Economic Indicators for Professionals

Putting the Statistics into Perspective

Charles Steindel

• Editore: Routledge
• Anno: 2018
• ISBN:
  9781351363891
• Edizione: 1
• Argomento: Business
• Categoria: Business General

We are bombarded with economic numbers: unemployment, retail sales, inflation, GDP—the list goes on and on. Some analyst or another is constantly telling us about an obscure statistic that is the key to our future, or is apparently the indicator that the "Fed" will be using to key off its decisions. With economic numbers playing such a central role in the national and world dialogue on policy and markets, and spilling over into the political arena, a broad review of what they are all about is timely. This book reviews the critical US economic data, and how one may put the numbers into an intellectual structure that will depict evolving economic reality. The work is aimed at those who want and need to get some understanding about how the data contributes to a big picture of the economy and guides policy.

The objective is for the reader to grasp the overall logic of the data—how each piece of the puzzle contributes to our understanding of the overall economy. This is the way the Fed looks at the numbers.

There are other books that go through the economic numbers, but they do so in a "bottom-up" fashion, describing a series in some detail and adding something about how financial markets may respond to it. This book naturally has considerable discussion of series, but views them as part of the overall mosaic, not items of fundamental interest in themselves.

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GDP

GDP—gross domestic product—is the central measure of economic activity for the United States. This chapter discusses why GDP is such a critical measure, the various ways it is measured, and the mechanics of the presentation and release and revision of the data. It will describe how GDP measurement has changed over time, and address some of the criticisms that have been made of the series (Coyle, 2015 is an engaging account of the development of the GDP concept and how it is used, along with some criticisms). Subsequent chapters will dig into the various component sectors of GDP and see where those numbers come from. A major point is that in any quarter GDP growth can be heavily affected by unusual moves in one or another sector. In turn, these sectoral moves may be influenced by idiosyncratic moves in the data used to estimate them. We then will examine the income side of the accounts, and the data on GDP by industry.

Why worry about GDP?

The US is far from the only country that estimates its GDP. Comparable measures are produced for essentially every other nation. The release of the US number is often a major news story, with politicians and pundits ready to expound on the implications of the headline figure on “real growth.” All the discussion suggests that GDP is a tangible item—a hard objective measure. This common use leads to misinterpretation and misunderstanding. GDP is merely a summary statistic designed to provide an index of aggregate economic activity. It is not something like, say, a temperature, which is an inherent feature of a physical system. Temperature readings of 32 degrees Fahrenheit or 0 degrees Celsius are in the deepest sense precisely the same, and can be associated with something genuine: the freezing point of fresh water at sea level. The July 28, 2017 announcement¹ that the annual rate of GDP in the second quarter of 2017 was 17,010.7 billion “chained (2009)” dollars has no 12such inherent meaning. The figure cannot be sensibly interpreted without adding considerable context.

For anyone wanting to be knowledgeable about the aggregate economy and policy it is vital to have some basic understanding of how GDP is defined and measured. The measurement of GDP is affected by decisions made about what its constituent parts should be; those decisions in turn are affected by many things, including mundane realities such as the availability of timely information. Additionally, combining the parts into the whole—deciding how the pieces of GDP should “add up” to the total—is somewhat tricky.

Before getting into the measurement technicalities, let’s ask a basic question. Why should anybody care about GDP? Temperatures give us some guidance as to how to dress outdoors. What does a GDP number tell us?

There are a number of possible answers to this question. A key one is that GDP is a valuable guide to things of fundamental interest. GDP is such a valuable guide that it is often talked of as if it is a goal. For example, real GDP growth (to be explained) since the financial crisis has averaged little more than 2 percent a year. Many believe that the nation would be better off if growth were 3 percent or higher. Why would that be so? To reiterate, we will see that GDP is essentially a guide to things of genuine concern, not obviously a goal in and of itself.

