The overuse of the word “Strong” in economic news.

The US economy added 228,000 new jobs in November of 2017 and analysts rush to assess the state of the economy as “STRONG.” Although the job reports are indeed good indicators of the performance of the US economy, one should not simplify the job report as the snapshot of the economy that allows for those “strong” conclusions by and in itself. In this post, I show that despite the existence of a cointegration vector between unemployment rate data and the word-count of the word “Strong” in the Beige Book, journalists indeed overuse the word “Strong” in headlines. Although interpreting cointegration as elasticity goes beyond the scope of this post, I think that by looking at the cointegration relation it is safe to conclude that the current word count does not reflect the “strong” picture showed by the media, but somewhat more moderate economic conditions.

To start, let me go back to the first week of December of 2017. Back then, news outlets had headlines abusing the word “strong.” Some examples came from major newspapers in the US such as the New York Times, Reuters, CNN and the Washington Post. The following excerpts are just a sample of the narrative seen those days:

“The American economy continues its strong performance” (CNN Money).

“The economy’s vital signs are stronger than they have been in years” (NY Times).

“Strong US job growth in November bolsters economy’s outlook” (Reuters).

“These are really strong numbers, which is pretty exciting…” (Washington Post).

Getting to know what is happening in the economy challenges economists’ wisdom. Researchers are constrained by epistemological limits of data and reality, and so are journalists. To understand economic conditions, researchers utilize both quantitative and qualitative data while journalists focus on qualitative most of the times. Regarding qualitative data, the Beige Book collects anecdotes and qualitative assessments from the twelve regional banks of the Federal Reserve system that may help news outlets to gauge news statements and headlines. The Fed studies business leaders, bank employees, among other economic agents to gather information about the current conditions of the US economy. As a Researcher, I counted the number of times the word “strong” shows up in the Beige Book starting back in 2006. The results are plotted as follow:

If I were going to identify a correlation between the word count of “strong” and its relation to the unemployment rate, it would be very hard to do so by plotting the two lines simultaneously. Most of the times, when simple correlations are plotted, the dots show any relation between the two variables. However, in this case, cointegration goes a little deeper into the explanation. The graph below shows how the logs of both variables behave contemporarily over time. They both decrease during the Great Recession as well as they increase right after the crisis started to end. However, more recently both variables began to divert from each other, which makes it difficult to interpret, at least in the short run.

Qualitative data hold some clues in this case. Indeed, the plot shows a decreasing trend in the use of the word within the Beige Book. In other terms, as journalists increase its use in headlines and news articles, economists at the Federal Reserve Bank decrease the use of the word “strong”. If I were going to state causality from one variable to the other, first I would link the word “strong” to some optimism for expected economic outcomes. Thereby, one should expect a decrease in the unemployment rate as the use of the word “strong” increases. This is a classic Keynesian perspective of the unemployment rate. Such relation of causality might constitute the cointegration equation that the cointegration test identifies in the output tables below. In other words, the more you read “strong,” the more employers hire. By running a cointegration test, I can show that both variables are cointegrated over time. That is, there is a long-term relationship between both variables (both are I(1)). The cointegration test shows that at least there exists one linear combination of the two variables over time.

The difficulty with the overuse of the word nowadays is that the word is not being used by economists in the Federal Reserve at the same pace as journalist economists do. In fact, the word-count has decreased drastically for the last two years from its peak since 2015. Such mismatch may create false expectations about economic growth, sales and economic performance that may lead to economic crisis.

Raising economic expectations with the “after-tax” reckon: President Trump’s corporate tax cut plan.

The series of documents published by the White House Council of Economic Advisers indicate that President Donald Trump’s Tax Reform will end up being his economic growth policy. The most persuasive pitch behind the corporate tax cut is that lowering taxes to corporations will foster economic investment thereby economic growth. Further, the political rhetoric refers to GDP growth estimates of a tax-cut-boosted 3 to 5 percent growth in the long run. In supporting the corporate tax cut, the White House Council of Economic Advisers presented both a theoretical framework and some empirical evidence of the effects of tax cuts on economic growth. Even though the evidence presented by the CEA is sound and right, after reading the document, any analyst would promptly notice that the story is incomplete and biased. In this blog post, I will briefly point to the incompleteness of White House CEA’s tax cut policy justification. Then, I will show that the alleged “substantial” empirical evidence meant to support the corporate tax-cut policy is insufficient as well as flawed. In third place, I will make some remarks on the relevance of the tax-cut as a fiscal policy tool in balance to the current limitation of monetary policy. Finally, I conclude that despite the short-term benefits of the corporate tax cut, such benefits are temporal as the new normal rate settles, and at the end of the day, given that tax policy cannot be optimized, setting expectations from the administration is a policy waste of time.

