing sector’s debt and oil companies’ debt is a must for any analysis, the risk is somewhat similar at least regarding magnitude.

“Core” inflation might be reflecting pressures solely generated by retailers.

Data on both unemployment and prices have monetary policy analysts wondering whether or not the US supply side of the economy is heading towards overheating. Thus far, indicators on industrial production and capacity utilization show there is still room for the economy to advance at a good pace without risking too many resources. Such indicators are produced and tracked by the monetary authority of the nation, so they have particular relevance for every analysis. However, there still are data on both unemployment and prices to help out with the diagnosis of the actual economic situation. On one hand, 92% of the metropolitan areas in the nation experienced lower unemployment rates in July 2015 than a year earlier, while only 20 metro areas showed higher rates. On the other, measure of the “core” inflation, which isolates energy and foods price volatility, reaches 1.8 percent change from the first quarter of 2015.

So, if higher production leads to lower unemployment, and the latter in turn leads to higher prices, then the easiest way to identify whether or not an economy is overheating is by analyzing to what extent prices changes are pushed up by falling rates of unemployment. This far of 2015, both conditions are met apparently. Unemployment rates are indeed falling; therefore, it could mean production is moving up. Then, what is a stake currently is to clarify whether or not US production is exceeding its capacity. Again, by looking at capacity indexes, it seems not to be the case right now. But, it is better to make sure it is not happening and thereby ruling out any alternative possibility.

Many econometric methods will help analysts to achieve valuable conclusions.

Perhaps digging into the price setting relation through regressing real wages on profits may yield some clues about the current situation. However, econometric models would severely hide the actual magnitude of oil and energy price volatility. Therefore, a rather quicker alternative lives in qualitative data. In other words, if analysts would like to know whether or not companies would transfer increasing labor costs onto the customers via price increase, what would the answers be? looked at one of the state-level surveys in which such a question was included. The Texas Manufacturing Survey, which is conducted by the Dallas Fed, inquired among 114 Texas manufactures the following question. “If the labor costs are increasing, are you passing the costs on to customers in the way of price increases?” The survey answers were collected on August 18th through the 26th.

Here is what the study showed.

By sectors, surveyed retailers appear be the only ones prompted to transfer increasing labor costs to customers via price increase. Although very tight, 43.9 percent of the answers indicated that retailers would rise price as an outcome of increasing labor costs, whereas 41.5 would not. The Texas service sector respondents indicated that they would not do so by 54.5. Likewise, manufacturers rejected the possibility by 52.4 percent and considered positively by 35.7 percent. Below are the charts of which all used Texas Manufacturing Survey Data.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Although it is not feasible to extrapolate survey’s results onto the entire US economy, Texas’ has a particular significance for any current economic analysis. Indeed, Texas’ economy comprises a large share of oil related business, which is precisely the industry that brought this puzzle in the first place. Thus, it seems somewhat clear to conclude that following the Dallas survey, the economy might not be overheating currently.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

So, what does these data tell economists about the US economy?

Although some would answer it says little because of its sample size and geographic limits, and its business size aggregation, there are some hints within the survey. First, it could be said that companies are currently absorbing the cost of growing, which might indicate that they are indeed venturing and the economy is expanding. So far so good. The concerns, though, stem from the speed of such expansion, which is hard to identify by using these data. But again, it is important to check Federal Reserve Data on industrial production and capacity utilization, which would yield some confidence against overheating. Second, although business size matters for determining whether or not increasing labor costs can be transferred to the customer via prices, the fact that retailers stand out in the survey must mean something for analysts. According to these data, retail appears to be the most sensitive sector right now; therefore, the 1.8 “core” inflation might be reflecting inflationary pressures solely generated by retailers.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.

Texas Manufacturing Survey. Dallas Fed Aug. 2015.


