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.

 

 

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

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

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

Consumer Price Index and Producer Price Index

Consumer Price Index and Producer Price Index

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

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

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

Final Demand Index less Foods and Energy.

Final Demand Index less Foods and Energy.

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

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

Expect an increase in interest rates:

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

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

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

By Catherine De Las Salas. August 2015.

By Catherine De Las Salas. August 2015.

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

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

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

 

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

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

 

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

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

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

By Catherine De Las Salas

By Catherine De Las Salas.

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

Managers play a roll either in cutting or increasing wages:

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

The model:

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

Thus, the statistical model would look like the following:

Model

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

Data and method:

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

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

Results:

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

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

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

Regression output table:

Insurance

31 Data Sources, Surveys and Metrics for Doing Research on U.S. Labor Market.

If your research project encompasses facts on U.S. Labor Market, here are some useful data sources and metrics that might illuminate insights for your research. Although there might be some discrepancies between what you narrowed as your research question and the data sources showed below, chances are you will find a set of metrics that might capture a good proxy for your research topic.

Look through the list and then identify a possible match between your research question and the data source:

1. Employment and Unemployment (Regional, County, National and Metropolitan Area). Data source: U.S. Bureau of Labor Statistics.
2. Unemployment Insurance Claimants. Data source: U.S. Department of Labor.
3. Real Earnings. Data source: U.S. Bureau of Labor Statistics.
4. Labor Force Characteristics of Foreign Born Workers. Data source: U.S. Bureau of Labor Statistics.
5. Job Opening and Labor Turn Over. Data source: U.S. Bureau of Labor Statistics.
6. Employment Situation. Data source: U.S. Bureau of Labor Statistics.
7. ADP Employment. Data source: ADP.
8. Productivity and Cost. Data source: U.S. Bureau of Labor Statistics.
9. Employment Cost. Data source: U.S. Bureau of Labor Statistics.
10. Personal Income and Outlays. Data source: U.S. Bureau of Economic Analysis.
11. Business Employment Dynamics. Data source: U.S. Bureau of Labor Statistics.
12. Employment Characteristics of Families. Data source: U.S. Bureau of Labor Statistics.
13. Usual Weekly Earnings of Wages and Salaries of Workers. Data source: U.S. Bureau of Labor Statistics.
14. College Enrollment and Work Activity of High School Graduates. Data source: U.S. Bureau of Labor Statistics.
15. Number of Jobs, labor market experience (Longitudinal Survey). Data source: Bureau of Labor Statistics.
16. Occupational Employment and Wages. Data source: U.S. Bureau of Labor Statistics.
17. State and Local Personal Income and Real Personal Income. Data source: U.S. Bureau of Economic Research.
18. Employment Situation of the Veterans. Data source: U.S. Bureau of Labor Statistics.
19. Employer Cost for Employee Compensation. Data source: U.S. Bureau of Labor Statistics.
20. Volunteering in the U.S. Data source: U.S. Bureau of Labor Statistics.
21. Major Work Stoppages. Data source: U.S. Bureau of Labor Statistics.
22. Mass Layoffs. Data source: U.S. Bureau of Labor Statistics.
23. Union Members. Data source: U.S. Bureau of Labor Statistics.
24. Employee tenure (2014). Data source: Bureau of Labor Statistics.
25. Consumer Expenditure (2013). Data source: U.S. Bureau of Labor Statistics.
26. Summer Youth Labor Force. Data source: U.S. Bureau of Labor Statistics.
27. Employee Benefits (Private sector). Data source: U.S. Bureau of Labor Statistics.
28. Persons with Disabilities Characteristics. Data source: U.S. Bureau of Labor Statistics.
29. Employment Projections 2012-2022. Data source: U.S. Bureau of Labor Statistics.
30. Income of the 55 and older. Data source: U.S. Social Security Administration.
31. Women in Labor Force (2012). Data source: U.S. Bureau of Labor Statistics.

We can support your research:

Econometricus.com helps Researches in understanding the economic situation of specific industry, sector or policy by looking at the United States’ labor market environment. “U.S. Labor Market Analysis” starts by summarizing statistics on Income, Labor Productivity, and General Conditions of Labor Market. Applied-Analysis can be either “Snapshots of the U.S. Economy” or historic trends (Time-series Analysis). Our clients can rely on a thorough and exhaustive data driven analysis that illuminates forecasting and economic decision-making. Clients may down-size or augment the scope of the research as to tailor it to their needs.

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Email: giancarlo[at]econometricus[dot]com
Call: 1-917-825-5737

14 Data Sources, Surveys and Metrics for Doing Research on U.S. Macroeconomic Performance.

If your research project encompasses facts on the Macroeconomic Performance of the U.S. Economy, here are some useful data sources and metrics that might illuminate insights for your research. Although there might be some discrepancies between what you narrowed as your research question and the data sources showed below, chances are you will find a set of metrics that might capture a good proxy for your research topic.

