How and when to make “policy recommendations”.

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

To identify and describe the policy issue:

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

Proceed with the identification of metrics:

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

Advance with the configuration of the database:

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

Run regressions:

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

Summary ARIMA Model.

Summary ARIMA Model.

Compare results with other researchers’ estimates:

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

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

Correlograms and Summary Statistics.

Correlograms and Summary Statistics.

The American Statistical Association just made it easier to use p-values.

The American Statistical Association made it easier for researchers to use p-values. By laying down few simple principles, ASA aims at improving the understanding of a commonly misused statistic. Most importantly, ASA stresses the relevance of rigor in research over a mere “p-hacking” practice. In its recent statement, ASA concludes that “no single index should substitute for scientific reasoning.”

The Boards of Directors of ASA starts by acknowledging the expansion in the use of data. The recent proliferation of data-sets has fostered an unprecedented interest in statistical analysis. Along with this interest has come the need for validation of research and analysis through the extensive use of data. Thus, the first instrument in such validation is statistical significance for which the p-value allures oversimplification of statistical rigor. Mistakes in interpreting the statistic are such that “some scientific journals are discouraging the use of p-values”.

ASA explains the concept of a p-value “informally”, by stating what is and what is not.

The first principle ASA refers to, in its statement, is the reason behind the simplification in the use of p-values. P-values give an indication of “how incompatible the data are with a specified statistical model”. In ordinary words, to what extent the null hypothesis can be rejected by the index. “The smaller the p-value, the greater the statistical incompatibility of the data with the null hypothesis.” The second aspect of p-values that ASA points out links its limits about the researcher’s hypothesis.

The p-value either approves or disapproves the validity of the used data in connection with the specified hypothesis, exclusively. The emphasis here is the limit of the p-value, which is not to assert the validity of the hypothesis itself, but rather to bolster either the pertinence or impertinence of the data being used by the research. Neither does the p-value measure the size nor the importance of an effect of the result.

By Catherine De Las Salas

By Catherine De Las Salas

That being said, it looks like p-values are certainly essential for reaching scientific conclusions. However, they are not the only criterion for analyzing data and in doing research. But, its relevance has derailed researchers to become “p-hackers” in search solely of what ASA calls a “mechanical ‘bright line’ rules (such as “p<0.05″)”. ASA urges researchers to outpace the mere use of the p-value as the main criterion for statistical analysis by advocating for contextual factors in research.

The fourth issue stated by ASA refers to transparency in reporting research findings. For ASA, “p-values and related analysis should not be reported selectively”. It is the researcher’s duty to report and disclose the rationale behind data collection, analysis, and computations.

ASA’s Conclusions.

ASA reaches two main conclusion in its paper. The statement advocates for ending the use of a single index as a substitution for scientific reasoning as well as the relevance of a good practice of research. The Board of Directors emphasized that scientific research must correspond to high standards of research and study design, appropriate numerical and graphical summaries of data, cogent understanding of the phenomenon being studied, interpretation of the results in context, complete reporting, and the understanding of what data summaries mean.

Reference:

Ronald L. Wasserstein & Nicole A. Lazar (2016): The ASA’s statement on p-values: context, process, and purpose, The American Statistician, DOI: 10.1080/00031305.2016.1154108

US-China trade: There are two sides to every story.

The currency seems to have a negative effect…

There are two sides to every story, even for US-China foreign trade. Ever since China emerged to the world economy as a major manufacturing powerhouse, United States started to lose jobs in the manufacturing sector. Once upon the time firms of manufactured goods such as shoes, clothes even electronics, begun to move their production plants to China’s populous cities looking for an edge in low salaries. However, that trade story with China is oversimplified and misleading. Given that Donald Trump points to currency manipulation for blaming China for U.S.’ losses, I took data on Renminbi’s “depreciation” from January 2009 up to the end of 2015, and regressed it against the value of shipments of the American manufacturing sector. Yes, it does, the currency seems to have a negative effect on the value of shipments in the aggregate. Nonetheless, there are also gains on the U.S.’ side.

