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.


How and when to make “policy recommendations”.

The ultimate goal of a policy analyst is to make “policy recommendations”. But, when is it precise to make such bold type of statements without looking inexperienced? The following article looks at the requirements for reliable and sound policy recommendations. The focus here is not on how to write them, but on how to technically support them. The best policy recommendations are those that derive from identifying a research problem, pinpoint proxies, quantify magnitudes, and optimize responses under a set of constraints. In other words, good policy recommendations translate into the proper measurement, research, and interpretation.

To identify and describe the policy issue:

To identify and describe a policy issue is a matter that most of the analysts do well. Examining a hypothesis through several qualitative methods is what most of us do. So, let us start by saying that our policy issue concerns the exports of American manufactured products within a geographical scope and a targeted timeframe. In formulating the research, the analyst would identify a set of factors that have affected directly or indirectly manufacturing production in the U.S. from 2009 to 2015. Let us also say that, after a rigorous literature review as well as three rounds of focus groups with stakeholders, our analyst came up with the conclusion that the primary factor affecting negatively U.S. manufacturing exports is China’s currency. Although everyone knows that the issue of U.S. manufacturing is way more complicated than just the variation of China’s currency, let us stop there for sake of our discussion. Thus far, no policy recommendations can be made, unless our analyst wants to embarrass his work team.

Proceed with the identification of metrics:

Once our analyst has found and defined the research problem pertaining the policy issue, he might wish to proceed with the identification of metrics that best represent the problem. That is a measure for manufacturing production variation and a measure of China’s currency variation. Manufacturing can be captured by “Total value of Shipments” statistics from the Census Bureau. China’s currency can be taken from the Federal Reserve Bank in the form of US dollar – Renminbi exchange rate. Again, our analyst may come up with many metrics and measures for capturing what he thinks best represent the problem’s variables. But for now, those two variables are the best possible representation of the problem. Thus far, no policy recommendations can be made, unless our analyst wants to ridicule himself.

Advance with the configuration of the database:

After exhausting all possible proxies, our analyst may advance with the configuration of the dataset. Whether the dataset is a time series or cross-sectional, the configuration of the dataset must facilitate no violation of the seven assumptions of regression analysis. At this stage of the research, our analyst could specify a simple statistical or econometric model. In our example, such a model is a simple linear regression of the form Y=β1-β2X+Ԑ. Thus far, no policy recommendations can be made, unless our analyst wants to lose his job.

Run regressions:

Since data is ready to go, our policy expert may start running data summaries and main statistics. Then, he would continue to run regressions in whatever statistical package he prefers. In our example, he would regress U.S. value of shipment of non-defense capital goods (dependent variable) on U.S.-Renminbi exchange rate (independent variable). Given that our case happens to be a time series, it is worth noting that the series must be stationary. Hence, the measures had to be transformed into stationary metrics by taking the first difference and then the percentage change over the months. In other words, the model is a random walk with a drift. Just in case the reader wants to check, below are the graphs and a brief summary statistics. Thus far, no policy recommendations can be made, but out analyst is getting closer and closer.

Summary ARIMA Model.

Summary ARIMA Model.

Compare results with other researchers’ estimates:

Now that our analyst has estimated all the parameters, he would rush back into the literature review and compare those results with other researchers’ estimates. If everything makes sense within the bounds set by both the lit review and his regressions, our policy professional may start using the model for control and policy purposes. At this point of the research, the analyst is equipped with enough knowledge about the phenomena, and, therefore, he could start making policy recommendations.

Finally, to complete our policy expert’s story, let us assume that China’s government is interested in growing its industries in the sector in a horizon of two years. Then, what could our analyst’s policy recommendation be? Given that our analyst knows the variables and the parameters of the policy issue, he could draft now a strategy aimed at altering those parameters. In my example, I know that a unit change increase in the U.S. Dollar – Renminbi exchange rate could generate a 75% decrease in the monthly change in value of shipments – Non-defense Capital Goods. Despite the low level of statistical significance, let us assume the model works just fine for policy purposes.

Correlograms and Summary Statistics.

Correlograms and Summary Statistics.

Los Angeles’ Homelessness Crisis and the abuse of the term ‘chronic homelessness’.

Los Angeles’ failure to cope with homelessness has brought the issue to the light of many who believe that such a thing does not happen in the richest and powerful country on earth. Among the interesting facts, branches the distinction between both terms Homelessness and Chronic Homelessness. The nuance is relevant inasmuch as for public policy purposes Chronic Homelessness means something entirely different from just a growing crisis on the homeless population. The fact that Los Angeles tops the nation in homeless population makes regular people (including policy makers) think that since there is a crisis, every homeless is chronic. Even mainstream journalism reports both terms indistinctively. Peeling out layers on homelessness leads to a better understanding of the phenomenon thereby rising community expectations for homelessness plans and policies. This brief article stresses the essential differences that make a homeless situation chronic.

