Recent Narratives of Stock and Bond Bubbles.

On February 5th, 2018, Dow Jones index fell 1,175 points after the trading day. Four economic scenarios are being analyzed in the news as of the first week of February 2018. First, there are indeed both Stock Market and bonds Bubbles. Second, the Monday Dow’s selloff is just an anticipated correction move on the investor’s side. Third, the stock market returns to the old normal of higher levels of volatility. Fourth, Trump economic effect. I need not to cover the concerns on the US economy nowadays in this blog post. Hence, the analysis that I think is needed currently is the ruling out of a contagious effect from the narratives created around the Dow’s selloff on Monday. Indeed, I believe that such narrative, if any, can be traced back to former chairman Alan Greenspan’s comments when he stated on January 31st that America has both a bond market and stock market bubbles. By discarding the contagious effect in current narratives, I side with analysts who have asserted that the Dow’s fall was just an anticipated market correction.

Can economists claim there is some association between Alan Greenspan’s comments and the Monday fall of the Dow Jones? I may not have an answer for that question yet, but We can look into the dynamics of the phenomenon to better understand how narratives could either deter or foster an economic crisis in early 2018. If there is room for arguing that Mr. Greenspan’s comments triggered the Dow Selloff on Monday, I believe we should be observing some sort of panic or manifestation of economic anxiety. By looking at data from Google Trends, I spot on breakouts that may well be understood as “spreading” symptoms. In other words, if there is any effect of Mr. Greenspan’s comments on the Dow’s selloff on Monday, we should expect to see an increase in Google searches for two terms: first “Alan Greenspan”, and second “Stock Market Bubble.” The chart below shows google trends indexes for both terms. Little to nothing can be said about the graph after a visual inspection of the data. It is hard to believe that there are narratives of economic crisis fast-spreading, nor have Mr. Greenspan’s comments had any effect on the Dow’s sell-off.

How did things occur?

Economists are lagging on the study of narratives, hence the limited set of appropriate analytics tools. Robert Shiller wrote early in 2017 that “we cannot easily prove that any association between changing narratives and economic outcomes is not all reverse causality, from outcomes to the narratives,” which is certainly accurate whenever time has passed as empirical evidence become obscure. However, on February 1st of 2018 mainstream media reported extensively a couple of statements made by Alan Greenspan about bubbles. In the following two days, several market indexes closed with relatively big loses. In detail, the events occurred as follows:

  1. On January 31st, 2018 Alan Greenspan told Bloomberg News: “There are two bubbles: We have a stock market bubble, and we have a bond market bubble.”
  2. On February 5th, 2018, Dow Jones index falls 1,175 point after the trading day on Monday.

Whenever these events happen, we all rush to think about Robert Shiller. As Paul Krugman cited Shiller today February 6th, 2018, “when stocks crashed in 1987, the economist Robert Shiller carried out a real-time survey of investor motivations; it turned out that the crash was essentially a pure self-fulfilling panic. People weren’t selling because some news item caused them to revise their views about stock values; they sold because they saw that other people were selling”. In other words, Robert Shiller’s work on Narrative Economics is meant for these types of conjectures. Narratives of economic crisis play a critical role in dispersing fear whenever economic bubbles are about to burst. One way to gauge the extent to which such a contagious effect occurs is by looking at google trend search levels.

 

 

No signs of fast-spreading economic crisis narratives:

Despite the ample airtime coverage, there is little to none evidence of a market crash and economic crisis. In the wave of fast pace breaking news announcing crisis and linking them to political personalities, markets seem just to be having an expected correction after an extended period of gains. The best way to conclude such correction is by looking at the firm numbers reported lately on jobs markets as well as to investigate the collective reaction to fear and expectations. Thus, four economic scenarios are being analyzed as of the first week of February. First, there are Stock Market and bonds Bubbles. Second, the Monday Dow’s selloff is just an anticipated correction move on the investor’s side. Third, the market returns to the old normal of higher levels of volatility. Fourth, Trump effect. None of the scenarios seem plausible to me. First, the selloff appears not to have dug into the investors and people’s minds, thereby avoiding the contagious effect. Second, despite the unreliability of winter economic statistics, jobs reports on January 2018 seem optimistic (I think they will revise those number low). Third, claiming volatility is back to the stock market is like claiming Trump is back into controversy. Therefore, the only option left to explain Monday’s selloff is the argument of a market correction.

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 .

Is the Google search term “Dollar Rate” an useful predictor for economic crisis?

Economic conditions in the United States are so unchanging that economists started to explore global conditions as potential threats to its economy. Without ruling out financial institutions altogether, economists are confident current regulation will keep turbulence away. Also, the household sector appears solid for many analysts while fiscal issues seem not to alarm anybody. Nevertheless, the reality is that current economic conditions look much as an economic boom that nobody knows where it is going to burst from. This situation makes economic fears to hide just in front of analysts. Given that the economic expansion that started with the economic recovery from the Great Recession is about to reach six years now, one useful way to try to anticipate ambiguous economic situations could be by looking at Google Trends insights. Mainly, Google searches for certain terms such as “Dollar rate”. However, China’s battle against Google limits the most interesting insight we could possible get from China’s economic situation as it develops.