I wanted to see quickly to what extent a mere variation of the China’s currency would have an effect on U.S.’ manufacturing production. Then, the stats that I chose for analyzing this phenomenon were the value of shipments (see below for definition) made by U.S. manufacturing firm’s facilities . Then, I took the variation of the Renminbi as recorded by the U.S. Federal Reserve Bank. That is a ratio between nominal measures of the U.S. Dollar and the Yuan. The initial date is January of 2009 for all the time series. The final month is December of 2015.

By Catherine De Las Salas

By Catherine De Las Salas

During this period, China’s currency has been allegedly devaluated down to at least 5 percent. The results bolster Trump’s idea that China’s currency takes a toll in American manufacturing. Though, I do not aim at proving that for these reasons jobs have moved from U.S. to China. Nevertheless, there are also gains for some of the industries within the United States.

Finding statistical significance in these time series is hard:

Finding statistical significance in these time series is difficult. Just for the sake of the debate, I lowered the statistical threshold by amplifying the confidence intervals even down to 80 percent. That way I could achieve a bit of evidence of the trade impact of China’s currency on American manufacturing sector. Twelve items stood out of the rest. Positive coefficients could be found in Wood Products, Metal Machinery, Turbines and power transmission equipment, and Pharmaceutical goods. Note that statistical significance in these cases is down to 80 percent. So, if anyone ever would like to make a case out it, one has to be cautious with any assertion. Nevertheless, those coefficients are still positive and deserve some attention whenever generalizations come to drive the debate about U.S.-China’s trade.

On the other hand, negative coefficients showed up in eight items. The most important line, total manufacturing, registered a negative coefficient (-.42) with statistically significant at the 80 percent level. Total manufacturing excluding defense also classified with a negative coefficient of -.47. Nondurable goods revealed a negative coefficient of -.60 percent.

Below is the list of items and their correspondent coefficients alongside the confidence levels. Remarked in red cells are items with negative coefficients, whereas items with positive coefficients are noted in green cells. Here I also attached the database (Renmimbi US Manufacturing).

Results:

Table of coefficients.

“Value of shipments covers the received or receivable net selling values, f.o.b. plant (exclusive of freight and taxes), of all products shipped, both primary and secondary, as well as all miscellaneous receipts, such as receipts for contract work performed for others, installation and repair, sales of scrap, and sales of products bought and resold without further processing. Included are all items made by or for the establishments from materials owned by it, whether sold, transferred to other plants of the same company, or shipped on consignment. The net selling value of products made in one plant on a contract basis from materials owned by another was reported by the plant providing the materials”.

United States meets protectionism: anti-trade sentiment will not last further than the primaries.

Journalists seem alarmed by the fact that the Republican and Democratic primaries have the potential to shift a historical stance on U.S. trade policy. That is, most of the American governments during the 20th century can be characterized as free traders, and that there is a particular association between free trade policies with the Republican Party. This tradition seems to be forgotten as Donald Trump leads national Republican polls. Indeed, the real estate mogul claims that his trade policy will shift American policy from free trade to fair trade. His main argument stems from the assertion that China manipulates its currency thereby affecting American workers. Although political rhetoric may lure votes from radicals momentarily, consumer-voters will drive ultimately the opinion toward the effect trade has in their pockets, especially the Republican ones. In other words, the anti-trade sentiment will not last further than the primaries.

Donald Trump’s electoral coalition:

Paradoxically, nowadays the political far left coincides with the far right when it comes to blaming China for the economic stagnation of the United States. These two extreme positions may end up unexpectedly joining efforts for winning the White House. The fact that Donald Trump could steal voters, from the far left of the political spectrum by speaking against trade, represents a real threat to the establishment (either left or right). Thus, such a threat leads to the perception that it may contribute to building Donald Trump’s electoral coalition for the White House.

Nonetheless, any person may note that the campaign trial exacerbates the rhetoric as primaries demand presidential candidates to radicalize as much as possible. Bear in mind that the main goal of primaries is to reach out to the partisan constituency solely, which makes politicians deepen their positions. However, such a trend on radicalization tends to disappear once politicians get the party nomination. The challenge this time, though, is that there seems to be a tacit agreement between the two mainstream ideological stands. Both Democrats (Sanders) and Republicans (Trump) are pushing foreign trade into the political debate. Both sectors are calling for revisions of U.S. trade policies.