Let us start with the confusion in mainstream media. Los Angeles Times and The New York Times reported on their sites the following sentences. Note that the use of the terms, homeless and chronically homeless, is applied indistinctively.

“L.A.’s chronically homeless population has grown 55%, to 12,536, since 2013, accounting for almost 15% of all people in that category, HUD reported. More than one-third of the nation’s chronically homeless live in California, the agency added” (Los Angeles Times. November 19, 2015).

“The number of chronically homeless people nationwide remained basically flat, rising 1%, the report said”.

“The nationwide numbers came as a disappointment to HUD, which had extended a goal of ending chronic homelessness from the end of the year to 2017″.

“The government classifies disabled people who go without housing for a year, or who land in the street several times over three years, as chronically homeless“.

In spite of the last sentence in which the article on Los Angeles Times quotes the Federal Government in its definition of “chronic homelessness, the use of the term remains ambiguous for most of the readers. Plus, although both newspapers do a good job in informing by using data and quoting qualitative sources, the overall purpose gets defeated by the lack of clarity of the concepts. Then, the question worth asking is the following, what is chronic homelessness and how it differs from homelessness alone?

Chronic homelessness:

Homeless or house poor is every person who cannot afford to pay for shelter, whereas chronic homeless falls into a more complex definition. Among experts, the term “chronic homelessness” has emerged to define not only the absence of physical shelter but also shared psychological conditions among the homeless population. Piliavin et. al. (1996) points out several important factors that are often associated with causes of chronic homelessness. Among those factors is the lack of institutional support which is defined as having weak ties with institutions such as Employment, Marriage, Youth, and even Family. Another factor is Human capital deficiencies which are understood as poor relationship building skills. Personal disability is a third factor which ranges from substance abuse to mental health. Finally, acculturation is the last factor Piliavin et. al. (1996) relate.

Of primary relevance to Los Angeles’ crisis is the last cited aspect. Acculturation plays a significant role in determining the length of homelessness status. Shared stories, shared needs and shared means to survive among homeless individuals may affect the lasting permanence of a person’s homeless status. This phenomenon of acculturation evolves through a friendship building processes among homeless persons. Plain and simply put, homeless individuals with more homeless friends are more likely to remain homeless, therefore chronically homeless.

Also and regardless of ethnic groups, age, gender and marital status, chronic homelessness is portrayed as a phenomenon derived from mental illnesses such as depression, schizophrenia or psychotic disorders. Personal disabilities range from substance abuse to mental health. Risk factors for homelessness within this factor include social poverty, economic poverty, feeling unloved in childhood, mental disorder, and low level of friend support, sexual, drug or physical abuse and parental divorce.

Finally, structural pressures are to blame when it comes to chronic homelessness. Excessive Individualism among member of the society seems to lurk underneath the deterioration of social capital. In other words, our society reproduces cultural and ideological pressures that blame the homeless person as solely responsible for his situation (Lee et al., 1990). Experts believe that dysfunctional family environments, for example, homes headed by young (ages between 17 and 25) and female, in addition to social and cultural pressures, and a lack of public support, is a classic formula for chronic homelessness. Axelson and Dali (1998) state that chronic homelessness is an outcome of a lack of family and social support. In most, cases homeless women report having been physically abused.


In conclusion, nuances in homelessness must be considered when dealing with public programs intended to curb down the phenomenon. Policy-wise, these differences may affect the target and the outcomes of policies being formulated by government officials. Furthermore, it is evident that the crisis comes not only from the lack of public programs and strong public organizations but also from the weakness of social bonds and links. Thus, besides these institutional and social facades of homelessness, media must also illustrate the public so that we all know there is a shared responsibility in solving chronic homelessness. Even the family, as an institution has a role that needs to be addressed in Los Angeles’ homelessness crisis.



Lee, Barrett A., Sue Hinze Jones and David W. Lewis (1990). Public Beliefs about the Causes of Homelessness. Social Forces, Vol. 69, No. 1.

Piliavin, Irving, Bradley R. Entner Wright, Robert D. Mare and Alex H. Westerfelt (1996). Exits from and Returns to Homelessness. Social Service Review, Vol. 70, No. 1.

Axelson, Leland J and Paula W. Dail, (1988). The Changing Character of Homelessness in the United States. Family Relations, Vol. 37, No. 4.



“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.


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.



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.


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


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.


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.


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.


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.


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.







What could you infer from 06/17/15 Federal Open Market Committee decision?

What can we infer from today’s Federal Open Market Committee decision?

