Federal Reserve Economic Data

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Posts tagged with: "GDP"

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The declining wage component in GDP

The graph above shows the share of GDP from the wages and salaries of employees, which has clearly been on a downward trend over several decades. This post isn’t about the reasons behind this decline, which would require analysis of (i) supplements to wages and salaries such as pensions and other benefits and (ii) proprietors’ income, which is earned by independent workers and business owners that compensates for labor and capital. What we are interested in is whether the decline has bottomed out.

Indeed, the share has been increasing for about two years now. Is this evidence enough to declare the trend has reversed? Well, that call is difficult. If you play with the graph by changing dates—for example, by ending the data in the year 2000 or 1987—you’d find a pretty similar situation in which the decline appears to have reversed. Yet, the share has continued to decline.

But is this time different? Visit this blog in a couple of years and we may have the answer.

How this graph was created: Search for “compensation of employees” and the series used in the graph should be among the first options. Note that a share of it in national income is also among the top options, but it has less current data. Once you have the graph for the series, add a series to the first line, not as a separate line. Then create a data transformation by applying the formula a/b.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: GDP, WASCUR

Have earnings kept up with growth?

Recent policy discussions have focused on wage growth and whether it’s been “too sluggish.” In this post we argue this is a feature of the trend and not the cycle.

Before looking at the actual statistics, it’s worth noting that it’s not totally obvious how we should define wages—because wage dynamics change so much over the distribution. Low, medium, and high wages have grown at different rates and at different times. From a macroeconomic perspective, however, it makes some sense to measure the average wage. The effect of so doing is that we put more weight on the higher earners than the average person, a result of a positively skewed wage distribution. (Recall the definition of skewness: Here, it means the top tail can pull up the mean past the average person’s wage, the median wage.) Still, with macroeconomic aggregates, using the average makes sense. We often look at GDP per capita, and average wages are equivalent to wages per capita. Now that we’ve decided what moment of the distribution to study, we have to choose what constitutes wages: total compensation including benefits or strictly labor income. Here, we focus on strictly wages and salaries rather than on other benefits: Labor income is more directly linked to economic motivations, whereas other side benefits are often the result of tax distortions. There are two good sources for these data: The BLS uses survey data to provide an estimate of wages and salaries, and the BEA creates a measure of wage and salary income while putting together the national income and product accounts.

In the top graph, we plot the BLS and BEA measures of labor income as the red and blue lines, respectively, and look at this relative to GDP (the green line). We normalize all of these series to be 100 in 1982, at the trough of the recession and when the BLS data become available. It’s immediately apparent that the GDP figure is now higher than wages, meaning that it has grown faster since the 1980s. This observation, which isn’t new, is related to a large literature about how the labor share of output has (or has not) diminished. We see the separation in 2015, but this is not a result of developments during the Great Recession. In the bottom graph, we plot these series in growth rates: year-over-year percentages. Notice that through the first decade of the 2000s, GDP growth was almost always faster than wage income growth. Both plummeted in the Great Recession, but since then have been growing at about the same pace. The decline in wages as a fraction of GDP is not a result of a sluggish recovery from the Great Recession, but rather from effects predating it.

How these graphs were created: For the top graph, search for “compensation of employees: wage and salary,” “total wages and salaries,” and GDP. Add each series as a separate line. Then choose the units to be “Index (scale value to 100 for chosen period)” and choose the observation date of 1982-11-01 (the 1982 recession trough when all three series are available). For the bottom graph, follow the same process to show all three series but, instead of choosing an index scale, make the units “Percent Change from Year Ago.”

Suggested by David Wiczer.

View on FRED, series used in this post: A576RC1, BA06RC1A027NBEA, GDP

The GDP residual

GDP is intended to serve as a measure of all economic activity in an economy. But not every transaction is tracked, so one has to rely on estimates and models for parts of GDP. There are three ways one can measure GDP: adding up all expenditures, adding up all incomes, and adding up all value-added in the economy. All three should give you the same number. In practice, they don’t quite match up because of measurement issues. The difference between the expenditure and income approaches is called the statistical discrepancy. It is graphed above as a share of GDP, shown in red.

Another issue occurs when converting nominal values to real values: This is accomplished by applying a calculation to a basket of GDP components for a particular base year and keeping those prices constant for the other years. (The actual calculation is a bit more complex than this.) Adding up these GDP components does not exactly achieve GDP, and this difference is called the GDP residual. It is graphed above as a share of GDP, shown in blue. It is very small around the base year, but it deviates more substantially in earlier years, up to 6%.

How this graph was created: Search for GDP residual and add “Real Gross Domestic Product: Residual” (billions of chained 2009 dollars, quarterly, seasonally adjusted) to the graph. Then add real GDP (billions of chained 2009 dollars, quarterly, seasonally adjusted) to series 1 and apply transformation a/b. For the second line, search for GDP discrepancy and add “Gross Domestic Product (GDP); statistical discrepancy…” (quarterly, millions of dollars, seasonally adjusted) to the graph. Then add nominal GDP (quarterly, billions of dollars, not seasonally adjusted; not real GDP, as the discrepancy is nominal) to series 2 and apply transformation a/b/1000 . The division by 1000 here is because one series is in billions of dollars and the other is in millions of dollars.

Suggested by Christian Zimmermann

View on FRED, series used in this post: A959RX1Q020SBEA, GDP, GDPC1, GDPSDCQ027S


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