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Posts tagged with: "NYGDPPCAPKDUSA"

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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.

View on FRED, series used in this post: NYGDPPCAPKDGTM, NYGDPPCAPKDHND, NYGDPPCAPKDSLV, NYGDPPCAPKDUSA, SIPOVGINIGTM, SIPOVGINIHND, SIPOVGINISLV, SIPOVGINIUSA

Live by the barrel, die by the barrel

Connections between oil production, oil dependency, and economic growth

In every introductory macroeconomics course, oil is used as the classic example of a negative price shock. Professors tend to discuss the 1973 oil price shock triggered by the Arab-Israeli conflict and the 1979 oil price shock caused by the Iranian Revolution as reasons for rising inflation and falling global output—connecting these shocks to models about investment and aggregate supply and demand. More recent literature, including this presentation by St. Louis Fed President James Bullard, indicates that oil prices can sometimes be interpreted as a proxy for demand. But what’s the impact of oil supply for the consumers in oil-producing countries? We can use FRED to plot crude oil production versus GDP growth in oil-producing countries to get at least a first idea of just how oil-dependent a country might be.

For the United States, the relative importance of oil to industrial production (which is now less than 20% of the economy) is typically between 7% and 15%. Thus, in the graph above, the correlation between oil production and GDP growth per capita is practically negligible. In fact, the correlation is slightly negative. It’s unlikely that changes in oil production have much of an effect on aggregate economic activity.

But the relationship between oil production and GDP growth per capita is much stronger for countries that have more oil-dependent economies. For example, the correlation coefficient for this measure is 0.51 for the United Arab Emirates, 0.76 for Iran, and 0.93 for Iraq. (The closer this coefficient is to 1.0, the stronger the positive correlation.) The scatter plot below indicates the strength of this positive relationship. For these countries, aggregate well-being could be largely influenced by how much oil the country produces—which is why economic diversification is key to building a national economy less susceptible to oil or other shocks.

How these graphs were created: For the first graph, search for and select “constant GDP per capital United States” and click “Add to Graph.” From the “Edit Graph” panel, use the “Add a Line” feature to search for and select “industrial production crude oil”; change the units to “percent change from year ago” in the “Units” dropdown menu and click “Copy to All.” In the “Format” tab, change the line type to “Scatter Plot.” For the second graph, search for and select “constant GDP per capita United Arab Emirates” and click “Add to Graph.” From the “Edit Graph” panel, use the “Add a Line” feature to search for and select “crude oil production United Arab Emirates.” Repeat this process for each individual country. Change the units to “percent change from year ago” in the “Units” dropdown menu and click “Copy to All.” Change the line graph to a scatter plot by using the “Format” tab and changing “Graph type” entry to “Scatter” and pick different colors as needed.

Suggested by Darren Chang and Christian Zimmermann.

View on FRED, series used in this post: IPG211111CN, NYGDPPCAPKDUSA

The cost of servicing public debt: An international comparison

In a previous blog post, we looked at the cost of servicing U.S. debt. The metric we used is the gap between the real interest rate on debt and the growth rate of real GDP. We perform a similar exercise here, but we add a selected sample of OECD countries: Germany, Italy, Japan, and the U.K. This mix is interesting because Italy and Japan have high ratios of government debt to GDP, while Germany and the U.K. have more moderate ratios, which are all shown in the graph above.

The dotted line represents the 100 percent mark. As of 2017, three countries in our sample have debt-to-GDP ratios greater than 100 percent of GDP:  Italy, Japan, and the U.S. The U.S. debt-to-GDP ratio started to rise with the onset of the Great Recession in 2007, while the ratios for Japan and Italy started to rise in the 1990s.

As noted above, we calculate the cost of servicing debt for these countries as the difference between the real interest rate (measured as the difference between the interest rate on 10-year government bonds and the CPI inflation rate) and the growth rate of real GDP (measured as the sum of real GDP per capita growth and population growth). The second graph shows that, in 2017, Italy had the highest cost of servicing its debt, followed by Japan and the U.S. However, all of these countries have a negative cost of servicing their debt, which implies that they have a low burden of debt, since the growth rate of the economy is greater than the real interest rate for each of these countries.

 

It’s also worth noting that in the recovery period after the Great Recession, only Italy and Japan had positive costs of servicing their debt. Population growth in these countries is very low or even negative, which increases the cost of servicing the debt according to this measure.

How these graphs were created: For the first graph, search for and select the non-seasonally adjusted series “General Government Debt for Italy.” From the “Edit Graph” panel, select the “Add Line” option and repeat the above step for Japan, Germany, the U.K., and the U.S.

For the second graph, search for and select the series “Long-Term Government Bond Yields: 10-year: Main (Including Benchmark) for the United States” and set the units to be “Percent” and frequency to be “Annual” (average). Then add three more series to this line: “Consumer Price Index: All Items for the United States” (with units set to “Percent Change”), “Constant GDP per Capita for the United States” (with units set to “Percent Change”), “Population Growth for the United States” (with units set to “Percent Change at Annual Rate”). Then, in the Formula bar, enter the formula a-b-c-d. In the “Add Line” tab, repeat the above steps for Germany, Italy, Japan, and the U.K.

Suggested by Asha Bharadwaj and Maximiliano Dvorkin.

View on FRED, series used in this post: CPALTT01JPA661S, CPALTT01USA661S, DEUCPIALLAINMEI, GBRCPIALLAINMEI, GGGDTADEA188N, GGGDTAGBA188N, GGGDTAITA188N, GGGDTAJPA188N, GGGDTAUSA188N, IRLTLT01DEM156N, IRLTLT01GBM156N, IRLTLT01ITM156N, IRLTLT01JPM156N, IRLTLT01USM156N, ITACPIALLAINMEI, NYGDPPCAPKDDEU, NYGDPPCAPKDGBR, NYGDPPCAPKDITA, NYGDPPCAPKDJPN, NYGDPPCAPKDUSA, SPPOPGROWDEU, SPPOPGROWGBR, SPPOPGROWITA, SPPOPGROWJPN, SPPOPGROWUSA


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