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What’s real about wages? A look at the increases and decreases in wages

People have been talking about the evolution of wages. Some say they’re increasing, others say they’re decreasing. Who’s right? As is so often the case in economics, it depends. First let’s look at the graph above, which has four different indicators for wages. Three of them show a clear and steady upward trend. But one of them—the green line, which shows median weekly earnings—is starkly different. It could be because the median is different from the mean if the distribution of wages skews strongly at the top. Or it could be that people work less per week. Or it could be that it’s a real measure, whereas the others are nominal.

The second graph corrects for this bias. The three nominal series are now real, after being divided by the consumer price index so that general price increases aren’t reflected in the wage. Now all four series evolve along basically the same path. It’s clear that decreases can be frequent and sometimes long lasting. It’s also clear there’s a lot of variability, which means one should really wait for a good amount of data before reaching for any conclusions.

How these graphs were created: For the first graph, search FRED for “wage” and pick the four series. Limit the time period to the past 10 years. From the “Edit Graph” section, choose “Index” for the units with the default of 100 at the end of the last recession. Then click on “Apply to all.” For the second graph, add the CPI to each of the three nominal series, apply formula a/b, and again choose “Index” for the units.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: CES0500000003, CPIAUCSL, ECIWAG, LES1252881600Q, USAHOUREAQISMEI

Cyclical asymmetry in the labor market Slow but steady improvements versus sharp declines

Have you ever spent hours on the beach meticulously building the perfect sandcastle, only for a bully to waltz by and kick it down in an instant? A similar phenomenon occurs in the labor market, as even the shortest recessions can undo years of progress made during an expansion. The unemployment rate tends to fall only gradually during economic expansions but rise sharply during recessions. This mismatch of slow declines versus sharp spikes is known as “cyclical asymmetry.”

This cyclical asymmetry of the unemployment rate derives in part from the fact that it takes much longer to create jobs than to destroy them. We can think of the labor market as a market for productive relationships between employers and employees. These relationships are a durable form of capital: They provide long-term economic benefits over time for firms and workers alike. It can take a while to build these relationships but only a short time to terminate them.

Consider the drawn-out process it takes to match employers and employees. First of all, both parties much search for and identify potential matches. Then, employers usually put candidates through a lengthy hiring process before making any offers. And even once the employee is hired, it takes time to establish a working relationship. Hence, job creation tends to be slow, even when the economy is performing well. The unemployment rate does not decline sharply when the economy is hit by a positive disturbance because relationships take time to develop.

Conversely, consider what happens when a recession hits. Letting workers go takes only an instant. In a flexible labor market, firms are often quick to lay off workers to save costs, often letting go workers who have been with the company for years. So, when the economy is hit by a negative disturbance, the unemployment rate tends to spike as firms lay off large numbers of workers.

Cyclical asymmetry also occurs in population dynamics in the form of the “heat wave effect.” Often, mortality rates rise and the population suddenly declines when bad weather hits. Yet, when a streak of good weather hits, we don’t see a corresponding boost in the population, as it takes time to repopulate after a tragedy strikes. In the context of the labor market, a recession is a “heat wave” that leads to sudden job losses and an expansion is a spell of good weather that, over time, creates jobs more steadily.

How this graph was created: Search for “civilian unemployment rate” and pick the seasonally adjusted series from the first result.

Suggested by David Andolfatto and Andrew Spewak.

View on FRED, series used in this post: UNRATE

How’s manufacturing? Depends on the sector

The industrial production (IP) index is a popular metric of economic activity because it’s available relatively quickly. This monthly data series covers only a part of economic activity, however. In particular, it misses the service sector and the government sector. The graph above shows its evolution since 1972 along with a subcomponent that covers only manufacturing. Note that the index is set at 100 in 2012, meaning that all the indexes will always cross in 2012. A particularly healthy sector will start lower before 2012 and then rise higher after 2012. The graph shows that manufacturing has done well compared with overall industrial production before 2012 and a little less well afterward. This hides considerable sectoral differences, though.

In this second graph, we highlight some sectors within manufacturing. The sector that appears to have suffered massive losses is apparel and leather goods. Indeed, clothes manufacturing largely migrated abroad during this time span, with a decrease in production of about 80% since the mid 1990s. On the other extreme is computer manufacturing; It was insignificant in the first years but has increased by 1200% since the mid 1990s. All other sectors lie somewhere in between, and they average out to the manufacturing index shown in the first graph, which does not look as dramatic as the second graph. Some other interesting observations in this second graph: The primary metal industry has remained essentially unchanged over the past 45 years, with its index hovering around 100 throughout the sample period. The furniture industry incurred great losses from the Great Recession that it has not yet recovered from. And the car industry is doing pretty well.