Today’s Federal Open Market Committee (FOMC) decision corresponds to Fed’s previous statements about the current state of U.S. economy. First, data inputs on Capacity Utilization led timidly FOMC to insights on industry output gap. Second, the Beige Book clearly illuminated onto issues related to the economic geography of current economic conditions. Third, preliminary data on GDP 2015Q1 continued to be obviously a major concern. Finally, neither was employment at stake this time, nor inflation, nor household consumption. First, In spite of the Beige Book reveling regionally based concerns, they believe nothing can be fixed institutionally. The FOMC left unchanged interest rates for federal funds, which is the rate they use to influence market loan rates. Its statement of June 17th 2015 reads “To support continued progress toward maximum employment and price stability, the Committee today reaffirmed its view that the current 0 to 1/4 percent target range for the federal funds rate remains appropriate”.

On one hand, Oil Prices favor certain policy pressures mostly coming from Texas; therefore, policy preference coming from related industries such as transportation and utilities. On the other hand, other type of monetary policy preferences are coming from the most recent levels of exchange rates of U.S. dollar vis-à-vis Euro dollar. Here though, the FOMC has a muscle through influencing the rate. However, doubts are cast given the uneven reality of foreign exchange rates. Thus, not modifying the interest rate –in absents of the lowering option- seems the only way for the FOMC to bolster U.S. exports thereby doing so to employment. These two economic aspects made up the current concerns of Fed’s officials. Nonetheless, neither of them could be effectively influenced under the dual mandate of the FOMC. They demonstrated today liquidity trap keeps restraining monetary policy options.

What seems to be clear after today’s FOMC statement is that although the U.S. Federal Reserve aims at closing the output gap by influencing the interest rate, the institution has no clear diagnostic on Capacity Utilization. Apparently, Fed’s officials know data on Capacity Utilization no longer unveil facts worth concluding on output gap. What FOMC probably learned on Capacity Utilization during the second week of June is that Industrial Capacity on Manufacturing is below its long term average only 1.6 percent. The Federal Reserve Index for Manufacturing Industrial Capacity is at 77 percent. Non-durable goods Industrial Utilization Capacity is just 1.5 percent below its long term average. The latter Index showed 79.1 percent. Mining Industry Capacity Index shows the sector is adjusting to oil price rapidly and registered 83.3 percent Utilization.

Finally, trying to predict what the FOMC would do regarding the interest rate seems to be more complicated task than just plugging in policy targets on the Taylor Rule equation. Actually, the Taylor Rule is nothing but a crystal bowl inasmuch as economists look at it in isolation of surrounding data and information. Indeed, they seem to seriously consider broader sources of data and make a judgement comprehensively.

Fed may get monetary policy mussel back in 2015.

2014 showed no excitement in monetary trends, generally speaking. Besides the Federal Reserve’s Quantitative Easing program (QE), the pockets where the American money is remained mostly stable. The interesting changes were in the first pocket which comprises currency that circulates on the streets (Households). This pocket grew in one year 9.6% (from November 2013 to November 2014), which is a remarkable figure as long as changes in such pocket -arguably- reflect either Inflation or increase in nominal income. Considering that inflation rate in the United Sates has been oscillating roughly around 1.5% for the last two years, this 9.6% may mean that changes in the currency pocket were due largely to an increase in nominal income. Basically, this pocket consists of money that circulates outside the U.S. Treasury, the Federal Reserve’s Banks, and the Vaults of Depository Institutions.

Monetary trends
Changes in the circulating currency may give the Federal Reserve the chance to have the monetary policy mussel back. With data showing low inflation levels for the last two years in the United States, it is almost virtually impossible for the Fed to influence changes in interest rates. Thus, by having 9.6% year change in currency due possibly to an increase in nominal income, chances may increase for a larger number on expected inflation for 2015. Larger expected inflation number may encourage lending during 2015 thereby stimulating consumption and investments, which at the end of the day translate into economic growth.
Another pocket currently experiencing decent growth is the one that contains Demands Posits at domestic commercial banks and US branches of foreign banks. From November 2013 to November 2014 the pocket increased its amount by 147 billion dollars. Such amount represents almost 15% change in just one year. Roughly speaking, US branches of foreign banks hold on average 14% of the total money contained in the Demand Deposits pocket.
Finally, there is the richest pocket –the Vaults of commercial Banks- in the United States: all other money that is out of street circulation, which includes thrift institutions, savings deposits, retail money funds and small denomination time deposits. On the Graph this pocket is tagged as “Total non-M1 M2”. This pocket enlarged its amount by 394.70 billion from November 2013 to November 2014, which represents a percentage change of 4.7% for the same period.
Note: All used data in this article were seasonally adjusted.
Data source: Federal Reserve Statistical Release. December 18th 2014.