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 tr7re.com, 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.

Real Earnings: Bureau of Labor Statistics, January 2016.

Press Release by the Bureau of Labor Statistics.

All employees Real average hourly earnings for all employees increased 0.4 percent from December to January, seasonally adjusted, the U.S. Bureau of Labor Statistics reported today. This result stems from a 0.5-percent increase in average hourly earnings combined with no change in the Consumer Price Index for All Urban Consumers (CPI-U).

Real average weekly earnings increased 0.7 percent over the month due to the increase in real average hourly earnings combined with a 0.3-percent increase in the average workweek.

Real average hourly earnings increased 1.1 percent, seasonally adjusted, from January 2015 to January 2016. This increase in real average hourly earnings combined with no change in the average workweek resulted in a 1.2-percent increase in real average weekly earnings over this period.

Production and nonsupervisory employees.

Real average hourly earnings for production and nonsupervisory employees increased 0.3 percent from December to January, seasonally adjusted. This result stems from a 0.3-percent increase in average hourly earnings combined with no change in the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W).

Real average weekly earnings increased 0.3 percent over the month due to the increase in real average hourly earnings combined with no change in average weekly hours. From January 2015 to January 2016, real average hourly earnings increased 1.3 percent, seasonally adjusted. The increase in real average hourly earnings combined with no change in the average workweek resulted in a 1.3-percent increase in real average weekly earnings over this period.

 

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Real Earnings Technical Note:

The earnings series presented in this release are derived from the Bureau of Labor Statistics’ Current Employment Statistics (CES) survey, a monthly establishment survey of employment, payroll, and hours. The deflators used for constant- dollar earnings series presented in this release come from the Consumer Price Indexes Programs. The Consumer Price Index for All Urban Consumers (CPI- U) is used to deflate the all employees series, while the Consumer Price Index for Urban Wage Earners and Clerical Workers (CPI-W) is used to deflate the production employees series.

Seasonally adjusted data are used for estimates of percent change from the same month a year ago for current and constant average hourly and weekly earnings. Special techniques are applied to the CES hours and earnings data in the seasonal adjustment process to mitigate the effect of certain calendar-related fluctuations. Thus, over-the-year changes of these hours and earnings are best measured using seasonally adjusted series. A discussion of the calendar-related fluctuations in the hours and earnings data and the special techniques to remove them is available in the February 2004 issue of Employment and Earnings or on the Internet under ‘Technical Notes’ (http://www.bls.gov/ces/).

Earnings series from the monthly establishment series are estimated arithmetic averages (means) of the hourly and weekly earnings of all jobs in the private nonfarm sector of the economy, as well as of all production and nonsupervisory jobs in the private nonfarm sector of the economy. Average hourly earnings estimates are derived by dividing the estimated industry payroll by the corresponding paid hours. Average weekly hours estimates are similarly derived by dividing estimated aggregate hours by the corresponding number of jobs. Average weekly earnings estimates are derived by multiplying the average hourly earnings and the average weekly hours estimates. This is equivalent to dividing the estimated payroll by the corresponding number of jobs The weekly and hourly earnings estimates for aggregate industries, such as the major industry sector and the total private sector averages printed in this release, are derived by summing the corresponding payroll, hours, and employment estimates of the component industries. As a result, each industry receives a “weight” in the published averages that corresponds to its current level of activity (employment or total hours). This further implies that fluctuations and varying trends in employment in high-wage versus low- wage industries as well as wage rate changes influence the earnings averages.

There are several characteristics of the series presented in this release that limit their suitability for some types of economic analyses. (1) The denominator for the all employee weekly earnings series is the number of private nonfarm jobs. Similarly, the denominator of the production employee weekly earnings series is the number of private nonfarm production and nonsupervisory employee jobs. This number includes full-time and part-time jobs as well as the jobs held by multiple jobholders in the private nonfarm sector. These factors tend to result in weekly earnings averages significantly lower than the corresponding numbers for full-time jobs. (2) Annual earnings averages can differ significantly from the result obtained by multiplying average weekly earnings times 52 weeks. The difference may be due to factors such as turnovers and layoffs. (3) The series are the average earnings of all employees or all production and nonsupervisory jobs, not the earnings average of “typical” jobs or jobs held by “typical” workers. Specifically, there are no adjustments for occupational, age, or schooling variations or for household type or location. Many studies have established the significance of these factors and that their impact varies over time.