Look through the list and then identify a possible match between your research question and the data source:

1. Gross Domestic Product (Regional, State, Metropolitan Area): U.S. Bureau of Economic Analysis.
2. Money Stock Measures: Federal Reserve System, Board of Governors.
3. U.S. Imports and Exports Price Indexes: U.S. bureau of Labor Statistics.
4. Selected Interest Rates: Federal Reserve System, Board of Governors.
5. Consumer Price Index / Producer Price Index: U.S. bureau of Labor Statistics.
6. Federal Open Market Committee Minutes: Federal Reserve System, Board of Governors.
7. Industrial Production and Capacity Utilization: Federal Reserve System, Board of Governors.
8. Monthly Treasury Statement: U.S. Bureau of the Fiscal Service.
9. Consumer Credit: Federal Reserve System, Board of Governors.
10. U.S. International Trade in Goods and Services: U.S. Bureau of Economic Analysis.
11. Senior Loan Officer Opinion Survey: Federal Reserve System, Board of Governors.
12. Beige Book. Summary of commentary on Current Economic Conditions: Federal Reserve System, Board of Governors. By District
13. U.S. International Transaction – Current Account: U.S. Bureau of Economic Analysis.
14. State and Local Tax revenue: U.S. Census Bureau.

We can support your research:

Econometricus.com helps Social and Political Scientist Researchers in understanding the economic situation of a specific industry, sector or policy by looking at the United States’ Macroeconomic environment. Econometricus.com may guide you through empirical data on Economic Growth, Monetary Pressures, Fiscal Spending, Current Account, and Employment. Applied-Analysis can be either “Snapshots of the U.S. Economy” or historic trends (Time-series Analysis). Our clients can rely on a thorough and exhaustive data driven analysis that illuminates forecasting and economic decision-making. Clients may down-size or augment the scope of the research as to tailor it to their needs.

Get a quote from econometricus.com.
Email: giancarlo[at]econometricus[dot]com
Call: 1-917-825-5737

20 Data Sources and Surveys for Research on U.S. Economic Prospective.

If your research project involves aspects on the general prospective of U.S. Economy, here are some useful data sources, surveys and metrics that might illuminate insights for your research. Although there might be some discrepancies between what you narrowed as your research question, and the data sources showed below, chances are you will find a set of metrics that might capture a good proxy for your research topic.

Look through the list and then identify a possible match between your research question and the data source:

1. Texas Manufacturing Outlook Survey. Data source: Federal Reserve Bank of Dallas.
2. Fifth District Survey of Manufacturing Activity. Data source: Federal Reserve Bank of Richmond.
3. Kansas City Manufacturing Survey. Data source: Federal Reserve Bank of Kansas.
4. Manufacturing Business Outlook. Data source: Federal Reserve Bank of Philadelphia.
5. Empire State Manufacturing survey. Data source: Federal Reserve Bank of New York.
6. Durable Goods Manufacturers. Data source: U.S. Census Bureau.
7. Retail Trade, Monthly and Annual. Data source: U.S. Census Bureau.
8. Manufacturing Trade Inventories. Data source: U.S. Census Bureau.
9. Monthly Whole Sale Trade. Data source: U.S. Census Bureau.
10. Manufacturers’ shipments, Inventories and Orders. Data source: U.S. Census Bureau.
11. Corporate Profits. Data source: U.S. Bureau of Economic Research.
12. Tourism Sales. Data source: U.S. Bureau of Economic Research.
13. Services, annual and quarterly. Data source: U.S. Census Bureau.
14. Fifth District Survey of Services. Data source: Federal Reserve Bank of Richmond.
15. Chicago Fed National Activity Survey Economists. Data source: Federal Reserve Bank of Chicago.
16. Livingston Survey Economists. Data source: Federal Reserve Bank of Philadelphia.
17. E-Commerce. Data source: U.S. Census Bureau.
18. Survey of Professional Forecasters. Data source: Federal Reserve Bank of Philadelphia.
19. National Economic Trends. Data source: Federal Reserve Bank of St. Louis.
20. National Transportation Statistics. Data source: U.S. Bureau of Transportation Statistics

At Econometricus.com we help Social Science Researchers in understanding the economic situation of specific industry, sector or policy by looking at the United States’ general economic environment. We support Researchers by summarizing leading Economists Consensus about the current conditions of the U.S. Economy. Also, we do analyze empirical data on Manufacturing Activity, Trade and Commerce, and Service Sector. Applied-Analysis can be either “Snapshots of the U.S. Economy” or historic trends (Time-series Analysis). Our clients can rely on a thorough and exhaustive data driven analysis that illuminates forecasting and economic decision-making. Clients may down-size or augment the scope of the research as to tailor it to their needs.

Get a quote from econometricus.com.
Email: giancarlo[at]econometricus[dot]com
Call: 1-917-825-5737