The Dallas Fed conducts the Texas Manufacturing Outlook Survey monthly to obtain a timely assessment of the state’s factory activity. Data were collected Aug. 18–26, and 114 Texas manufacturers responded to the survey. Firms are asked whether output, employment, orders, prices and other indicators increased, decreased or remained unchanged over the previous month.



“Core” inflation rate will have huge influence on monetary policy next month.

Second Estimates for real GDP growth in the United States indicate that the economy grew at 3.7 percent during the second quarter of 2015 after correcting by price change. The report from the Bureau of Economic Analysis informs that the change mainly derived from positive contribution of consumer spending, exports, and spending of state and local governments. These increases are said to have been offset by a deceleration in private inventory investment, federal government investment, and residential fixed investment. The revised figure for first quarter of 2015 went up from -0.7 percent to 0.6 percent.

Besides real GDP calculations stand the estimates for prices changes in goods and purchases made by American residents that the Bureau of Economic Analysis (BEA) does simultaneously to the calculations made by the Bureau of Labor Statistics (BLS). In this regard, this time around the second quarter, prices had a positive growth of roughly 1.6 percent, which the BEA reports was derived from an increase in both consumer prices, and prices paid by local and state governments. Please bear in mind that, in the first quarter of 2015, prices were said to have dragged down the GDP numbers since the index decreased by roughly 1.1 percent change.

H&M Store in Broadway NYC. By Catherine De Las Salas. Summer 2015.

H&M Store in Broadway NYC. By Catherine De Las Salas. Summer 2015.

These price changes are actually good news for the Federal Reserve System for whom a moderate upswing in inflation helps them to achieve their yearly monetary goal of 2.0 percent inflation rate. And for those of whom like to make economic forecast, these figures mount onto their analysis for determining whether or not the Federal Reserve will increase interest rates in September. So, although real GDP measures are certainly corrected for price changes, the BEA’s price index will -on its own- have huge influence on monetary policy options for the months to come.

Thus, relevant data nowadays stem from BEA’s “core” inflation rate, which is to say price change without food prices and energy prices. Indeed, when figures isolate energy and foods volatility, the measure of inflation reaches 1.8 percent change from the first quarter of 2015. These changes in prices and output rightly affect the wallet of American residents. Price changes, plus increases in output -which reflect decreases in unemployment rate- may take consumer and producers to edge up their spending, which was one of the factor behind positive change in real GDP growth as mentioned above. Then, whenever spending tends to accelerate beyond its capacity the Federal Reserve reacts with an increase in interests rates. Even though one could argue that such is not currently the case, given that data on capacity utilization clearly shows that the American Economy has room to further spending, the BEA’s “core” inflation will be the measure that could possible make Federal Reserve Officials think twice about interest rates.

So, the puzzle about what the Federal Reserve will end up doing next Federal Open Market Committee meeting is fourfold, and it will derive from the different sources of data: first, price change data from BEA, which BEA claims to be way more “accurate” than BLS’. GDP growth from BEA, which is calculated by correcting price changes with their own price index. Price change from BLS, which may vary from BEA’s calculations. And capacity utilization from the Federal Reserve, which is whom finally decides on interest rates changes.

Follow up on US Construction Industry Data.

Follow up on US Construction Industry Data.

At the beginning of the summer of 2015, both labor statistics on employment levels and US Gross Domestic Product showed a slowdown on job creation coming from construction related activities. Given that the summer represents a time window for developers to build fast thanks to good weather conditions, economists always expect summer job increases to largely stem from construction. However, it was not the case for the summer of 2015, which alerted analysts to look cautiously at construction investment. On the first week of July, poked on construction investment by looking at statistics on Construction Put in Place (US Census Bureau) for the month of May of 2015, as a way to find out whether or not construction investments had slowed-down effectively. Data on such a metric revealed no statistically significant change, which accurately corresponded to data reflecting job creation from the US Bureau of Labor Statistics, and data on GDP growth. Now that the summer is almost gone, it is worth looking at Residential Construction to either dissipate or collect more concerns.
July’s Construction Data from the US Census Bureau and the US Housing Department.