The very first policy instance that CEA stresses in its document is the fact that corporate tax cut does affect economic growth. Following CEA’s rationale of current economic conditions, the main obstacle to GDP growth rates above 2 percent is low rates of private fixed investment. CEA infers implicitly that the user cost of capital far exceeds profit rates. In other words, profit rates do not add up enough to cover for depreciation and wear off capital investments. Thus, if private investment depends on expected profit as well as depreciation, simply put I_t=I(π_t/(r_t+ δ)) where the numerator is profit, and the denominator is the user cost of capital (Real Interest rate plus depreciation), the quickest strategy to alter the equation is by increasing profit through lowering on fixed cost such as taxes. CEA’s rationale assumes correctly that no one can control depreciation of capital goods, and wrongly thinks that no one (including the Federal Reserve which faces serious limitations) can control real interest rate, currently.

CEA fetched some data from the Bureau of Economic Analysis to demonstrate that private sector Investment is showing concerning signals of exhaustion. The Council sees a “substantial” weakness in equipment and structures investments. More precisely, CEA remarks that both equipment and structure investment have declined since 2014. Indeed, both variables show a decline in levels of 2 and 4 percent respectively. However, and although CEA considers such decline worrisome, those decreases seem not extraordinary for the variables to develop truly policy concerns. In fairness, those variables have shown sharper decreases in the past. The adjective “substantial,” which justifies the corporate tax cut proposal, is fundamentally flawed.

The problem with the proposal is that “substantial” does not imply “significant” statistically speaking. In fact, when put in econometric perspective, one of those two declines does not appear to be statistically different from the mean. In other words, the two declines look perfectly as a natural variation within the normal business cycle. A simple one sample t-test will show the incorrectness of the “substantial” reading of the data. A negative .023 change (p=.062), in Private fixed investment in equipment (Non-Residential) from 2015 to 2016, is just on the verge of normal business (M=.027, SD=.097), when alpha level is set to .05. On the other hand, a negative .043 change (p=.013) in Private fixed investment for nonresidential structures stands out of the average change (M=.043, SD= .12), but still, it is too early to claim there is a substantial deacceleration of investments.

Thus, if the empirical data on investment do not support a change in tax policy, then the CEA tries to maneuver growth by policy expectations. Their statements and publications unveil the desire to influence agents’ economic behavior by reckoning with the “after-tax” condition of expected profit calculations. Naturally, the economic benefits of corporate tax cuts will run only in the short term as the new rate becomes the new normal. Therefore, the benefit of nominally increasing profits will just boost profit expectations in the short term while increasing the deficit in the long run. Ultimately, the problem of using tax reform as growth policy is that tax rates cannot be controlled for optimization. Unlike interest rate, for numerous reasons, governments do not utilize tax policy as a tool for influencing either markets or economic agents.

 

Why is America’s center of gravity shifting South and West?

Ever since Florida surpassed New York as the third most populous state in the nation, journalists started to document the ways in which the South region of the United States began attracting young sun-lovers enthusiasts. Two factors have been identified as drivers of an apparent migration from the north towards the south. On one hand, real estate prices have been arguably one of the major causes for people heading south. On the other, employment growth and better job opportunities allegedly support decisions on moving out regionally. This article checks empirical data on those two factors to determine the effect on population growth of major cities in the United States. The conclusion, in spite of the statistical model limits, indicates that employment dynamic seems to drive a slightly higher level of influence in population growth when compared to housing costs.

Is it because of real estate prices?

The first factor some prominent people have identified is real estate prices. Professor Paul Krugman highlighted in his NYTimes commentary of August 24th, 2014 that the most probable reason for people heading south is housing costs, even over employment opportunities. From his perspective, employment has little effect on such a change given that wages and salaries are substantially lower in southern states when compared to the north. Whereas, housing costs are significantly lower in southern regions of the country. Professor Krugman asserts that “America’s center of gravity is shifting South and West.” He furthers his argument “by suggesting that the places Americans are leaving actually have higher productivity and more job opportunities than the places they’re going”.