Seasonally adjusted data are preferred by some users for analyzing general earnings trends in the economy since they eliminate the effect of changes that normally occur at the same time and in about the same magnitude each year and, therefore, reveal the underlying trends and cyclical movements. Changes in average earnings may be due to seasonal changes in the proportion of workers in high-wage and low-wage industries or occupations or to seasonal changes in the amount of overtime work, and so on.

 

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 MarketRealists.com 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.

Despite job losses, New Jersey’s labor market looks vibrant rather than sclerotic.

Regional and State statistics on employment and unemployment for the month of July 2015 looked motionless for the great majority of States in terms of over-the-month changes. Over-the-year though, nonfarm employment increased in 47 states and deceased in 2. In terms of employment levels, the greatest over-the-month increases were seen in California (+80,700), Texas (+31,400) and Florida (+30,500); while percentage wise, greatest increases were in Wyoming (+0.9 percent), Oklahoma (+0.7 percent), and Rhode Island (+0.7 percent). It is worth noting that a year ago, Rhode Island had an unemployment rate of 7.6 percent, while California’s was about 7.4 percent. Today, those two states reported unemployment rates of 5.8 percent (Rhode Island) while California recorded 6.2 percent.

Unemployment Rate July 2015.

Unemployment Rate July 2015.

Otherwise, declines in employment levels were statistically significant in North Dakota (-0.5 percent), Hawaii, Kansas, New Jersey, and West Virginia with -0.3 percent decline each. West Virginia noted an increase of 1 percent point and registered an unemployment rate of 7.5 percent. Both Dakotas also showed increases in Unemployment rate.

The challenging aspects for the analysis this time stem from the data coming out from New Jersey, Kansas and Louisiana. These three states showed decreases in employment level from June to July 2015. New Jersey’s level of employment decreased by -13,600 jobs, while Louisiana and Kansas did so also by -4,500 and -4,300 respectively. Given that the declines happened during the summer season, they all beg the question on whether those job losses were quits or separations.

When it comes to labor markets, employment levels can have negative variation for two reasons. First, firms may stop hiring new employees and further start firing the current ones. Second, employees may quite their jobs. In order to be accurate, it is key for the analysts to determining under what circumstances the drop in the statistic happened. The most expedited way to find out, whether the job losses were on the firm’s end or on the employee’s end, used to be by looking at Massive Layoff data from the BLS. However, the Massive Layoff program ended since the budget cuts fights in 2013 between Republicans and Democrats.

So, going back to New Jersey’s employment level data for the month of July 2015, intuitively it is hard to believe that a job drop happened in the state during the summer, which only has happened 13 times in almost 40 years –five of which happened since the Great Recession Started-, and it has done so mostly during economic recessions. So, particularly in the case of New Jersey, the question about quits versus separations begs an answer.

New Jersey's Level of Employment Change June-July 1076-2015.

New Jersey’s Level of Employment Change June-July 1976-2015.

Given that there is not Massive Layoff data available, one way to scratch the surface of what is going on in the State’s labor market is by looking at its output and current economic conditions. Indeed, the southern part of the state -Lehigh Valley and the Southern Jersey Shore- have seen a slowdown in real estate markets. The region, which is covered by the Eleven District of Philadelphia at the Federal Reserve System, has experienced moderate to positive changes in the economy through the second quarter of 2015. In particular, regionally speaking, auto-dealers have seen flat sales during the summer. Home builders also reported little change in activity for the same period. Likewise, and although manufacturing picked a bit up, food products, primary metals and electronic products have seen sales decreases. Similarly, staffing firms reported slowdowns as well as trucking activity showed signs of weakness.

Apparently, there is no drama when it comes to assess the current economic condition of the region. Besides the industries cited above, every other sector reported moderate improvements. Thus, the overall economic conditions of the state are not that bad so as to expect such a drop in employment levels. In fact, the State Unemployment Rate has declined since 2009 to 6.5 percent. Right after the Great Recession started, New Jersey’s Unemployment rate was over 9 percent. Even though the state’s labor market recovery appears to be slow, it also looks steady. Therefore, what seems feasible to interpret under the current circumstances is that New Jersey’s labor market looks vibrant rather than sclerotic. That is, workers in New Jersey quitted their job for better opportunities elsewhere.

