Federal Reserve Economic Data

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Country classifications by income level

A guest post with perspectives from the World Bank

The FRED Blog has used World Bank data to discuss infant mortality and life expectancy and refugee populations across groups of countries, economies, or territories classified by their level of income. Today, we discuss how these income categories are assigned.

The FRED graph above shows annual population growth data between 1961 and 2023 for the four income categories defined by the World Bank: high (blue line), upper middle (red line), lower middle (green line), and low (purple line).

Each country’s income is measured through its gross national income (GNI), the economic value added by all national producers (plus and minus some adjustments). The income figure is converted from various local currencies to US dollars, then divided by the number of persons in the total population and compared against a series of numerical thresholds for each group. For example, at the time of this writing, a low-income economy is defined as one with a GNI per person of $1,145 or less.

The threshold values separating each income category are updated every year to account for price inflation. Also, because income levels do change over time, some countries move into different categories. For example, all South Asian countries were classified as low-income countries in 1987, whereas in 2023 only one in eight were in that category.

This FRED Blog post is adapted from the World Bank’s Data Blog post “World Bank country classifications by income level for 2024-2025.”

How this graph was created: Search FRED for and select “Population Growth for High Income Countries.” Next, click the “Edit Graph” button and then the “Add Line” tab to search for and add “Population Growth for Upper Middle Income Countries.” Repeat that last step two more times to add the “Population Growth for Lower Middle Income Countries” and the “Population Growth for Low Income Countries” to the graph.

Suggested by Diego Mendez-Carbajo.

Customs duties: What do they amount to?

The history and math behind import tariffs

Before the Civil War, the principal way the US federal government raised income was through customs duties, a.k.a. import tariffs. These duties were easy to implement, by simply imposing them on all incoming ships at US ports.

During the Civil War, sales and excise taxes were introduced to help defray wartime costs.

During World War I, income taxes were introduced to help defray those wartime costs. By then, technology had made it possible to raise taxes in a much more decentralized way.

After the Great Depression, the US government put considerable effort into reducing customs duties through bilateral or regional agreements and the United Nations–sponsored General Agreement on Tariffs and Trade (GATT), to avoid hurting economies with tariff rates that might be set too high (or set at all). Thus, the importance of customs duties as a source of revenue has decreased.

The FRED graph above shows quarterly federal government income from customs duties since 1959. Contrary to the comments above, the data seem to show strongly increasing revenue from customs duties. Why?

The graph above ignores two important pitfalls that can cause a long series of macroeconomic data to appear misleading: The US economy grew considerably over this time period, as did the general level of prices. To correct for both these factors, we can divide this series by another series that also increased with the economy and inflation. Total tax receipts of the federal government is a good choice, as it allows us to see the share of customs duties in those receipts.

The FRED graph below shows that, while customs duties are on the high side nowadays, they have never exceeded 4% of total tax revenue for the 1959-2024 period and typically make up only about 2%. Quite far from the nearly 100% share two centuries ago.

How these graphs were created: Search FRED for “customs duties” and you have the first graph. For the second, take the first, click on “Edit Graph,” search for “Federal government tax receipts,” and apply the formula a/b*100.

Suggested by Christian Zimmermann.

The inflation rate of lemonade

Data on lemon and sugar prices

The lemonade stand. A longstanding symbol of childhood summer entrepreneurship.

In this FRED Blog post, we answer the cornerstone question of every sound lemonade stand business plan: “How much does it cost to produce a glass of lemonade?”

The FRED graph above uses data from the U.S. Bureau of Labor Statistics to calculate the cost of a single glass of lemonade. The white bars represent the cost of the sugar and the yellow bars represent the cost of the lemon juice. The white and yellow bars are stacked to more easily show the total cost. We use tap water, which is so cheap in this recipe that we can ignore it without substantially impacting our calculation.*

The latest data available at the time of this writing showed that the cost of a glass of lemonade was $0.36 in 2023. Since 1980, when the data were first available, that cost has increased almost threefold. However, the year-over-year inflation rate of this sweet and tart drink has been volatile, given that food item prices themselves are volatile.

Clearly, the cost of the ingredients is only one of the factors that drives how much money one spends at a summer lemonade stand. The cost of labor, overhead, marketing ingenuity, and overall entrepreneurial tactics of the stand owners will account for the rest.

*The recipe is from MyPlate.gov, the website of the U.S. Department of Agriculture that provides recipes based on the Dietary Guidelines for Americans, 2020-2025.

How the graph was created: Search FRED for and select “Average Price: Lemons (Cost per Pound/453.6 Grams) in U.S. City Average.” Next, use the “Edit Line” tab to customize the data by typing the formula (a/15)*2. Next, use the “Add Line” tab to search for and add “Average Price: Sugar, White, All Sizes (Cost per Pound/453.6 Grams) in U.S. City Average.” Use the “Edit Line” tab to customize the data by typing the formula (a/12.5). Next, use the “Format” tab to change the graph type to “Bar” and the stacking to “Normal.” Last, customize the plot area and bar colors to your liking, stir well, and serve cold.

Suggested by Elizabeth Marmer and Diego Mendez-Carbajo.



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