On annual basis increases were significant, but on monthly basis they were not so much. For instance, projected economic activity on residential construction increased significantly in aggregate terms for Approved Building Permits, Housing Starts, and Housing Completion, for the month of July 2015. On one hand, and in spite of a decrease from the previous month of June, plans to build housing units jumped 7.5% when compared to the month of July 2014. Likewise, Housing Starts augmented by 10% in July 2015 when compared to the same month of 2014. In terms of Housing Completion, which shows how fast contractors wanted to finish their work during the summer, privately-owned completed units skyrocketed by 14.6% in July 2015 vis-à-vis July 2014.

Construction summer statistics by region.

Regionally speaking, so far this summer the South has shown decent pace of Housing Completion growth. But, it is not the same case everywhere else. In the West region, privately-owned Housing units completed has declined steadily since summer 2015 started. In the Midwest, although July represented a rebound for the statistic, the numbers dropped to winter season levels. Currently the rate of Completed units is a bit higher than it was a year before though. On the other hand, the Northeast region bounced back after a big drop in June 2015. The graph below shows the trajectory for New Privately-owned Housing units completed, in which the blue line represents the Northeast region. The region’s statistic is back at the level it was one year before.

Privately-Owned Housing Units.

Privately-Owned Housing Units.

Therefore, coming up with a set of conclusions, to determine whether or not housing is holding back economic growth and job creation, is really hard at this point of the year. Having seen what we have observed so far, it is tough to adventure hardcore statements. However, except by the South region, Construction has experienced a slow-down all over the United States during the summer of 2015, which is reflects on both indicators, jobs and GDP Growth.

United States Housing Units Completed on July 2015.

United States Housing Units Completed on July 2015.


Northeast Housing Units Completed on July 2015.

Northeast Housing Units Completed on July 2015.


Midwest Housing Units Completed on July 2015.

Midwest Housing Units Completed on July 2015.


West region Housing Units Completed on July 2015

West region Housing Units Completed on July 2015


South Region Housing Units Completed on July 2015.

South Region Housing Units Completed on July 2015.



It’s time to look at price changes without accounting for oil price effect.

After a year of declining crude oil prices which forged price spillovers all over the US economy, it is time for economists to look at price changes without accounting for the petrol effect. So far, 2015 has been a year in which dropping gas prices have affected almost every index from the US Bureau of Labor Statistics. Indeed, the Consumer Price Index started to decline since summer 2014 when the price of crude oil marked roughly U$107 per barrel. Since then, the Consumer Price Index declined continuously until January 2015. Likewise, the Producer Price Index, which behaves similarly, followed the decline until the beginning of the current year. However, both indexes started to increase from negative territory to positive areas up to 0.4 percent in July 2015, which is particularly the case of Producer Price Index.

So, if economists believed that oil prices accounted vastly for the overall decrease on Inflation, then, what is going on now with the hike in Indexes since oil prices are still low? The clear answer is that inflation has begun to bounce back.

Consumer Price Index and Producer Price Index

Consumer Price Index and Producer Price Index

Price statistics have begun to move wider than they did before the summer of 2014:

Generally speaking, data in Price Indexes show that price statistics have begun to move wider than they did before the summer of 2014. This trend marks a year of some sort of stagnation in Indexes that can be traced back to the spring of 2013. This period between summer 2013 and the summer 2014 looks almost flat for both indexes. Right after such a flat period, oil prices started to drop and so did both indexes. However, oil prices are still at record lows whereas the indexes started to rebound.

Therefore, it is time to scrutinize indexes in order to establish to what extent oil prices are still dragging down arithmetically consumer prices, and at the same time looking at the origin of current monetary pressures. By isolating prices from oil effect, several conclusions on prices can be drawn. First, inflation rate without accounting for energy prices, is higher than what got reported officially. Second, prices for “guest rooms”, which is to say tourism, may indicate people are spending conspicuously. And third, almost everything else -independent from oil- is increasing.

