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.

 

“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.

 

 

“Core” inflation rate will have huge influence on monetary policy next month.

Second Estimates for real GDP growth in the United States indicate that the economy grew at 3.7 percent during the second quarter of 2015 after correcting by price change. The report from the Bureau of Economic Analysis informs that the change mainly derived from positive contribution of consumer spending, exports, and spending of state and local governments. These increases are said to have been offset by a deceleration in private inventory investment, federal government investment, and residential fixed investment. The revised figure for first quarter of 2015 went up from -0.7 percent to 0.6 percent.

Besides real GDP calculations stand the estimates for prices changes in goods and purchases made by American residents that the Bureau of Economic Analysis (BEA) does simultaneously to the calculations made by the Bureau of Labor Statistics (BLS). In this regard, this time around the second quarter, prices had a positive growth of roughly 1.6 percent, which the BEA reports was derived from an increase in both consumer prices, and prices paid by local and state governments. Please bear in mind that, in the first quarter of 2015, prices were said to have dragged down the GDP numbers since the index decreased by roughly 1.1 percent change.

H&M Store in Broadway NYC. By Catherine De Las Salas. Summer 2015.

H&M Store in Broadway NYC. By Catherine De Las Salas. Summer 2015.

These price changes are actually good news for the Federal Reserve System for whom a moderate upswing in inflation helps them to achieve their yearly monetary goal of 2.0 percent inflation rate. And for those of whom like to make economic forecast, these figures mount onto their analysis for determining whether or not the Federal Reserve will increase interest rates in September. So, although real GDP measures are certainly corrected for price changes, the BEA’s price index will -on its own- have huge influence on monetary policy options for the months to come.

Thus, relevant data nowadays stem from BEA’s “core” inflation rate, which is to say price change without food prices and energy prices. Indeed, when figures isolate energy and foods volatility, the measure of inflation reaches 1.8 percent change from the first quarter of 2015. These changes in prices and output rightly affect the wallet of American residents. Price changes, plus increases in output -which reflect decreases in unemployment rate- may take consumer and producers to edge up their spending, which was one of the factor behind positive change in real GDP growth as mentioned above. Then, whenever spending tends to accelerate beyond its capacity the Federal Reserve reacts with an increase in interests rates. Even though one could argue that such is not currently the case, given that data on capacity utilization clearly shows that the American Economy has room to further spending, the BEA’s “core” inflation will be the measure that could possible make Federal Reserve Officials think twice about interest rates.

So, the puzzle about what the Federal Reserve will end up doing next Federal Open Market Committee meeting is fourfold, and it will derive from the different sources of data: first, price change data from BEA, which BEA claims to be way more “accurate” than BLS’. GDP growth from BEA, which is calculated by correcting price changes with their own price index. Price change from BLS, which may vary from BEA’s calculations. And capacity utilization from the Federal Reserve, which is whom finally decides on interest rates changes.

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.

 

 

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.

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Email: giancarlo[at]econometricus[dot]com
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Is the Current U.S. Dollar Strong All Over the World?

The “Strong Dollar” factor:

“Strong Dollar” is said to be one of the main causes explaining the poor economic performance of the United States during the first quarter of 2015. United States’ exports were affected by the appreciation of the currency vis-à-vis some of the foreign currencies. Likewise, exports towards the United States benefited from the U.S. Dollar appreciation. In aggregated terms, currencies of major U.S. trade partners, versus the U.S. dollar, have gone up in average by roughly 3.2% in the last five months of 2015. This trend can be seen in the picture below on blue line (1 Broad). Furthermore, a subset of other major currencies that circulate worldwide have appreciated since January by an average of 5.7% against the U.S. dollar, which can be observed in the orange line in the graph below (2 Major Currencies). The euro, instead, has depreciated 8.3% since the beginning of the year (Yellow line in the graph below). There is where the competitive advantage resides for Europe nowadays.

Currency

Who’s to blame?

When it comes to blame foreign trade for poor economic performance in US, Asian countries come to mind of Americans. For economic growth on 2015Q1, such a statement seems to be somewhat ambiguous. The graph below shows how major Asian currencies have performed recently. The Yuan, dotted white line in the graph, has been mostly steady for what has bygone of 2015. Otherwise, the currency that has depreciated the most, amongst Asians monetary markets, is the Taiwanese Dollar. The Indian Rupee initiated the year with a downward trending which lasted until the second week of April; then, the Rupee started to gain value against the U.S. Dollar. South Korean currency, the Won, had its lowest value against U.S. Dollar this year by the last week of April. The Won did so after having been depreciating since the first week of March. Dollar from Singapore has also been depreciating since early March after a spike in its value against dollar (Yellow line in the graph).

Currency 2
In order to elaborate these currency series, we took the value of the currency against the U.S. Dollar at its value during the first week of 2015, and use such value as index. The data source is the Board of Governors of the Federal Reserve System, which aggregates the currency data for major U.S. trading partners. The Federal Reserve defines these aggregated data as follows:
1) Broad: “A weighted average of the foreign exchange value of the U.S. dollar against the currencies of a broad group of major U.S. trading partners”.
2) Major Currency: “A weighted average of the foreign exchange value of the U.S. dollar against a subset of the broad index currencies that circulate widely outside the country of issue”.
3) OITP: “A weighted average of the foreign exchange value of the U.S. dollar against a subset of the broad index currencies that do not circulate widely outside the country of issue.

Did the housing market affect negatively economic growth in 2015Q1?