By Catherine De Las Salas

By Catherine De Las Salas

Is it because of employment opportunities?

Otherwise, Patricia Cohen –also from the NYtimes- stresses the relevance of employment opportunities in cities like Denver in Colorado. In her article, the journalist unfolds the story of promising entrepreneurs immersed in an economically fertile environment. The opposite situation to that prosperous environment happens to locate northeast of the United States. Cohen writes that not only “in the Mountain West — but also in places as varied as Seattle and Portland, Ore., in the Northwest, and Atlanta and Orlando, Fla., in the Southeast — employers are hiring at a steady clip, housing prices are up, and consumers are spending more freely”. Her article focuses on contrasting the development of urban-like amenities and how those attractions lure entrepreneurs.

A brief statistical analysis of cross-sectional data.

At first glance, both factors seem to be contributing factors for having an effect on migration within states. However, although both articles are well documented, neither of those readings goes beyond anecdotal facts. So, confirming those very plausible anecdotes deserves a brief statistical analysis of cross-sectional data. For doing so, I took data on estimated population growth for the 71 major cities in the U.S. from 2010 to 2015 (U.S. Census Bureau), and regressed it on the average unemployment rate in 2015 (U.S. Bureau of Labor Statistics), median sale price of existing houses for the same year (National Association of Realtors), and the U.S. Census Bureau’s vacancy rate for the same year and cities (Despite that the latter regressor might be multicollinear with sale price of existing houses, its inclusion in the model aims at reinforcing a proxy for housing demand). The statistical level of significance for the regression is a 90 percent confidence interval.

Results.

The results show that, for these data sets and model, the unemployment rate has a bigger effect on population growth than vacancy rate and median home sale prices altogether. The regression yielded a significant coefficient of -2.78 change in population growth as unemployment decreases. In other words, the lower the unemployment rate, the greater the population growth. A brief revision of empirical evidence shows that, once the coefficients are standardized, unemployment rate causes a higher effect on the dependent variable. If we were to decide which of the two factors affects population growth greater, then we would have to conclude that employment opportunities do it largely.

Regression Results.

Regression Results.

By using these data sets and this model, the employment dynamic seems to drive a slightly higher level of influence in population growth, when compared to housing costs. The unemployment rate has a standardized effect of negative 56 percent. On the other hand, median sale price of houses pushes a standardized change effect of 23 percent. Likewise, vacancy rate causes in the model an estimated 24 percent change in population change. Standardized coefficients are a tool meant to allow for disentangling the combined effect of variables in a model. Thus, despite that the model explains only 35 percent of population growth, standardized coefficients give insights on both competing factors.

Limits of the analysis.

These estimates are not very reliable given that population growth variable mirrors a five years lapse while the other variables do so for one year. In technical words, the delta of the regressand is longer than the delta of the regressors. For this and many other reasons, it is hard to conclude that employment constitutes the primary motivation for people moving out south and west. Nonetheless, this regression sheds light onto a dichotomy that needs to be understood .

US-China trade: There are two sides to every story.

The currency seems to have a negative effect…

There are two sides to every story, even for US-China foreign trade. Ever since China emerged to the world economy as a major manufacturing powerhouse, United States started to lose jobs in the manufacturing sector. Once upon the time firms of manufactured goods such as shoes, clothes even electronics, begun to move their production plants to China’s populous cities looking for an edge in low salaries. However, that trade story with China is oversimplified and misleading. Given that Donald Trump points to currency manipulation for blaming China for U.S.’ losses, I took data on Renminbi’s “depreciation” from January 2009 up to the end of 2015, and regressed it against the value of shipments of the American manufacturing sector. Yes, it does, the currency seems to have a negative effect on the value of shipments in the aggregate. Nonetheless, there are also gains on the U.S.’ side.

I wanted to see quickly to what extent a mere variation of the China’s currency would have an effect on U.S.’ manufacturing production. Then, the stats that I chose for analyzing this phenomenon were the value of shipments (see below for definition) made by U.S. manufacturing firm’s facilities . Then, I took the variation of the Renminbi as recorded by the U.S. Federal Reserve Bank. That is a ratio between nominal measures of the U.S. Dollar and the Yuan. The initial date is January of 2009 for all the time series. The final month is December of 2015.

