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More on boundary changes

In our previous post, we mused about changes in country boundaries that can affect the time series of that region, taking up the example of German reunification. Such changes aren’t limited to international borders; they can also occur within a country. FRED has data for subnational units only in the United States. And although state boundaries haven’t changed much, some county boundaries have. The most dramatic changes happen when metropolitan statistical areas (MSAs) are redrawn. In 2010, the U.S. Census Bureau removed Scott County from the Louisville MSA, dropping about 24,000 residents, which can be seen clearly in the graph above.

The Census Bureau doesn’t recalculate population time series, but the Bureau of Labor Statistics adopts new definitions (after a delay) and recalculates its statistics. This is visible in the graph below, which shows the same series, total non-farm employees, with two data vintages. The ALFRED site allows us to see how data are revised over time and in this case shows the data as of March 17, 2014, and March 25, 2016. The difference is the deletion of Scott County.

How these graphs were created: For the first graph, simply search for “Louisville population.” For the second, Go to ALFRED, search for “Louisville employment,” change the graph type to “line,” expand the sample period to 10 years, and change the earlier vintage to 2014-03-17.

Suggested by Christian Zimmermann

View on FRED, series used in this post: LOINA, LOIPOP

When country boundaries change

The graph above shows a startling feature about German data: a huge increase in GDP and employment in 1991. Anybody with a little background in history will immediately recognize that this is not due to an error in the data or a sudden increase in German productivity: It all has to do with the reunification of East and West Germany after the fall of the Berlin Wall. While not as spectacular as this one, other changes in the definition of data, including the redrawing of geographic boundaries, do happen and one needs to be careful interpreting the data in those cases. Sometimes, statistical offices go back to the underlying data and recompute the times series using the new definition. For the German case, we have a new graph below that uses data from the OECD that is recalculated all the way back to 1970. Now we do not see the huge jump; rather, we notice how the large increase in unemployment in the eastern parts of Germany after reunification had a significant downward impact on employment. Quite a different story from the first graph.

How these graphs were created: For the first graph, search for “Germany” and click on the “discontinued” tag in the side bar (you may have to expand the list). Check the two series you want and choose “Add to Graph.” Expand the menu for the second series, set the y-axis to the right side. Finally, add the vertical line by adding another series, selecting “Trend line” from the dropdown menu, setting both dates to 1991-01-01 and putting start and end values that make the line appropriately long. For the second graph, search for “Germany GDP,” select the annual index series for consistency with the previous graph. Then add the second series by searching for “German civilian employment.” Set the y-axis to the right and add the trend line again.

Suggested by Christian Zimmermann

View on FRED, series used in this post: DEUEMPT, DEUEMPTOTQISMEI, DEURGDPR, NAEXKP01DEA661S

China’s trade surplus since 2000

The trade balance of a country is defined as the difference between its exports and its imports. When exports are greater than imports, for example, a country runs a trade surplus, which has been the case for China at least for the past 16 years. Thanks to FRED, we can analyze each of the components separately: exports, imports, and the overall trade balance (all as a percentage of GDP).

The graph shows four interesting episodes, marked by the vertical lines.

  1. In 2001, China became a member of the World Trade Organization (WTO) and saw a big increase in both its exports and its imports. Trade remained roughly balanced, however, since the increases of both components were similar.
  2. From 2004 to 2007, China started to build large trade surpluses (from 1.7 percent of GDP in 2004 to 7.5 percent in 2007) mainly driven by an increase in its exports that wasn’t matched by an increase in imports. There are two reasons: Exports in manufacturing increased rapidly, especially machinery, electronic appliances, and transportation equipment. Imports of intermediate goods slowed down, since China began to produce them domestically. (More about this.).
  3. In 2007, the trade surplus started to decline when China’s exports decreased more than its imports. Global demand also went down as a result of the financial crisis that started in the United States and spilled over to the developed countries.
  4. Since 2011, China’s trade surplus has increased. Even though exports are still falling (due to weak global demand), for the past few years imports have decreased, mainly due to lower domestic demand and commodity prices (More about this.)

How this graph was created: Select the first two series listed here for Chinese exports and imports and select “Add to Graph.” For each of the series, choose “Modify Existing Series” to add the third series (GDP) and insert in the formula tab “a/b*100” to calculate exports and imports as a percentage of GDP. For the trade balance (exports minus imports), add the three series listed (exports, imports, and GDP) and modify by using the formula “(a-b)/c*100” to calculate the trade balance as a percent of GDP. To add the vertical lines to denote time periods, click “Add data series” and then “Add trend line.” Set the start date and end date to be the same, set the start value as 0 and the end value as where you want the line to end. For the y-axis positions, choose the right side for exports and imports and the left side for the trade balance.

Suggested by Ana Maria Santacreu and Usa Kerdnunvong.

View on FRED, series used in this post: MKTGDPCNA646NWDB, XTEXVA01CNA667N, XTEXVA01CNA667S, XTIMVA01CNA667N


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