Recent news on GDP 2015Q1 have many economists wondering about the possible domestic causes for such a negative growth (-.7%). The U.S. Bureau of Economic Analysis (BEA) did not hesitate in pointing out towards Investment in non-residential structures, which decrease 20%. Perhaps, data on housing market from both Construction Spending and Existing Home Sales might advance clues on what is going on in the U.S. economy currently. First, preliminary data on Construction Put in Place might shed light into what BEA signaled earlier, and data on Existing Housing Sales may complement an explanation, at least for as far as to the domestic economic dynamic concerns.

A

First, the Total Value of Residential Construction Put in Place in the U.S. economy decreased by 1.8% when comparing April 2014 to the most recent estimated statistics from the U.S. Census Bureau for April 2015. The estimated value for Private Residential Construction in April 2015 was roughly 353,086 million dollars, which totals 7,740 million less put in place than in April 2014. In spite of the decrease during April, official at the U.S. Census Bureau stated that “during the first 4 months of this year, construction spending amounted to $288.7 Billion, 4.1 percent (+/-1.5) above $277.3 Billion for the same period 2014”.

B

Perhaps the deceleration for the sector is being brought by Residential and Power sectors. The preliminary value of construction put in place for Residential and Power -type of constructions- went down during April 2015 inasmuch of -6,417 and -11,657 million dollars correspondingly, much of which came from a decrease of roughly 7,850 million dollars less pertaining the private sector and -3,808 million dollars less from the public sector. Though, the overall account got offset by increases in Manufacturing, Transportation and Commercial.

C

Since most of Construction Spending indicators went up in April 2015p, the question to ask economists is to whether or not the housing market actually slowed down economic growth during the first quarter of 2015; at least for the domestic side of the U.S. economy. Construction growth in Lodging and Commercial industries went up both by 17%, while Offices and Recreation related constructions did so by roughly 20% (April 2014 compared to April 2015p).

D

Data Source: U.S. Census Bureau. Data Overview: “The Value of Construction Put in Place Survey (VIP) provides monthly estimates of the total dollar value of construction work done in the U.S. The United States Code, Title 13, authorizes this program. The survey covers construction work done each month on new structures or improvements to existing structures for private and public sectors. Data estimates include the cost of labor and materials, cost of architectural and engineering work, overhead costs, interest and taxes paid during construction, and contractor’s profits. Data collection and estimation activities begin on the first day after the reference month and continue for about three weeks. Reported data and estimates are for activity taking place during the previous calendar month. The survey has been conducted monthly since 1964”.

Real US GDP increased 5.0 percent in the third quarter of 2014: BEA.

1

Real Gross Domestic Product increased 5.0 percent in the third quarter of 2014, the US Bureau of Economic Analysis reported today January 22 of 2015. The largest contributor for its expansion was the Finance, insurance, real state, rental and leasing Industry with a significant 20% of the total value added to GDP during the third quarter of 2014. Real State and leasing industry contributed 13 percent while the Finance and Insurance contributed 7.4 percent. The actual change in Value Added of the Finance industry was 21.2 percent when compared to the second quarter 2014, from which it had grown previously only 6.0 percent. Real Value Added is a measure of an Industry’s contribution to GDP given in constant prices (2005) rather than current prices.

Value Added by Industry group as a Percentage of GDP during the third quarter of 2014 was largely driven by the Finance and Insurance Industry. The second contributors for total GDP Value Added were both manufacturing Industry as well as Professional and Business Services Industry, which both contributed with 12 percent each. The public sector contributed with 9 percent of the GDP Value Added for the third quarter 2014. Education and health care also bolstered GDP Value Added largely with 8 percent.

These data point out toward a more convincing signals of a solid path of United States GDP expansion. First quarter of 2014 posed many question about the strength of the economic recovery from the Great Recession.

Take a look at Real Value Added by Industry:

2
Real Value Added by Mining industry augmented by 25.6 percent, which meant its largest increase since the fourth quarter of 2008. It contributed 3 percent of the 5% GDP increase.

3
Utility Industry which contributed 2 percent out of the 5 percent GDP growth, showed a 18.2 percent change from the preceding period.

4
Real Value Added by Construction industry registered a small 2.3 percent change during the third quarter of 2014. Construction as a whole industry enlarged by 4 percent the total GDP Value Added for the same period.

5
Manufacturing barely changed with a small 0.5 percent from the second quarter of 2014, though it still made up 12 percent of the total Value Added to GDP for the third quarter 2014.

6
Real Value Added by the Wholesale trade industry registered a 7.3 percent change from period before. Wholesale industry made up 6 percent of the third 2014 quarter change. (Learn more details on Wholesale trade industry during 2014)

7
Retail trade industry changed 1.1 percent and contributed the 5 percent GDP change by 6 percent. (See more details on Retail trade Industry).

8
Real Value Added by the Transportation and Warehousing Industry changed 6.7 from preceding period. Such increase represents 3 percent of the total GDP change of third quarter 2014 (Read more on industries related to oil).

9
Information Industry contributed 5 percent to GDP growth during the third quarter 2014, which came out of a 6.4 percent change of Real Value Added from preceding period.

10
Real State, Rental and Leasing also grew its Value by 4.4 percent from the preceding period. The entire industry, which includes Finance and Insurance contributed 21 percent.

11
Real Value Added by the Professional and Business services Industry experienced a 5.3 percent change during the third quarter of 2014. Professional Services Industry’s Value Added as a percentage of the total GDP represented 12 percent.

12
Education services and Health care industries accounted for 8 percent change of the total 5 percent GDP Value Added during third quarter 2014.

13
Real Value Added by Arts, recreation, Food Services, Entertainment added value at 5.1 percent when compared to the period before the third quarter 2014. This Industry as a group made up 4 percent of the total value added to GDP for the same period.

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