By Catherine De Las Salas

By Catherine De Las Salas

During this period, China’s currency has been allegedly devaluated down to at least 5 percent. The results bolster Trump’s idea that China’s currency takes a toll in American manufacturing. Though, I do not aim at proving that for these reasons jobs have moved from U.S. to China. Nevertheless, there are also gains for some of the industries within the United States.

Finding statistical significance in these time series is hard:

Finding statistical significance in these time series is difficult. Just for the sake of the debate, I lowered the statistical threshold by amplifying the confidence intervals even down to 80 percent. That way I could achieve a bit of evidence of the trade impact of China’s currency on American manufacturing sector. Twelve items stood out of the rest. Positive coefficients could be found in Wood Products, Metal Machinery, Turbines and power transmission equipment, and Pharmaceutical goods. Note that statistical significance in these cases is down to 80 percent. So, if anyone ever would like to make a case out it, one has to be cautious with any assertion. Nevertheless, those coefficients are still positive and deserve some attention whenever generalizations come to drive the debate about U.S.-China’s trade.

On the other hand, negative coefficients showed up in eight items. The most important line, total manufacturing, registered a negative coefficient (-.42) with statistically significant at the 80 percent level. Total manufacturing excluding defense also classified with a negative coefficient of -.47. Nondurable goods revealed a negative coefficient of -.60 percent.

Below is the list of items and their correspondent coefficients alongside the confidence levels. Remarked in red cells are items with negative coefficients, whereas items with positive coefficients are noted in green cells. Here I also attached the database (Renmimbi US Manufacturing).

Results:

Table of coefficients.

“Value of shipments covers the received or receivable net selling values, f.o.b. plant (exclusive of freight and taxes), of all products shipped, both primary and secondary, as well as all miscellaneous receipts, such as receipts for contract work performed for others, installation and repair, sales of scrap, and sales of products bought and resold without further processing. Included are all items made by or for the establishments from materials owned by it, whether sold, transferred to other plants of the same company, or shipped on consignment. The net selling value of products made in one plant on a contract basis from materials owned by another was reported by the plant providing the materials”.

“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? Econometricus.com 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.

Note:

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.

 

 

Summer’s economic optimism vanishes with the season in the Midwest.

Summer enthusiasm in the Midwest lasted short. The season has not ended yet, as business leaders started to pose concerns in the months to come. The Tenth District Manufacturing Survey revealed that manufacturing activity declined moderately in August 2015. Not only manufacturing declined, but also optimism about future economic activity. Economic expectation in the Midwest are being tempered by the strength of the dollar versus other currencies -especially China’s- around the world, as well as oil prices. Manufacturer leaders expect no increases in most of the matters they are asked about.

By Catherine De Las Salas.

According to Chad Wilkerson, vice president and economist of the Bank, “survey respondents reported that weak oil and gas activity along with a strong dollar continued to weigh on regional factories”. In addition, when manufacturers identify strong dollar as one of their economic challenges, they also relate China’s devaluation of the renminbi. Even though it is very unlikely that the present survey had captured the real effect of such monetary effect, one survey respondent stated that “I speak to many business executives who do exporting, and all seem considerably concern about the dollar strength and the devaluation of the Chinese currency”.

Manufacturer’s opinions on this type of surveys yield insights for analysts to sort out in terms of business leader’s expectations of future profits. It is widely accepted by economists that both current and future output determine heavily investment decisions. Future profits is indeed in every CEO’s reckoning of investment plans. Thereby, investment ends up driving regional economic growth. Thus, the horizon for the fall and winter of 2015 does not look promising for some of the respondents to the survey, all of which live and conduct business in the states of Wyoming, Oklahoma, Kansas, Colorado, Nebraska, the western third of Missouri, and the northern half of New Mexico.

More in detail, the over-the-month change in the Composite Index, which features an average of all indexes, decreased after three months of solid gains. The index had gone up from -13 to -7 in July. However, it dropped again to -9 in August. One year ago the same measure hovered on 3 and was trending upwards in positive terrain.