By Catherine De Las Salas

By Catherine De Las Salas

Both parties are getting into a widely known debate about the “perverse” effects of trade on domestic production. Unlike U.S., the world has known these arguments since the beginning of the Cold War, which made the United States took an initial stance against protectionism pushed by the geopolitical fight against the Soviet Union. With the “end of history”, as Mr. Francis Fukuyama would put it, it is time for the United States to meet protectionism. Perhaps this recent development in policy debates may change the current agreements. However, the discussion will tackle, sooner than later, the positive effects that trade has brought to the U.S. And certainly, those arguments are solid for trade.

The growth of household disposable income in the U.S. is mostly due to foreign competition:

Politically speaking, though, arguments for foreign trade discourage critical endorsements. Those arguments go beyond the support of radical views on the economy as well as beyond labor unions interests. For instance, between 1901 and 2005 “the average US household’s income increased 67-fold, from $750 to $50,302” (Dolfman and Mc Sweeny, 2006). That story is well known by now, and it might sound unrelated to foreign trade. But, the fact of the matter is that such a success would have never been achieved without foreign trade. Indeed, the effect is noticeable in the household income-household expenditure ratio. Household expenditure has been roughly 90 percent of household income since the beginning of the 20th century. The growth of household disposable income in the U.S. is mostly due to foreign competition.

Please read carefully, because such gains have been realized by Households, and not by corporations, makes a big difference. A stakeholder analysis would yield conclusions on how households outperform firms in getting benefits from trade, therefore, Donald Trumps’ stance. If there is a factor in the economy that has nurtured households’ welfare in the United States is the fact that trade has made consumer goods cheaper all history long. Hence, consumer spending is perhaps the largest component of US Gross Domestic Product.

Journalists may continue to agitate on this issue for some time moving forward in the presidential election while political rhetoric lures votes from radicals. However, the consumer-voter will drive the opinion towards the effect of trade in their pockets finally. In other words, the anti-trade madness will last until the consumer-voter takes the stage.

 

Despite good U.S. economic conditions, analysts are alarmed. Why?

Economists are looking forward in time for signals that point out to the next US economic turn, either down or up. Most of the ongoing concerns about the US Economy respect to the perils of unexpected macroeconomic shocks. And, what complicates the outlook is the word “unexpected”. Analysts can either forecast a manufactured crisis or see healthy economic growth. The difficulties stem from the lack of instruments for forecasting macroeconomic shocks. In other words, the problem derives from our natural inability to look into the future. And, that is just okay. The great news is that old fashion methods of analysis outperform any sophisticated econometric technique, particularly in these instances. What is that old fashion method of analysis? Written English.

The current economic situation alarms most of the analysts since, aside oil-related industry, everything else seems to be working just as it should. As of March 2016, the pace of employment levels is satisfactory for many (4.9 percent), household debt looks healthy, inflation is “sort of” on target (1.4 percent), financial sector appears tranquil, and the US economy seems resilient to unexpected international upheavals.

Economic Sectors:

In fact, looking at the Beige Book Report (March 2, 2016) by sectors, it is possible to read that consumer spending increased in almost every district, with Kansas City being the exception. Also, the economic industry related to tourism reported good activity thanks to mild winter weather as well as to low gas prices. Auto sales continued to report elevated levels all over the country. The demand for staffing services increased as a signal of the strength of labor markets. Both residential and non-residential real estate sales and construction expanded at an acceptable pace. And, the finance sector reported slight increases in loans demand as well as stable credit environment.

Otherwise, agricultural sector reported flat to down conditions as an effect of mostly low commodity prices and weak global demand. Contacts that participate in the surveys suggested that low levels of exports are limiting gains, which is congruent with the appreciation of the U.S. Dollar. Also, as it has become usual, most districts noted that the energy sector is spilling over many industries.