Final Demand Index less Foods and Energy.

Final Demand Index less Foods and Energy.

For instance, “in July, a 3.1 percent advance in margins for building materials, paint, and hardware wholesaling was a major factor in the increase in prices for services for intermediate demand. Furthermore, “the indexes for processed goods and feeds and for processed materials less food and energy moved up 0.9 percent and 0.1 percent respectively”, reported the US Bureau of Labor Statistics last August 14th 2015.

More in detail and in regards to final demand services, “over 40 percent of July increase in the index for final demand services is attributable to prices for “guest room rental”, which jumped 9.9 percent”. Clearly, prices are moving up whenever oil effect gets removed from calculations.

Expect an increase in interest rates:

US monetary authorities should be aware of these recent trends for sure. Therefore, it is reasonable to expect an increase in interest rates in order to curb down excessive consumer spending, particularly whatever spending gets associated with “guest room rentals”. Nonetheless, although this conclusion is drawn exclusively from the point of view of price stability, such a thing happens to be the main mandate of central banks.

Do Workers on Unemployment Insurance make Other Workers’ Income Worst?

Economists like to think that wages are set depending upon two basic factors plus a “catchall” variable. The two basic factors are expected price level and unemployment rate. The “catchall” variable stands for all other overlooked factors affecting wage. The way in which the relationship is established by labor theory is that expected price level affects wage determination positively (since the economy has not experienced deflation effect systematically); and, unemployment does it negatively (supposedly, given that workers compete for jobs, employers take advantage of it through price-taking behavior). All other factors affecting wages are assumed to be positive.

Among those all other factors –which are believed to affect positively wage levels- is the Unemployment Insurance benefit. However, depending upon ideology, Unemployment Insurance benefits may be interpreted as affecting wage determination either positively, or affecting wages negatively. On one side, Unemployment Insurance may affect upward wages given that it sums up into the so-called reserve salary, which is the minimum amount of money that makes a person indifferent to the choice between working and not working. In other words, if a person has Unemployment Insurance for any given dollar amount, why would that person work for less that such a figure? The flip side of the coin is that, if Unemployment Insurance contributes to keep people from work, then the unemployment rate goes up due to the UI, thereby pushing down the wages. At first glance, analysts might be tempted to think that those two forces cancel off each other. There is where data becomes important in determining the real breadth of those factors without binding to any ideology.

By Catherine De Las Salas

By Catherine De Las Salas.

By the way, in case you have not noticed it yet, right wing politicians tend to believe that UI pressures upwards wages thereby increasing production costs. Therefore, right wing politicians believe that such a pressure constraints hiring within the United States affecting negatively production and forcing employers to find cheap labor elsewhere overseas.

Managers play a roll either in cutting or increasing wages:

It is important to note that wage laws create downward wage rigidity, which prevents managers to lower nominal salaries. However, and despite of such a rigidity, administrators may manage to cut ‘earnings’ by lowering workloads. Therefore, looking at measures such as hourly wage, or minimum legal wage does not capture the reality of compensation. Instead, looking at ‘earnings’ might give a hint about the variance created by unemployment insurance, unemployment rate and inflation.

The model:

So, the logic goes as follows: wage levels are an outcome of unemployment rate (negatively); plus, unemployment benefits (positively); plus, expected price level (positively). In other words, wage setting gets affected by those three factors since a manager ‘virtually’ would adjust her payroll based on how easy is for her to either hire or fire an employee, and how enthusiastic she is to increase or decrease the employee workload.

Thus, the statistical model would look like the following:


Where y is the dependent variable Average weekly earnings for November 1980 to November 2014; x1 represents Unemployment Rate at its annual average; x2 represents Unemployment Insurance Rate for November’s weeks seasonally adjusted average; x3 stands for inflation rate at its annual average.