By devaluating the renmimbi, China unveils its fears of losing manufacturing jobs.

Operating a fixed exchange rate system has only a little time advantage.

China’s effort to devaluate the renmimbi is buying time for Chinese government officials, only to figure out what to do next. However, this strategy will not last as much as they expect it to do so. The recent monetary moves speak more of Chinese officials’ fears than about the actual competitiveness of the Chinese economy. When it comes to exchange rates, what really drives investments and business insights is the real exchange rate, rather than the nominal exchange rate –which is what China is manipulating currently. That implies two things: first, it is a matter of time for value of commodities to adjust; and second, every conclusion on any effect must revise currency counterparts. Regarding Chinese competitiveness against U.S., two facts shed light onto what China is fearing to happen: big increases of US labor productivity –which apparently is not happening-, and at the same time, a slow down on unit production costs within the U.S.

Data on labor productivity matters:

In the case of the United States, preliminary data on labor productivity for the second quarter of 2015, show American workers are working harder instead of smarter. Compared to the second quarter 2014, labor Productivity increased 0.3 percent, “reflecting increases in output and hours worked of 2.8 percent and 2.6 percent, respectively”, reported the U.S. Bureau of Labor Statistics on August 11th 2015. Among nonfarm business sector, labor productivity increased at an annual rate of 1.3 percent, while output did so by 2.8. Hours worked increased by 1.5 percent. By looking at these data, anyone would conclude that United States is not experiencing big jumps in labor productivity, which should not scare anybody including China. Honestly, and although China’s workers are far away from U.S. labor force productivity, the U.S. sluggishness nowadays should have deterred Chinese Officials from devaluating the renmimbi. Therefore, there are no reasons to believe Chinese officials expect US labor productivity to jump.

Tech makes professionals more productive. By Catherine De Las Salas. NYC, summer 2015.

Tech makes professionals more productive. By Catherine De Las Salas. NYC, summer 2015.

Furthermore, just consider that labor productivity increases with changes in technology. Job site implementation of new technologies make workers more efficient per hour which increases the measure. These changes in technologies may include managerial skills, organization of production, and characteristics of the labor force, amongst others influences. Comparing China versus United States in this regard should give Chinese Officials confidence in their cheap labor leverage, while acknowledging the need to in developing competitive technologies.

Manufacturing jobs will move out from China sooner than later:

On the other hand, what could have encouraged Chinese officials to devaluate their currency is the actual trend in US labor cost. Preliminary data on unit labor cost in the nonfarm business sector show a modest increase of 0.5 percent in the second quarter. That is almost nothing and everything. On one hand, it could mean a starting trend that could allow for shoring back from China manufacturing jobs. One must acknowledge that it is too soon to assert such a prediction, but it is enough to believe that in the same way manufacturing jobs were exported from the U.S., the same way those jobs could be repatriated. Even though it is still very expensive to manufacture within the United States (Unit labor cost increased 2.1 percent over the last four quarters), it is also true that those manufacturing jobs have never disappeared from the US Economy. They are, if you will, transitorily in China. Those jobs are still part of the US chain of production. In fact, that is what Chinese official are certain and resolute to avoid by setting the nominal exchange rate of the renmimbi. The message from the Chinese currency moves is the following: manufacturing jobs will move out from China sooner than later. The question is, who is going to take over them. It makes sense to shore them in to the United States since those jobs are often commanded from US Big cities. Just look at an iPhone: designed in California, assembled in China.

Does a worker choose not to work when collecting Social Security?

Campaigns against social security usually claim that Social Security Benefits discourage workers from being employed. Many right wing policy advocates point their fingers at Social Security Benefits as being expensive and further making the labor force lazy – to say the least. In this article I analyze to what extent the number of unemployed people is determined by the number of people collecting Social Security Benefits given out by disability claims. That is, workers’ own disability; workers’ spouse disability; and, workers’ children disability. I use the term workers because, in spite being disable, I assume they are willing to work. Thus, the argument from the right would be that people readily available to work will remain unemployed whenever they can secure an income from the Social Security Administration. Furthermore, workers will do so too before the scenario in which their spouses collect benefits. And third, workers will not work in the case in which social security benefits are being collected for their children. In other words, workers would rather take care of the disable children or spouse and live out of public transfers. Then, the question that possesses this analysis is the following: does a worker choose not to work when collecting some form of Social Security Benefit for her family?

