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Living in an uncertain world

More uncertainty data in FRED

We’ve recently looked at different ways to measure uncertainty in the U.S. economy. Today, we look at international data on economic and policy uncertainty. While U.S.-level data were measured by looking at what newspapers report, the international data are based on quarterly reports from the Economist Intelligence Unit in each country. Having a single source for each country means one must be careful in interpreting the data: It contains quite a bit of noise, and there may be some idiosyncrasies for each country that make cross-country comparisons difficult: A report may focus on one particular aspect of a country, or it may be related to uncertainties in other locations that may affect that country.

The GeoFRED map above covers the third quarter of 2019. The countries with the highest uncertainty are Ireland and the United Kingdom, no doubt due to the Brexit situation. But Switzerland also has a very high score, while Iraq and Pakistan enjoy perfect scores. And observations from other quarters change dramatically for some countries. This is where it’s important not to focus on a single data point, but to have a more holistic approach to the data.

The graph below shows a few things: The economic and policy uncertainty in the United Kingdom was not a fluke for that one quarter. The situation in Iraq is clearly not always that rosy, but it still regularly scores low, maybe because no change is expected. In Switzerland, where certainty is the norm, uncertainties are worth playing up.

As we implied earlier, there are some lessons to draw from the data taken as a whole. The authors of the data highlight in their report that this uncertainty index does usually foreshadow growth troubles. Democracies, likely due to their political process, have more uncertainties than authoritarian regimes. (Again: See Switzerland versus Iraq.) More developed economies also have fewer uncertainties to worry about.

How the map and the graph were created: The graph: From the World Uncertainty Index release table, click on the link to the individual country indices, select the relevant country, and click “Add to Graph.” The map: Look below the graph at the related content, click on the GeoFRED link, and zoom out to the desired focus.

Suggested by Christian Zimmermann.

View on FRED, series used in this post: WUICHE, WUIGBR, WUIIRQ

The national growth quilt

GDP growth for each U.S. county

What’s new from the Bureau of Economic Analysis? Real GDP data at the county level, which is now part of FRED’s ever-growing database. The data shown here are for 2015 and are still considered “beta”; but visit FRED in December and the data will be even more definitive.

The map above shows GDP growth for each county across the U.S. It looks like a patchwork quilt. Clearly, high and low and middle-ground growth rates are sprinkled across the nation, with very little uniformity within each state. (Though you could make a case that Nevada and Illinois have much more homogeneous growth across the state.)

The data are even more detailed than just overall county growth: They’re also divided into the government, services, and goods sectors. The second map shows growth of real GDP in the government sector. Here, state borders do seem to matter in many cases, as county- and local-level government finances are in many cases governed at least partially by state-level funding decisions.

The last two maps show the services and goods sectors. The former reveals another patchwork across all states, with more uniformly strong growth in services in the West and Florida. The latter reveals weakness in goods production in the Midwest and the Mississippi Valley and stronger growth in the northern Mountain states, the West, and Florida.

How these maps were created: Search FRED for GDP for your favorite county and select the growth rate. Below the graph, there is a section for related content, including a GeoFRED map. Click on that and expand it to show the entire country. You can adjust the sizing to show Hawaii and Alaska. Once you have the first map, you can find the others by clicking on the cogwheel and selecting another series.

Suggested by Christian Zimmermann.

The economics behind the motivation to migrate

Income gaps and inequality in the U.S. and Central America's Northern Triangle

In the past two years, the surge in undocumented immigrants from Central America’s Northern Triangle has been covered extensively by most news outlets. The stories of these migrants from El Salvador, Guatemala, and Honduras involve compelling and often perilous human experiences and intense reactions to the issues involved.

Apart from the political and social views about immigration, there are fundamental questions to ask that may have some economic answers: What is the main motivation for these migrations? And why are people willing to put themselves and their families at great risk to migrate to the U.S.?*

The graph above shows the ratio of per capita income in the U.S., the intended destination for many of these migrants, to per capita income in the three Northern Triangle countries: El Salvador (in blue), Guatemala (in red), and Honduras (in green). The gaps are huge, as expected, but also quite varied, with clear movements over time.

Some history: For El Salvador, during most of the 1970s, the ratio was below 10. But, as a result of the civil war (1979-92), the gap surged to 17.5 by the late 1990s, which coincides with the migration of many Salvadorans to the U.S. (especially to L.A., D.C., N.Y., and Houston.) Ever since, the ratio has remained at the high end of its trajectory, around 15. Guatemala has a similar pattern: The ratio steadily rose from around 11 in 1980 to more than 18 in 2005, and it also has remained at a higher level. In Honduras, the poorest country in Central America, we see even more dramatic disparity: The ratio for Honduras has never been lower than 17.5. It reaches its peak of 28.4 in 1999, and as of today it’s at 25.

The income gaps between the U.S. and the three source countries reveal the magnitude of the potential earnings migrants could gain and the potential improvements migrants could experience in their living conditions. That is, the data suggest that increased migration is motivated by economic considerations. Obviously, these migrants wouldn’t expect, if they managed to enter and remain in the U.S., that they’d attain the average income of U.S. residents. Undocumented workers with much lower labor market qualifications would receive much less than the average. So the ratios in the above graph seem to greatly overestimate income gains. But consider that the countries in the Northern Triangle have traditionally had enormous internal economic disparity, and many immigrants are from the poorer segments of the population. So the ratios could greatly underestimate the earnings gains.

The second graph conveys income disparity by showing the Gini coefficients for El Salvador, Guatemala, and Honduras, as well as for the U.S. (The previous FRED Blog post also used Gini coefficients, a very common indicator of inequality: The higher the Gini, the more concentrated the income distribution: A value of 100% indicates perfect inequality, in the sense that all income would be concentrated with one person [or the tiniest fraction of the population]. A value of 0% indicates perfect equality, a state in which everyone has the same income.)

For most of these years, the Gini coefficients for these countries are very high. Guatemala and Honduras maintain similar levels over time, above 50%, with very slow improvement. In the early 1980s, El Salvador was on par with them; but since the end of the war, its inequality seems to have trended down dramatically. In fact, by the end of the sample, El Salvador exhibits less inequality than the U.S. But it should not be surprising that very poor countries have many desperate people and generate the economic motivation to migrate.

* “I cannot help feeling self-conscious as I try to answer these questions from the comforts of my office. But my aim in this FRED Blog post, as in every other FRED Blog post, is to show how using data from FRED can provide some objective, big-picture perspectives, even on this highly charged issue.” —Alexander Monge-Naranjo

How these graphs were created: For the first graph, search for and select “GDP per capita for the United States in constant dollars” (series ID NYGDPPCAPKDUSA). From the “Edit Graph” panel’s “Edit Lines” tab, use the “Customize data” tool to search for and add “GDP per capita for El Salvador in constant dollars” (series ID NYGDPPCAPKDSLV). Then add the formula a/b. Repeat these steps for Guatemala and Honduras. For the second graph, search for and select “Gini index for El Salvador” (series ID SIPOVGINISLV), and do the same for Honduras, Guatemala, and the U.S. From the “Edit Graph” panel’s “Format” tab, choose “Mark type” square with a width of 5 and a “Line style” width of 1 for all.

Suggested by Alexander Monge-Naranjo.


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