Data and method:

Thus, I took data on three variables: Average weekly earnings for the month of November starting from 1980 through 2014. These data, taken from the U.S. Bureau of Labor Statistics (BLS), were adjusted by the average inflation rate of the correspondent year. The second variable is year average inflation rate from 1980 to 2014, taken also from BLS too. I use Inflation Rate as a proxy for the “expected price level”. The third variable is the November’s Unemployment Insurance rate from 1980 to 2014, which was taken from the Unemployment Insurance Division at the U.S. Department of Labor. I chose data on November series given that this month’s Average weekly earnings has the greatest standard deviation among all other months.

Ordinary Least Square Method was used to run the multiple regression.


Data for the month of November, starting 1980 through 2014, show that Unemployment Insurance Rate could have a negative effect on average weekly earnings for Americans. Apparently, the statistical relation of the data is negative. The actual estimated coefficient for these data points out toward a figure of (+/-) $123 less for U.S. Worker’s average weekly earnings per each percent point increase in Unemployment Insurance Rate. In other words, the greater the share of people collecting Unemployment Insurance, the lower the average weekly earnings of U.S. workers. One limitation of the regression model is that it only captures the employees effect of the variable, the model is not intended to explain costs of employers. In such a case the dependent variable should be some variable capable of capturing employer’s labor costs. The statistical significance for the effect of Unemployment Insurance on November average weekly earnings data is at 95%.

Furthermore, data also show that inflation rate (proxy for “expected price level”) actually works against average weekly earnings. The estimated coefficient for the months of November is (+/-) 28 dollars less for the average paycheck. The statistical significance for the effect of Inflation Rate on November average weekly earnings data is at 95%.

Finally, the Unemployment Rate shows a positive effect on average weekly earnings indicating that, per each percent point increase in Unemployment Rate, average weekly earnings increases by an estimated figure of (+/-) 49 dollars. The statistical significance for the effect of Unemployment Rate on November average weekly earnings data is at 90%.

Regression output table:


Where 0.8 percent US’ Inflation came from?

The Consumer Price Index increased 0.8 percent from December 2013 through December 2014, the US Bureau of Labor Statistics reported on January 16th 2015. This small increase represents the second-smallest December-December increase since 1965. Energy prices in general dragged the average down with a sharp drop in Gasoline prices of about -21 percent change over the year, though Energy in general increased a little more than 10 percent. Indexes for both Food and Rent rose 3.4 percent during 2014. The Consumer Price Index is a measure of the average change in prices of goods and services purchased on a daily basis by US households. Prices are collected monthly in 87 urban areas from roughly 26,000 retail stores and 4,000 housing units. The US Federal Reserve Bank aims at a 2 percent increase in the Consumer Price Index.

Although Consumer Price Index is a good statistical measure, its own aggregation as an average may lead to wrong conclusions. A more detailed approach on price changes can be done by looking at each good’s and service’s average price change. The graph below shows average price changes for the major 30 goods and services, which were selected by their weight relevance in the CPI calculation (higher than 1.0). These are the goods and services that matter the most for a day-to-day household tracking expenses.

Major 30 CPI

As the graph shows the biggest drop in prices were on energy related goods and services. Gasoline prices dropped roughly -21 percent through the year. Wireless telephone services also decreased its price by -4 percent approximately. Women’s and girl’s apparel fell -3.6 percent along 2014, whereas Apparel in general did so by -2 percent. Read the entire BLS’ report.

On the other hand, the index that increased the most during 2014 was the one related to protein food. Meats, poultry, fish and eggs increased their prices by 9.2 percent nearly, whereas Food in general increased 3.4 percent. Prescription drugs were up 6.4 percent. Rent of primary residencies were augmented by 3.4 percent.