Geographic Regions:

Regionally speaking, the tenth district –Kansas City- is the only one reporting some sort of economic contraction. Every other area from Richmond to Minneapolis are reporting flat to modest economic conditions. San Francisco, Cleveland, Atlanta, and Chicago altogether reported moderate growth. St. Louis, Dallas and New York reported flat circumstances.

The fear rises from the fact that thus far economists do not distinguish between a financial distress akin situation and a crisis triggered by uncertainty. Given that they both usually happen at the same time, the empirical evidence makes them difficult to disentangle. There is where the “word of mouth” comes in handy for economic analysis.

Understanding whatever the future holds for the economy requires defining measurably the very elusive concept of uncertainty. When main economic indicators perform acceptably, the focus of analysts turns onto uncertainty. Although everything looks good, analysts still have some reasons to worry, especially, about the fact that economic expansions last on average no more than 58.4 months. From there, uncertainty starts to drive the logic of positive analysis, especially when everyone believes that the current expansion will not outlast the longest one, ten years (1991-2001).

Now, reading lengthy reports about the economy or mapping economic news will also have limited insights. Nonetheless, those sources comprise most of the information analysts need to outlook the future. The problem is that the latest version of the Beige Book reveals mostly good things about the state of the economy, which leaves everybody at the beginning of this story.

 

Internal demand strengthens as external conditions weaken.

Main national economic indicators reveal a solidifying moment of the American economy. In spite of job losses in mining and oil-related sectors, total nonfarm payroll employment increased by 242,000 in February; and although the unemployment rate kept unmovingly, the economy shows signs of very good standing relative to past winter season data. The biggest risk, though, is probably to come from outside the United States. In that regard, the latest data on international trade in goods and services confirm that the economic momentum in being built on the internal demand for goods, whereas the international market weakens. In other words, foreign trade is not adding much to the current economic expansion given that both imports and exports decreased in January. With the dollar as it stands currently, what analysts expect to see is a big inflow of trade, which has not realized yet. That could be somewhat worrisome.

By Catherine De Las Salas

By Catherine De Las Salas

Countries have not found their way in:

Most of the accounts of trade balance declined in January. Countries have not found the way in for commodities even when the US Dollar remains high. In fact, the US Balance of Trade in 2015 exhibited a positive trend with a net gain of U$851 million of dollars (Graph 1 below). In January, imports of goods declined U$2.9 billion as a result of a noticeable drop in the value of Crude Oil imports, and a decrease in Capital Goods. On the other hand, exports of Goods fell U$4.0 billion mainly as a result of small international sales of Capital Goods and Industrial Supplies Materials. Nevertheless, those decreases, exports of services increased especially on Travel for all purposes and transportation.

No analyst expects to see US Exports to grow considerably currently. Otherwise, economists expect overseas countries to take advantage of the current dollar rate, which has not happened yet. US Exports deteriorate due to strong dollar abroad. The deficit in the Balance of Trade continues to grow negatively for the United States in spite of 2015 being a good year. The deficit increased by $U2.1 billion over the year, which correspond to 4.8 percent when compared January 2016 and January 2016. Exports decreased U$12.5 billion over the year as Imports did so too by U$10.5 billion. Over the month changes in the Balance of Trade registered only positive increases in exports of Services. All these data beg the question about international markets. Why countries overseas are not selling to the United States?

U.S. Balance of Trade

Internal demand is gaining momentum:

With almost every international trade indicator declining, what is feasible to infer about the economy is that the internal demand for good and manufacturing is gaining momentum. The evidence rests on employment data. Just in the past three months, payroll data has shown an average increase of 228,000 jobs created per month. Usually, employment creation in January and February are not that good because of the weather. February 2016 employment data exhibited gains in Healthcare (+38,000), Retail trade (+55,000), Food and Services (+40,000) and Construction (+19,000). Retail trade, Food and Services, and Construction usually are affected by weather conditions. This year seems to be different.

 

Low price of oil: some winners and losers.

Low prices of gas and oil are creating a reallocation of wealth not seen very often in capitalism. The recent version of the Beige Book from the Federal Reserve Bank gives some clues about the way this phenomenon is evolving. This short article looks at the winners and losers of the current oil story.