 

Kansas Manufacturing Composite Index. August 2015.

Kansas Manufacturing Composite Index. August 2015.

Looking at individual indexes, the prospective might give some hope for those who like to see the glass half full. For instance, in terms of number of employees the index shows improvements in spite of it remaining in negative numbers. Number of employees’ index went from -19 up to -10 in the month of August. Similar changes were registered for the average of employees’ workweek. Also, new orders for exports index showed a bit of speed the period of the survey. One of the respondents commented that “…Our year to date has been up from last year and our cash flow position is better; however, the next six months appear shaky at best”.

Kansas Manufacturing Number of Employees Index. August 2015.

Kansas Manufacturing Number of Employees Index. August 2015.

Kansas Manufacturing Workweek Index. August 2015.

Kansas Manufacturing Workweek Index. August 2015.

Kansas Manufacturing Exports Index. August 2015.

Kansas Manufacturing Exports Index. August 2015.

Kansas Manufacturing Production Index. August 2015.

Kansas Manufacturing Production Index. August 2015.

Kansas Manufacturing Inventories Index. August 2015.

Kansas Manufacturing Inventories Index. August 2015.

Kansas Manufacturing Inventories Index. August 2015.

Kansas Manufacturing Inventories Index. August 2015.

Kansas Manufacturing Prices Index. August 2015.

Kansas Manufacturing Prices Index. August 2015.

Kansas Manufacturing Prices paid Index. August 2015.

Kansas Manufacturing Prices paid Index. August 2015.

Kansas Manufacturing Supply delivery Index. August 2015.

Kansas Manufacturing Supply delivery Index. August 2015.

Kansas Manufacturing Shipments Index. August 2015.

Kansas Manufacturing Shipments Index. August 2015.

Kansas Manufacturing Backlogs Index. August 2015.

Kansas Manufacturing Backlogs Index. August 2015.

Kansas Manufacturing Volume of orders Index. August 2015.

Kansas Manufacturing Volume of orders Index. August 2015.

“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.

Real Earnings and the use of Dubious Statistics.

The use of the Average Statistic deceives readers very often whenever the Mean gets severely affected by outliers within the data. One of the most repeated critics to data analysts is the unaware use of average figures, which frequently leads to dubious generalizations. Social scientists, those of whom refuse to use statistics in their analysis, commonly attack this analytical tool by saying: ok, so… if you eat a chicken and I do not eat anything, in average… we both have had half chicken. Nobody would oppose that conclusion as wrong and deceiving. However, such a reasoning uses just half of the procedure statisticians and econometricians use for determining whether or not the conclusion is statistically valid. Therefore, although it is evident that none of the subject in the example ate half a chicken, it is also true that the analysis is half way done.

Outliers heavily affect the Mean statistic:

There is no question that all types of statistics have limited interpretations. In the case of the Mean (arithmetic average), outliers heavily affect the statistic, thereby –very often- the analysis. However, that does not mean arithmetic averages cannot illuminate wise conclusions. For instance Real Earnings, which is a very easy deceiving data on labor economics. Data on Real Earnings “are the estimated arithmetic averages (Means) of the hourly and weekly earnings of all jobs in the private non-farm sector in the economy”. Real Earnings are derived by the US Census Bureau of Labor Statistics from the Current Employment Statistics (CES) survey. So, any unaware reader could jump quickly on to ask if Real Earnings are the average of the hourly earnings of all Americans working in the non-farm private sector. Thus, analysts may also quick respond that in fact that is true. Then, most of the times, the follow up question would read as the following: Does Real Earnings mean that as a “typical” worker in the United States, I would make such an average? The answer is no, it does not. There is precisely where statistical analysis starts to work.

Few Examples:

First. In terms of worker’s earnings various aspects determine how much money people make per hour. Educational attainment is perhaps the greatest determinant of earnings in the American economy. One also can think of geography as a factor of income per hour; even taxes could have an effect on how much money a worker does; age clearly controls income; so on and so forth. Intuitively, it is possible to see that for Earnings and Income there might be many exogenous factors influencing its variability.