An even more detailed information about Price changes by goods and services can be found in the list below. The list is organized by alphabetical order:

Expenditure category followed by CPI percent change.
Admission to movies, theaters, and concerts 0.4
Admission to sporting events 2.7
Admissions 0.7
Airline fare -4.7
Alcoholic beverages 1.3
Alcoholic beverages at home 0.7
Alcoholic beverages away from home 2.2
All items 0.8
All items less food and energy 1.6
Apparel -2
Apparel services other than laundry and dry cleaning 1.8
Apples -2.3
Appliances -5.2
Audio discs, tapes and other media -3.6
Audio equipment -7.3
Automobile service clubs -0.4
Baby food 2.1
Bacon and related products -1
Bacon, breakfast sausage, and related products 2.4
Bakery products 0.9
Bananas -0.7
Bedroom furniture -2.4
Beef and veal 18.7
Beer, ale, and other malt beverages at home 0.7
Beer, ale, and other malt beverages away from home 2.1
Beverage materials including coffee and tea 2.6
Boys’ and girls’ footwear 6.1
Boys’ apparel -2.7
Bread 1.2
Bread other than white 0.8
Breakfast cereal 1.3
Breakfast sausage and related products 7.3
Butter 22.5
Butter and margarine 11.6
Cable and satellite television and radio service 2.2
Cakes, cupcakes, and cookies 0.6
Candy and chewing gum 1.8
Canned fruits 0.5
Canned fruits and vegetables -0.2
Canned vegetables 0
Car and truck rental 0
Carbonated drinks 1.4
Care of invalids and elderly at home 1.8
Cereals and bakery products 0.5
Cereals and cereal products -0.3
Checking account and other bank services 0.1
Cheese and related products 8.2
Chicken 2.1
Child care and nursery school 2.2
Cigarettes 3.1
Citrus fruits 5.4
Clocks, lamps, and decorator items -5.8
Club dues and fees for participant sports and group exercises 0.4
Coffee 3.6
College textbooks 5
College tuition and fees 3.4
Commodities less food and energy commodities -0.8
Computer software and accessories -1.2
Cookies -0.2
Cosmetics, perfume, bath, nail preparations and implements 1
Crackers, bread, and cracker products 1
Dairy and related products 5.3
Delivery services 1.1
Dental services 1.8
Dishes and flatware -6.7
Distilled spirits at home 0.9
Distilled spirits away from home 2.2
Distilled spirits, excluding whiskey, at home 0.8
Domestic services 1.2
Dried beans, peas, and lentils 4.6
Education and communication commodities -4.9
Education and communication services 0.9
Educational books and supplies 4.6
Eggs 10.7
Electricity 3.1
Elementary and high school tuition and fees 4
Energy -10.6
Energy commodities -20.5
Energy services 3.7
Eyeglasses and eye care 2.6
Fats and oils 1
Fees for lessons or instructions 2
Film and photographic supplies 23.4
Film processing 3.8
Financial services 3.5
Fish and seafood 4.3
Floor coverings 0.8
Flour and prepared flour mixes -1.9
Food 3.4
Food at elementary and secondary schools 2.3
Food at employee sites and schools 1.8
Food at home 3.7
Food away from home 3
Food from vending machines and mobile vendors 0.5
Footwear 2.8
Frankfurters 12.1
Fresh and frozen chicken parts 1.6
Fresh biscuits, rolls, muffins 1.9
Fresh cakes and cupcakes 1.5
Fresh fish and seafood 5.6
Fresh fruits 3.6
Fresh fruits and vegetables 4.1
Fresh milk other than whole 4.1
Fresh sweetrolls, coffeecakes, doughnuts 0.6
Fresh vegetables 4.6
Fresh whole chicken 3
Fresh whole milk 5.2
Frozen and freeze dried prepared foods 1.9
Frozen and refrigerated bakery products, pies, tarts, turnovers -0.5
Frozen fish and seafood 5.2
Frozen fruits and vegetables 1.5
Frozen noncarbonated juices and drinks 2.3