Most people with any interest in the numbers seem to know that aggregate GDP figures are reported in two ways: 1. “Real” (price adjusted), often expressed in chained dollars (it will be explained that using the word “real” for this measure is actually a bit odd); and 2. “Nominal,” expressed in ordinary, non-chained, dollars. Both of these measures can be useful, by themselves or in combination, to understanding critical developments that may be underway or loom for items of genuine importance.

Some things that are of fundamental interest to policymakers, and thus to financial markets, which are sensitive to what drives policy changes, are the number of jobs, the rate of aggregate price inflation, and tax revenues.² It certainly would be useful to have some guide to where each of these will be in the near future. It would be even better if the guide to each were comparable. In looking for the guides, we first have to decide what we mean by “the number of jobs,” “aggregate price inflation,” and “tax revenues.”

For now, let’s take it as reasonable that a data series called nonfarm payroll employment is an important measure of the number of jobs, and that the growth of the Consumer Price Index (CPI) is a sensible measure of aggregate price inflation. A reader may be familiar with these series, to be described in more detail in later chapters, and may have views as to their worth, but for the moment take it as given that particular values for the two are sensible proxies for major economic policy goals: maximum employment and low, stable, price inflation. The Federal Reserve Board and the Federal Open Market Committee (FOMC), the group that directs monetary policy, are legally mandated to seek maximum employment and price stability,³ so if one is interested in what the Fed may do, it’s certainly desirable to figure out where jobs and inflation may be heading. Both the payroll employment and CPI figures are announced every month and, like GDP, receive widespread 13media, policy, and market attention. While there’s not an obvious equivalent series for aggregate national tax revenues, there are estimates available (these are contained in BEA’s detailed GDP database).

Now let’s take a look at some relationships. Their tightness is here gauged by the simple measure of correlation, but merely glancing at them gives some idea of their strength. Figure 1.1 suggests that there is a clear—not perfect, but clear—link between the growth rate of the real GDP series and the growth rate of nonfarm payroll employment. The faster real GDP grows, the faster is the growth of jobs. Figure 1.2 shows a comparable, albeit looser, relationship between the growth of nominal GDP and the growth of aggregate tax revenues received by the federal and state and local governments. The faster nominal GDP grows, the faster tax revenues grow. Figure 1.3 shows us another looser, but not nonexistent, relation between the growth of nominal GDP and the growth of the CPI. An increase in the growth rate of nominal GDP appears to be followed, a year or two later, with increased growth in the CPI. For convenience, the figures show annual data but the relationships would look comparable if quarterly series were plotted. Also, there is no attempt made here to do a full-blown statistical analysis. The relationships between these series can be expressed in more complex and conceivably more accurate terms. The intent here has only been to suggest the existence of such relationships.

A note on the figures

Many of the figures contain shaded areas marking the periods the US economy has been deemed to have been in an economic downturn, or recession. The recession dates set by the Business Cycle Dating Committee of the National Bureau for Economic Research are universally accepted. Formally, recession dates are months. For example, the last recession is considered to have started in January 2008 and ended in June 2009. It’s fairly straightforward to assign quarters to recessions—the last one is seen as lasting from the first quarter of 2008 to the second quarter of 2009. Assigning years to recessions can sometimes be problematic. For the purposes of 14charting, I consider 1960, 1970, 1974, 1980–1982, 1990, 2001, and 2008–2009 to be recession years. In some instances, these assignments can be disputed; most notably, there was a fairly marked business cycle expansion from August 1980 to July 1981, interrupting two recessions.

FIGURE 1.1 Growth rates of real GDP and payroll employment

Source: BEA and BLS

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FIGURE 1.2 Growth rates of nominal GDP and current tax revenues

Source: BEA. Revenues are the sum of “current tax receipts” (line 2 of BEA’s National Income and Product Accounts Table 3.1) plus “contributions for government social insurance” (line 7 of Table 3.1).

FIGURE 1.3 Growth rates of nominal GDP and the Consumer Price Index

Source: BEA and BLS

None of these relationships is perfect. There have been periods in which faster real GDP growth seemed to have been more closely linked to smaller boosts to job growth than it has in others. In the 1990s, for instance, job growth was only modestly more rapid tha...