Factoring in potential risks:

Since economists believe in economic cycles and expansions that last for about ten years in average, the current development should start factoring in potential risks. Thus far, in regards to the American economy, there appear to be two suspicious economic sectors that might be fueling economic anxiety as current expansion continues to grow. On one hand low oil prices are generating a reallocation of resources that is allowing many sectors of the economy to grow. On the other hand, inflows of international capital migrating to the United States could be signaling countries under pressures. The latter issue is where analysts are looking for potential threats. In this article, econometricus looks at data that could hold some clues on where turbulence could be starting to grow abroad. Indeed, one of the Americans’ biggest concerns is about China’s economic performance. The question “is it safe to invest in China?” has popped up as one of the most question asked the last two years, even over concerns about Greece.

Remembering 2015 and Google query “Dollar rate”:

Let us start by remembering that 2015 exhibited panic for international spillovers of default of local economies. Greece had the world wondering who is next, while Greeks looked for currency alternatives (Graph 1). That same question has U.S. analysts looking for potential risks from the international community. Thus, using big data and Google search terms may help researchers to track concerning developments. Indeed, growing interest for exchange rate could hold a clue for analysts as Google searches unveil the geography of those interest.

So, econometricus took the Google query “Dollar rate” as a proxy for the interest in exchanging local currency random people could have. In other words, if people worry about economic condition in a given country, they will try to exchange local currency into U.S. Dollar. Such behavior could unveil early developments in big capital outflows from key trading partners. Therefore, looking at those Google searches might illuminate analysis for identifying potential global threats.

Graph 1.

Dollar rate 3

Graph 2

Dollar rate 4

Although it could be helpful to assume money tenders tend to sell local currency and exchange it for foreign dollars, it is still hard to claim that Google searches are useful predictors for economic crisis. Econometricus does not claim that by any means. However, when complemented with other data, Google searches may help picture a better analysis and, what is more relevant, Google searches could illuminate real time developments. So, let us try to see where Google could take us.

China blocked the sensitive information on Google:

Unfortunately data on Google searches will not take the analysis any where as far as the most pressing issues regards. The most concerning country nowadays, China, blocked the sensitive information on Google. Graph 2 shows how China’s data on US currency searches has been blocked for retrieval since May 2014, which limits this analysis. Nevertheless, Google still offers others country data such as Brazil, which has also been closely watched by analysts. Brazil shows an upward trend in interest for “Dollar rate” term (Graph 2). Instead, Taiwan shows a steady trend (Graph 2). Mexico and Canada, the two biggest trading partners after China, appear to have a growing interest in US dollars (Graph 3).

Graph 3

Dollar rate 2

Graph 4

Dollar rate

Finally South Korea, Germany, and the United Kingdom. Among these three nations, only South Korea shows significant increases in the search term “Dollar rate”. Perhaps the UK may exhibit a bit of interest recently. However, it is not clear right now to what extent it could relate to economic troubling factors. We all wish we can count on China’s data so that the analysis could be expanded properly. Sadly, that is not the case as of March 2016.

“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.

 

 

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.

Follow up on US Construction Industry Data.

Follow up on US Construction Industry Data.

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

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

Construction summer statistics by region.

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

Privately-Owned Housing Units.

Privately-Owned Housing Units.

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

United States Housing Units Completed on July 2015.

United States Housing Units Completed on July 2015.

 

Northeast Housing Units Completed on July 2015.

Northeast Housing Units Completed on July 2015.

 

Midwest Housing Units Completed on July 2015.

Midwest Housing Units Completed on July 2015.

 

West region Housing Units Completed on July 2015

West region Housing Units Completed on July 2015

 

South Region Housing Units Completed on July 2015.

South Region Housing Units Completed on July 2015.

 

 

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.

 

In July’s retail sales, Food and Drinking Services is King.

With some exceptions, retail sales for the month of July showed positive signs. As the summer season fades down, July sales’ advance estimates show good increases in food and drinking services (9%), Furniture (6.1%) and Building materials (2.8). Those three lines boosted the annual change in sales, according to data from the U.S. Census Bureau. In aggregated terms, the most significant changes were in food services, which showed a 2.4% increase when compared to the same month 2014.

Food sales July 2015

Food sales July 2015

 

Retail trade sales were little changed from July 2014 given a sharp decline in Gasoline sales, electronic appliances and sales at Department Stores. Regarding gasoline, it is most than natural that the nominal value had deceased since oil price is still at record lows. On the other hand, Health and personal care store sales were 3.1 percent change along with the same figure for Clothing and accessories stores. Sales of Sporting goods, hobby, books and music increased by 6.4 percent when compared to the same month 2014.

Health sales July 2015

Health sales July 2015

Good news were mostly on sales of Furniture and Building materials. Sales of Furniture stores increased by 6.1 percent, while Building materials, garden equipment and supplies did so by 2.8 percent. Electronics, as already mentioned, declined on sales by -2.5 percent.

Furniture Sales July 2015

Furniture Sales July 2015

Retail sales in Department stores also declined by -2.7 percent. Other retailers such as miscellaneous stores and nonstores retailer increased their sales in July by 3.1 and 6 percent respectively.

Dept Stores Sales 2015

Dept. Stores Sales 2015

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