Frozen vegetables 0.9
Fruits and vegetables 3.2
Fuel oil -19.1
Fuel oil and other fuels -13.7
Full service meals and snacks 3.1
Funeral expenses 1.2
Furniture and bedding -1.6
Garbage and trash collection 1.4
Gardening and lawncare services 4.4
Gasoline (all types) -21
Gasoline, unleaded midgrade -19.6
Gasoline, unleaded premium -18.3
Gasoline, unleaded regular -21.6
Girls’ apparel -4
Hair, dental, shaving, and miscellaneous personal care products -0.3
Haircuts and other personal care services 1.5
Ham 13.1
Ham, excluding canned 14.4
Health insurance -0.5
Hospital and related services 4.5
Hospital services 4.9
Household cleaning products -0.9
Household furnishings and supplies -1.9
Household operations 2.8
Household paper products -0.7
Housekeeping supplies -0.8
Housing at school, excluding board 2.7
Ice cream and related products 3.5
Indoor plants and flowers 1.9
Infants’ and toddlers’ apparel 0.4
Infants’ equipment -0.7
Infants’ furniture
Information technology commodities -9
Inpatient hospital services 5.5
Instant and freeze dried coffee 0.2
Intercity bus fare
Intercity train fare 3.8
Internet services and electronic information providers 1.6
Intracity mass transit 1.1
Intracity transportation 1.1
Jewelry -5.1
Jewelry and watches -4.3
Juices and nonalcoholic drinks 0.1
Lamb and mutton 3.2
Lamb and organ meats 8.8
Land-line telephone services 1.8
Laundry and dry cleaning services 2.2
Laundry equipment -7.4
Leased cars and trucks -0.1
Legal services 1.4
Lettuce 4.4
Limited service meals and snacks 3.2
Living room, kitchen, and dining room furniture -1.9
Lodging away from home 6.3
Lunchmeats 5.8
Major appliances -6.9
Margarine 2.6
Meats 12.7
Meats, poultry, and fish 9.1
Meats, poultry, fish, and eggs 9.2
Medical care commodities 4.8
Medical care services 2.4
Medical equipment and supplies 0.9
Medicinal drugs 5
Men’s and boys’ apparel -3
Men’s apparel -3
Men’s footwear 1.8
Men’s furnishings -2.4
Men’s pants and shorts 1.1
Men’s shirts and sweaters -4.5
Men’s suits, sport coats, and outerwear -7.1
Milk 4.3
Miscellaneous household products -0.7
Miscellaneous personal goods -0.6
Miscellaneous personal services 2.1
Motor fuel -20.8
Motor oil, coolant, and fluids 2.4
Motor vehicle body work 2.1
Motor vehicle fees 0.3
Motor vehicle insurance 4.7
Motor vehicle maintenance and repair 2.1
Motor vehicle maintenance and servicing 2.2
Motor vehicle parts and equipment -0.7
Motor vehicle repair 2
Moving, storage, freight expense 2.1
Music instruments and accessories 2.4
New cars -0.1
New cars and trucks 0.6
New trucks 1.3
New vehicles 0.5
Newspapers and magazines 4.8
Nonalcoholic beverages and beverage materials 0.7
Nonelectric cookware and tableware -3.7
Nonfrozen noncarbonated juices and drinks -1
Nonprescription drugs -0.2
Nursing homes and adult day services 2.9
Olives, pickles, relishes 0.2
Oranges, including tangerines 3.7
Other appliances -3.1
Other bakery products 0.4
Other beverage materials including tea 1
Other condiments 1.8
Other dairy and related products 3.7
Other fats and oils including peanut butter -2.5
Other food at home 1.5
Other food away from home 2
Other foods 1.7
Other fresh fruits 6.2
Other fresh vegetables 2.3
Other furniture 0.8
Other goods 1.3
Other household equipment and furnishings -3.9
Other intercity transportation -0.7
Other linens -5.2
Other lodging away from home including hotels and motels 7.3
Other meats 7.4
Other miscellaneous foods 1.6
Other motor fuels -11.9
Other personal services 1.9
Other pork including roasts and picnics 12.5