Social Security and Unemployment levels.

Social Security and Unemployment levels. By Catherine De Las Salas (Summer 2015).

The data:

So, by looking at the correlation between number of unemployed people and number of people claiming benefits for the above mentioned three reasons, I am able to capture the “willingness” of disable workers, whom are collecting social security benefits, to work. I take data at the United States county level from the U.S. Social Security Administration database which contains the number of beneficiaries by type of benefit. Also, I take observations pertaining to the number of people claiming benefits for disability reasons. In addition, I take the number of unemployed people at the county level (data from the U.S. Bureau of Labor Statistics). Both data correspond to 2014. The only counties excluded from the sample are the ones at U.S. Virgin Islands. All other counties, and independent cities are included in the sample regression.

One could argue, correctly, some sort of multicollinearity in the data since people collecting benefits usually do not work. However, unemployment statistics from the Bureau of Labor Statistics interestingly count as unemployed persons those who have looked actively for a job during the recent past weeks of the application of the survey. This means that what the unemployment statistics is capturing here is the “willingness” of disable people to work while collecting social security benefits. Given that the answers to BLS Household Survey data have no conditional effect on social security benefits, it is reasonable not expect the survey to be corrupted by the interest of keeping the benefit on the beneficiaries’ end. In other words, in spite of the statistical identity, data can be further interpreted given the nature of the question being asked by BLS Household Survey.

Results:

What I found at the county level is that as the number of disable workers rise by 2.9, the number of unemployed persons do so by one. This is an obvious outcome of the effect that disabilities have on the labor market. So, this should not surprise anyone. However, what turned out to be interesting is the fact that disable people collecting social security benefits are counted as unemployed. This basically means, to some extent, that disable people are “willing” and actively looking for jobs. Although the logic is counterintuitive at first glance, it may reveal something thought-provoking. On one hand, if the person is disable to work, and at the same time collecting social security benefits, such a person should not be looking for a job. But, what the data show is that they actually, and actively, looked for a job despite their condition. Although interpretations have to be carefully examined, either disable persons are cheating the system, or they are just eager to be incorporated to the labor market. Further, given the statistical significance at 95% confidence level for all of the estimated coefficients, there is little room for concluding the variation is due to sampling error only.

Likewise, unemployment levels are affected by workers’ disable spouses. For every increase of roughly 46 people collecting benefits for their spouses, there is a unit increase in the number of unemployed people. Clearly, having a disable spouse does little discouragement for the worker to work. Finally, unemployment levels decrease with increases of disable children. That is, disable children make workers look for jobs eagerly. As the number of disable children increases by 10.5, the number of unemployed people drops by one.

One obvious limitation of the analysis is the type of disability that beneficiaries may have, which certainly mediates the “willingness” of the disable person to work. Nonetheless, some narrow conclusions can be drawn from this regression. First, even though disable people get support from social security, it does not translate necessarily in quitting the labor force, which means neither disabilities, nor public transfers make them lazy. Also, data show that paying for a disable children encourages parents to work.

Regression Output, Social Security Benefits and Unemployment levels

Regression Output, Social Security Benefits and Unemployment levels

 

Employment statistics cannot be interpreted in a vacuum.

Employment statistics cannot be interpreted in a vacuum inasmuch as other economic indicators do determine employment growth. There are many nuances that require attention to detail. Indeed, details on June’s 2015 report on labor market are twofold. First, mining related industries –including utilities- started to adjust to low oil prices, as well as low demand for several manufacturing goods tempered high expectations risen before summer season. Likewise, spring low levels of investment on residential construction realized a decrease on employment for the summer. Second, Professional Business and Services continues to lead job creation in United States. In other words, oil prices do continue to affect the economy, though the industry started to adjust; strong dollar dragged international demand for U.S. metals products thereby affecting employment in manufacturing; third, low levels of investment in construction are taking a toll in employment creation.

Employment level June 2015.

Employment level June 2015.