Labor layoffs:

The rearrangement of resources from current gas prices has consumers happy while big oil corporation worry. With record-low prices of oil, the petroleum industry began to cut back capital spending as well as to layoff employees. It is worth noting that although oil companies are certainly wealthy, analysts should look for risks in debt default as well as significant labor layoffs from those companies. As new projects halted within the industry, thousands of companies have started already to cut jobs (Mining Industry has shed 171,000 workers since September 2014).

By Catherine De Las Salas

By Catherine De Las Salas

It is too early to establish to what extent those layoffs could spill over the economy, as well as in what ways it could do so. The obvious externality is defaulting on mortgages, credit cards, and loans payments, among others. Otherwise, it could also be argued that the size of the sector is not even half of others such as the financial sector. However, what could stand out is the leverage ratio of those layoff employees as their salaries compare high against other similar professionals.

Prices are fostering an economic adjustment all over the economy:

In spite of the layoffs, low gas prices are fueling economic growth on other fronts. Tourism is one sector that has benefited from low gas prices since people travel longer and more often nowadays. Notably, Atlanta in this version of the Beige Book reported that due to gas prices tourism has increased considerably.

By Catherine De Las Salas

By Catherine De Las Salas

Along with increasing trends in tourism, there has been an interesting shift in sales of automobiles. Apparently, automobile customers are opting for bigger engines and trucks as gas prices allow them to fill up the gas tank quickly. Choosing light trucks seem fashionable nowadays even if they are driven for delivering pizzas. Clients of the automobile industry seem confident that gas prices will not increase shortly.

Low oil prices are fostering an economic adjustment all over the economy. The fact that gas station prices average U$ 1.74 (Regular) has meant cheaper connectivity for business. The latter is another good aspect of low gas prices from which many companies are benefiting from either lowering costs or increasing consumer spending. Production costs in transportation have meant increasing margins of profit for businesses that employ resources in mobilizing goods.

Conclusion:

Many economic transformations go unnoticed since they take place over the extended periods of time. Not very often can analysts see economic changes like the one that low prices in oil is shaping. Combined with government policies that put in place financial incentives for the development of clean energy, fossil-based combustion products have seen a rapid deceleration that has created winners and losers at the same time.

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.

Gross Domestic Product in 2015: same tempo as in 2014.

Real GDP in the United States increased by 2.4 percent in 2015 when compared to 2014. This growth rate represents the same tempo as in 2014. Growth was pulled up mainly by Personal Consumption Expenditures, Nonresidential fixed Investments, Private Inventory Investment, State and local Governments, and Exports. Although these figures of economic growth may look sluggish for many analysts, the truth is that they encompass good news for the American economy as far as investments concerns. The fact that economic growth was pushed up by both Personal Consumption Expenditures and nonresidential fixed investments means that both business people and consumers are confident about future economic outlook.

Consumption and  Nonresidential Fixed Investments:

On one hand personal consumption expenditure reveals that people are spending and not holding back on economic plans. Such a situation, combined with recent data on household debt, shows household are in good stand not only for economic growth but also for absorbing unexpected economic shocks. Same intuition applies to businesses insofar as Nonresidential Fixed Investments grew considerable. Those Nonresidential Fixed Investments are the set of spending dedicated to improving and expanding business facilities. State and local state spending continue to bolster economic growth nationally proving public expenditures work out well for the economy.

Price Indexes have been dragging much of the GDP figures for the last year or so. Indeed, the price index for gross domestic purchases barely increased 0.4 percent during 2015. It is worth noting that in spite of deflationary pressures derived from low oil prices, the price index for purchases rose 1.2 percent in 2014.

On the quarterly basis, second estimates data for the last quarter of 2015 showed that the economy expanded at 1.0 percent (when compared 4Q2014 and 4Q2015). Unlike the year-round picture, the last quarter increases resulted mainly from residential fixed investments and federal government spending, whereas nonresidential fixed investments declined along with a decrease in private inventory investments.

In 2015 Consumer Price Index was pulled up by cost of shelter and health care.