Second. For the sake of discussion, let us say that neither education nor taxes affect hourly income of workers. In such a case, and at first glance, it is naïve to believe that counting such a low number of observations could work for any type of analysis, regardless of it being qualitative or quantitative. That means basically that for both qualitative and quantitative analysis, the number of observations matters a lot. In quantitative research the threshold number of observation hovers around 30. Hence, sample size are crucial not only for debunking the cited joke above, but also for reaching valuable conclusion in both qualitative and quantitative social science research.

Taking Real Earnings as example has no pitfall of the latter kind, but it surely does on the former, which certainly bounds the set of conclusion analysts can make. As an Average statistic, Real Earnings have a numerator and a denominator, for which the number in the series is the number of nonfarm private jobs. All types of jobs are included, regardless of age, education attainment, location, taxes, and etcetera. In other words, the companies CEO’s salaries may pull up the statistic. Conversely, minimum wage earners could drag down the Average.

The Median statistic would do a better job sometimes:

At this point, it is clear that for some social science analysis, perhaps other type of statistics happen to be rather more suitable. For instance, the median would help analysts better understand income. So, why should one consider such a computation on Real Earnings? The answer is that Averages figures can be really useful as long as the analyst makes thorough caveats on what the Average really tells; and more importantly, limitations on what the Average figure does not tell.

Real Earnings. Data Source: US Bureau of Labor Statistics.

Real Earnings. Data Source: US Bureau of Labor Statistics.

Hence, changes in Real Earnings shed light onto changes in the proportion of workers in high-wages and low-wages industries or occupations. High-wages salaries will tend to, as in the CEO’s example above, pull up the average without substantial change in the number of hours worked. Conversely, as in the example of the minimum wage earners above, low-wages industries or occupations will tend to lower the outcome statistic. Furthermore, when paired with other data, Real Earnings could be useful for noticing improvements in use technology. If the number of work hours remains stagnant, but both earnings and employment levels increase, the net effect might stem from improvements in technology, which turns on increasing productivity. In other words, workers may work smarter rather than harder and longer. Lastly, Real Earnings Averages can also inform analysts about the amount of overtime work.

So, uses of Arithmetic Means, such as Real Earnings, can be thought-provoking. However, much caution has to be considered whenever economic assertions are stated.

 

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.

Data show Car Industry does just well without Donald Trump’s Advice.

As Donald Trump raises political sympathy by using rhetoric against Ford Company, the Auto Industry’s output jumped 10.6 percent in July 2015. Mr. Trump’s remarks in Michigan, on July 12th 2015, questioned Ford Company for planning on building a $U2.5 billion dollars assembling plant in Mexico. The Republican Candidate suggested investments should be driven by national sentiments rather than by profits and economic opportunity. Judging by recent data released on Industry Capacity, the car industry seems to be doing business the right way since its index of industry utilization just jumped to 10.6 percent, whereas production elsewhere in manufacturing increased only by 0.1 percent in July 2015.

By Catherine De Las Salas. August 2015.

By Catherine De Las Salas. August 2015.

Generally speaking, and for Mr. Trump’s information, the largest increase in industrial output for the month of July of 2015 was seen in consumer goods thanks to production in automotive products. It is hard to believe that the car industry is building a plant that would not make economic sense for the company. In fact, Mr. Trump seems to ignore that the industry is doing so well that it shined among other manufacturing related business. Output in other industries such as Machinery, Aerospace and Miscellaneous Transportation, and Miscellaneous Manufacturing declined by 0.2 percent during the same month. Nonetheless, indexes measuring nondurable goods barely moved up in July. Apparel, Paper, and plastic and rubber products increased 1.0 percent each, while petroleum and textile products actually showed losses. So, clearly Mr. Trump is demanding an economic nonsense. It is hard to believe he manages his real estate business with his political standard.

The intersection of politics and economic fosters policy debates in which advocates, such as Mr. Trump, champion their opinions. However, what have remained steady along the years in the United States is the principle of free enterprise which leads the entire economy. Unless Mr. Trump’s remarks were intended to signal the willingness to installing a centralized economy in US, his opinion on Ford Co. business seem more like a statement of Venezuela’s President Nicolas Maduro.

Likewise, “the index for business equipment edged up, as an increase of 3.5 percent for transit equipment was mostly offset by a decrease of 1.5 percent for industrial and other equipment”, reported the US Federal reserve in its monthly publication on Industrial Production and Capacity Utilization. Consumer durable goods index rose by 1.2 percent.