Since Construction Investment slowed down in the spring, employment in the industry drops in the summer:

The latter factor should worry the most labor economist. Given that oil prices and exchange rates are beyond strictly control of United States institutions, and are also well known phenomena, investment in construction should call the attention of economic policy leaders. Since the beginning of the summer of 2015, when the statistics about GDP 2015Q1 were released, economists started to look at Investment in non-residential and residential structures. This sector is key for the seasonal employment since, as soon as weather allows for, construction and outdoor activities rebound. However, early in the spring 2015, this was not the case. Total residential construction put in place for the month of April 2015 decreased to $353,086 billion of dollars from 360,826 billion put in place the same month 2014, which equals 2 percent decrease over the year.

Residential Construction Put in Place, April 2015.

Residential Construction Put in Place, April 2015.

So, it should not surprise anyone that construction-contractors cut back employment as they see investment dropping. That very fact shows up in employment statistics astonishingly. Data from BLS show construction did not contribute significantly to augment employment levels nationally. Instead, 6.1KResidential construction building workers were dismiss from work; 5.6K Nonresidential specialty trade contractors were also cut from duty. That makes up to roughly 12K seasonal jobs that are crucial for year-round labor statistics.
These statistics are relevant for economics given that construction of new homes has many job spillovers in manufacturing industries. Another way to say the same is that whenever a new home is built, new sofas, TV’s, Kitchens, furniture, so on and so forth, are needed. Furthermore, the housing market was the sector that initiated the Great Recession, and construction as a sector was the latest in joining the path to recover. This issue helps to introduce the other weak flank of the current employment situation: manufacturing.

Manufacturing feels the spillover of strong dollar and low local demand:

The other drop in employment levels for June 2015 was seen in manufacturing, more precisely on metal related products which decreased employment level by 4.5K persons. In this case, apparently, it is not only internal demand which is cutting back employment levels, but also international demand for U.S. manufacture goods. In other words, last six months of strong dollar reduced the demand for U.S. manufacturing thereby affecting employment locally. Several sources have noted the extent to which the exchange rate is affecting negatively U.S. competitiveness and employment. Especially for metal products. Recent data on Current Account –Exports and Imports- released by the Bureau of Economic Analysis showed that during the first quarter of 2015 Goods exports decreased to $382.7 Billion from $409.1 billion. The drop in manufacture exports was mostly driven by a decrease in industrial supplies such as Petroleum, Chemicals and…. Metals products. This effect obviously spills over employment levels nationally. Once again, it is not a surprise.

Unemployment rates, June 2015.

Unemployment rates, June 2015.

The surprise:

Finally, what really happens to be a surprise is the revision of employment levels for the months of April and May 2015. The Bureau of Labor Statistics corrected its initial estimates on those figures by stating that April’s 2015 employments added were 187,000, and May’s employment added were 254,000. At first glance, April statistic fell below the threshold of 200,000 jobs per month, which should worry analysts to begin with. Then, when computed, the total amount of jobs not realized statistically amounts to 60,000. Thus, the monthly average for job gains is 221,000, which is slightly above the 200,000 threshold.

Average Figures on How Americans Spend Time 2003 – 2014.

The way Americans spend their time tends to drive their expending behavior. Either they may spend time working or spend time having fun. The latest release of the American Use Time Survey 2014 (ATUS) run by the U.S. Bureau of Labor Statistics (BLS) started to give more consistent data about changes on how Americans make use of their time. In its latest version which pertain 2014 data, the ATUS shows that Working and Work-Related are among the activities that had gone down since the Great Recession started. Nonetheless and although average of working hours levels are still below pre-Great Recession average, the Survey shows that those activities are rebounding at a good pace, which is consistent with data on labor market.

ATUS 1

How do  Americans make use of their time?

ATUS data reveal that Leisure and Sport Activities have gone up, as well as Personal Care and Other Activities. The first year in which the BLS collected data for that survey, Americans spent in average 5.11 hours per day in Leisure and Sports related activities. After eleven years and one economic recession, Americans spend in average 5.30 hours per day doing the same things. Personal Care related activities where at an average of 9.34 hours per day before the Great Recession. However, since the beginning of the Great Recession, Americans spend in average almost half an hour more.

ATUS 2

Time dedicated to Purchasing Goods and Services has also declined slightly. In average, Americans spend 44 minutes hours per day doing shopping, whereas in 2003 they spent roughly 48 minutes per day.

ATUS 3

Interpreting these data happens to be a double edge sword.