Consumer Price Index was pulled up by cost of shelter and health care this past January of 2016. Although low prices in energy offsetted most of the increases, inflation reached 1.4 percent over the last year. Core inflation, which is all items less food and energy, continues to be on expected levels of 2.2 annualized percent change. The Bureau of Labor Statistics informed that “this is the highest 12-months change since the period ending June 2012, and exceeds the 1.9 percent average annualized increase over the last ten years.

Energy prices continue to push down inflation:

Energy prices continue to push deflationary forces in the Consumer Price Index. In January 2016, Energy Index fell 2.8 percent when compared to December 2015. Gasoline itself declined 4.8 percent making the entire fuel oil index fell 6.5 percent for the first month of the year. Natural gas also decreased 0.6 percent as electricity did so by 0.7 percent. Not only oil prices are to blame for the decline in the energy index, but also the mild weather the nation has had this winter thus far.

Although the food index was unchanged for the month of January 2016, food items such as meat, poultry, fish, and eggs made the six major grocery stores index to decline 1.3 percent. These items’ declining trends were offset by 1.3 percent increase in fresh vegetables and fruits. Annualized measures indicated that meats, poultry, fish and eggs prices have fallen 3.5 percent while dairy products have done so also by 3.0 percent. And again, those price gains for the consumer’s wallet were mostly counterbalanced by fresh vegetables and fruits prices which have risen 2.7 percent during the last 12 months.

Shelter and Medical Care:

The index for shelter increased 3.2 as the index for medical care rose by 3.0 percent. Moving became ten percent more expensive than it was at the beginning of 2015. The rent of primary residence also increased above all items index level to 3.7 percent over the year.

Shelter Price Index - January 2016.

Shelter Price Index – January 2016.

Brief Explanation of the CPI. Taken from the US Bureau of Labor Statistics.
The Consumer Price Index (CPI) is a measure of the average change in prices over time of goods and services purchased by households. The Bureau of Labor Statistics publishes CPIs for two population groups: (1) the CPI for Urban Wage Earners and Clerical Workers (CPI-W), which covers households of wage earners and clerical workers that comprise approximately 28 percent of the total population and (2) the CPI for All Urban Consumers (CPI-U) and the Chained CPI for All Urban Consumers (C-CPI-U), which covers approximately 89 percent of the total population and includes, in addition to wage earners and clerical worker households, groups such as professional, managerial, and technical workers, the self-employed, short-term workers, the unemployed, and retirees and others not in the labor force.
The CPIs are based on prices of food, clothing, shelter, fuels, transportation fares, charges for doctors’ and dentists’ services, drugs, and other goods and services that people buy for day-to-day living. Prices are collected each month in 87 urban areas across the country from about 6,000 housing units and approximately 24,000 retail establishments-department stores, supermarkets, hospitals, filling stations, and other types of stores and service establishments. All taxes directly associated with the purchase and use of items are included in the index. Prices of fuels and a few other items are obtained every month in all 87 locations. Prices of most other commodities and services are collected every month in the three largest geographic areas and every other month in other areas. Prices of most goods and services are obtained by personal visits or telephone calls of the Bureau’s trained representatives.
In calculating the index, price changes for the various items in each location are averaged together with weights, which represent their importance in the spending of the appropriate population group. Local data are then combined to obtain a U.S. city average. For the CPI-U and CPI-W separate indexes are also published by size of city, by region of the country, for cross-classifications of regions and population-size classes, and for 27 local areas. Area indexes do not measure differences in the level of prices among cities; they only measure the average change in prices for each area since the base period. For the C-CPI-U data are issued only at the national level. It is important to note that the CPI-U and CPI-W are considered final when released, but the C-CPI-U is issued in preliminary form and subject to two annual revisions.
The index measures price change from a designed reference date. For the CPI-U and the CPI-W the reference base is 1982-84 equals 100. The reference base for the C-CPI-U is December 1999 equals 100. An increase of 16.5 percent from the reference base, for example, is shown as 116.500. This change can also be expressed in dollars as follows: the price of a base period market basket of goods and services in the CPI has risen from $10 in 1982-84 to $11.65.