How these graphs were created: For the first, search for industrial production, select the two series (likely the top choices), click on “Add to Graph,” and adjust the time period to start on 1972-01-01. For the second, go to the industrial production release, and select the monthly and seasonally adjusted tags. In the list, choose the series according to industry, and click on “Add to Graph.”

Suggested by Christian Zimmermann.

View on FRED, series used in this post: INDPRO, IPG315A6S, IPG331S, IPG334S, IPG3361T3S, IPG337S, IPMAN

Measuring financial access What can we learn from cross-country variations in households' bank deposits?

The International Monetary Fund compiles a financial access survey for most countries in the world. This survey allows us to compare metrics on how households and businesses in different countries participate in financial markets—as either borrowers or lenders. For this map, we chose one particular measure that determines how much households deposit in banks, with the latest numbers from 2015 as a share of that country’s GDP. The map highlights stark differences: The general tendency is that poorer countries have smaller shares. The lowest are Malta (0.2%), Chad (8%), and Sudan (11%). But Argentina has only 16% (in 2014, since 2015 data aren’t available) and Germany has only 28%. If you’re looking for the highest, you’ll need to zoom in a lot: The tiny republic of San Marino has 280%, followed by Lebanon (254%) and Venezuela (178%).

How can we make sense of this? In countries with sophisticated financial markets, households have more options and thus may choose to put their savings in other assets. For example, it’s very popular in Germany to save by investing in building societies, which promote homeownership. Conversely, the lack of options beyond savings and deposit accounts may induce households to concentrate all their wealth there or to look for assets outside the financial sector. In other words, every country should probably have some sort of footnote attached to its numbers to explain its unique context.

How this map was created: From GeoFRED, click on “Build map”; open the cogwheel in the upper left corner, open the “Choose data” panel, select “Nation” as region type, and find the series you want.

Suggested by Christian Zimmermann.

New tax code = new price index = new tax bracket adjustments How we measure price changes can affect our income tax

The new Tax Cuts and Jobs Act changed both personal and corporate income taxes. Much of the discussion has focused on the changes in the tax rates, but there’s another change in this law that has an effect on personal income tax. A household’s tax obligation depends on the income bracket for their earned income. For example, a married couple earning $77,000 faces a tax rate of 12% on all income over $19,050. The next bracket starts at $77,400. While the new tax code decreased the tax rate, another change that has received less attention deals with how the tax brackets are adjusted over time. Economists argue that the income brackets should be adjusted when prices change. If the household in this example received a 3 percent cost-of-living increase, their new income level would be $79,310. Despite their inflation-adjusted income level not changing, this household would now fall in the next income bracket and face a 22% tax rate on income over $77,400. If the tax brackets were adjusted for the increased inflation, the marginal rate would not change. This is referred to as indexing the tax brackets.

The Act continues to adjust the tax brackets for price changes, as did the prior personal income tax legislation. The difference is the price index used to adjust those tax brackets. The previous tax code used the CPI-U measure to adjust the brackets, while the new code uses the C-CPI-U measure. The first measure, the consumer price index for urban consumers, assumes you purchase the same quantity of each good in the CPI basket over time. In contrast, the C-CPI-U, or chained consumer price index for urban consumers, recognizes that individuals can shift their expenditure patterns toward cheaper goods in the same expenditure category. For example, suppose chicken prices increase while turkey prices remain unchanged. The CPI-U implicitly assumes individuals continue to buy the same quantity of chicken. In contrast, the C-CPI-U recognizes that consumers will see the price change and substitute turkey for chicken. As a result of this expenditure shift, the price change is less. In the graph, these two price indexes are shown for the period 2000 to 2017 and indexed so that both equal 100 in 2000. The prices in the C-CPI-U index increase at a slower rate because individuals can shift their purchases toward cheaper substitute goods.

The choice of CPI measure has consequences. As shown in the graph, C-CPI-U implies smaller price increases than the CPI-U. This means the tax brackets will have smaller adjustments and more individuals will fall into the higher tax brackets and pay more taxes. If social security payments are linked to the C-CPI-U, then social security payments will increase at a slower rate. In the popular press, some have pointed out that this change in the price index has resulted in an implicit increase in tax revenue, which is true. But from an economic perspective, the primary concern is which index more accurately measures the change in the prices of goods that individuals purchase.

How this graph was created: Search for “cpi” and select “Consumer Price Index for All Urban Consumers: All Items.” From the “Edit Graph” menu, click “Add Line” and enter “chained cpi” in the search box. Select “Chained Consumer Price Index for all Urban Consumers: All items” and click “Add Data Series.” Change the units to “Index (Scale value to 100 for chosen date),” change the date to 2000-01-01, and click “Copy to all.” Finally, adjust the date range to start at 2000-01-01.

Suggested by Daniel Eubanks and Don Schlagenhauf.

View on FRED, series used in this post: CPIAUCSL, SUUR0000SA0

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