Given that Average and Means are measures that get highly affected by outliers, analysts and readers must have caution while making inferences about these statistics. Furthermore, the perspective from where analysts operate tends to yield different conclusions. The first set of conclusions can be derived from the perspective of those that aim at interpreting the population as made up of Leisure-maximizing agents, whereas the second set of conclusion can be derived from the perspective of those who attempt to interpret the society as comprising of Income-maximizing agents. Therefore, some analysts may infer from these data that more time spent in Leisure activities might have a positive impact in quality life, personal health, and happiness. On the other hand, analysts could also conclude that Income could have been affected negatively and that population has become lazier.

ATUS 4

Generalizing will always be risky:

As the reader may have notice, these data work out even for bolstering political and ideological statements. In spite of its ambiguity, ATUS data can be used for identifying major economic trends, and at the same time for depicting a bigger picture of the American culture, habits and, indeed, about the “American life”. Generalizing will always be risky, but when analysts clearly identify the boundaries of the data, and readers understand the limits of the sources, honest conclusion can be drawn from these rather obscure data.

ATUS 5

ATUS 6

ATUS 7

ATUS 8

ATUS 9

ATUS 12

Labor Productivity: 2015Q1 vis-à-vis 2014Q1.

Labor Productivity increased 0.2 percent when comparing 2014 first quarter and 2015 first quarter. Hours worked by all persons in the labor market increased 0.3 percent in 2015Q1 vis-à-vis 2014Q1. Compensation per hour changed 0.18 percent, whereas the Unit Labor Cost increased 0.16%. It is important to note that, while Output per person, working in nondurable goods manufacturing industry, augmented by 2.2%, the Unite Labor Cost in the same sector decreased by 0.3 percent. Compensation instead changed by 1.9%.

LP2

Industry specifics:

By looking at industry specifics, Labor Productivity in Manufacturing of Durable Goods increased by 1.2%, while Output in the industry did so by 4.1%. The number of hours worked in this sector increased by 2.8%, whereas real compensation did so by only 1.6%.

These data may help economist in understanding why GDP slowed down in the first quarter 2015, while unemployment rate stuck to 5.4%. On one side, Discourage Workers seem to be back in the labor market as more hours worked are being demanded by producers. On the other side, although workers are working longer workweeks, compensation has changed significantly, which gets reflected in low levels of inflation.

Perhaps the only conclusion for now clear is that the economy might be being driven by expectations. Based on the current figures and the optimism revealed in the Beige Book, it is possible to assert that most of the increase in level of employment is being generated solely by positive, and perhaps “risky” expectation of the close future.

LP1
On the other hand, Nonfarm Business Sector Labor Productivity decline for second quarter in a raw during the past year. BLS Revised data, released on June 4th 2015, confirmed labor productivity declined by 3.1 percent at an annual rate, which complements data on GDP negative growth for 2015Q1. Data released along the current week on the contraction of GDP, Construction, Labor Market and Productivity, reveal expectations and future outlooks of Business Leaders ought to be driven the economy and especially the labor market.

Finally, the U.S. Bureau of Labor Statistics noted that “from the first quarter of 2014 to the first quarter of 2015 productivity increased 0.3 percent”. One of the key labor indicators derived from productivity measures is Unit Labor Cost, which has actually increased 1.8 percent in the last year. For the first quarter of 2015, BLS asserted Unit Labor Cost increased by 6.7 percent. Such a statistic reflects the mentioned 3.1 percent decline in productivity, and a 3.3 percent increase in compensation by the hour.

 

Utah ranks on top in job creation for April 2015.

Perhaps the state that showed the best performance in level of employment was Utah. Industries seem to be booming there, where 52K jobs positions were added. Construction registered an increase of 7.7%, while Leisure and Hospitality did on 7.4%. Trade and Transportation, and Financial Services also increased their levels by 5.0% and 4.9%.

Utah Level of Employment
Utah ranked first on industry growth for Leisure and hospitality along with Vermon. Arkansas, Georgia and Florida also experienced increases in such industry. In education Utah was surpassed by Oregon and Colorado both with 4.9% increase in job creation for the industry in April 2015. In Financial Activities Utah also made an appearance in April 2015, though the state was surpassed by Oregon, South Carolina and Washington State. Even in the Manufacturing sector Utah made it to the fourth place in April 2015. The only two sector in which the State did not make it to the four top rank were Construction and Professional Business.

California Level of Employment

Data source: US Bureau of Labor Statistics.

Change in employment