Other poultry including turkey -0.5
Other processed fruits and vegetables including dried 0.2
Other recreation services 0.8
Other recreational goods -3.8
Other sweets -0.2
Other video equipment -0.8
Outdoor equipment and supplies -0.3
Outpatient hospital services 4.5
Owners’ equivalent rent of primary residence 2.6
Owners’ equivalent rent of residences 2.6
Parking and other fees 2.2
Parking fees and tolls 2.7
Peanut butter -3.6
Personal care products 0.3
Personal care services 1.5
Personal computers and peripheral equipment -10.5
Pet food 0.4
Pet services 1.8
Pet services including veterinary 2.7
Pets and pet products 0.3
Photographer fees 1.1
Photographers and film processing 2.2
Photographic equipment -6.1
Photographic equipment and supplies -2.2
Physicians’ services 1.5
Pork 8.2
Pork chops 10.1
Postage 4.1
Postage and delivery services 3.8
Potatoes -1.8
Poultry 1.6
Prepared salads 3.9
Prescription drugs 6.4
Processed fish and seafood 3
Processed fruits and vegetables 0.4
Professional services 1.7
Propane, kerosene, and firewood -4.6
Public transportation -2.9
Purchase of pets, pet supplies, accessories 0.4
Recreation commodities -2.6
Recreation services 1.5
Recreational books -0.9
Recreational reading materials 2.2
Rent of primary residence 3.4
Rent of shelter 2.9
Rental of video or audio discs and other media 1.4
Repair of household items 4
Rice -2.8
Rice, pasta, cornmeal -2.1
Roasted coffee 4.2
Salad dressing -4.3
Salt and other seasonings and spices 4.8
Sauces and gravies 1.7
Services by other medical professionals 2
Services less energy services 2.4
Sewing machines, fabric and supplies 0.1
Shelf stable fish and seafood 1.3
Shelter 2.9
Ship fare -1.9
Snacks 1.8
Soups -0.6
Spices, seasonings, condiments, sauces 2.2
Sporting goods -2.2
Sports equipment -3.1
Sports vehicles including bicycles -1.1
State motor vehicle registration and license fees -1
Stationery, stationery supplies, gift wrap 0
Sugar and artificial sweeteners 0.2
Sugar and sweets 1.1
Tax return preparation and other accounting fees 6.1
Technical and business school tuition and fees 1.8
Telephone hardware, calculators, and other consumer information items -9.9
Telephone services -2.1
Televisions -16.7
Tenants’ and household insurance 5.6
Tires -1.9
Tobacco and smoking products 3
Tobacco products other than cigarettes 1.4
Tomatoes 16.5
Tools, hardware and supplies 0.8
Tools, hardware, outdoor equipment and supplies 0.1
Toys -5.4
Toys, games, hobbies and playground equipment -2.9
Transportation commodities less motor fuel -0.9
Transportation services 1.7
Tuition, other school fees, and childcare 3.2
Uncooked beef roasts 20.6
Uncooked beef steaks 16
Uncooked ground beef 19.2
Uncooked other beef and veal 24
Used cars and trucks -4.2
Utility (piped) gas service 5.8
Vehicle accessories other than tires 1.7
Vehicle parts and equipment other than tires 1.5
Veterinarian services 2.9
Video and audio products -10.5
Video and audio services 1.8
Video discs and other media -6.3
Video discs and other media, including rental of video and audio -3
Watches -1
Water and sewer and trash collection services 4.6
Water and sewerage maintenance 5.6
Whiskey at home 1.5
White bread 0.9
Window and floor coverings and other linens -3.6
Window coverings -2.3
Wine at home 0.6
Wine away from home 2
Wireless telephone services -4
Women’s and girls’ apparel -3.6
Women’s apparel -3.5
Women’s dresses 1.6
Women’s footwear 1.7
Women’s outerwear 3.6
Women’s suits and separates -8.2
Women’s underwear, nightwear, sportswear and accessories -0.3