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Where health is lacking Mapping public health issues with GeoFRED

[geofred id="9N7" esize="medium" height="900" width="1600"] GeoFRED maps can help us understand a lot of things, including trends in regional socioeconomic data, which could ultimately provide insights for policy recommendations. In this post, we look at two important indicators of health throughout the United States: premature deaths and preventable hospital admissions. High levels of premature deaths indicate issues with public health. (See a previous blog post for some background on this concept.) The South has a comparatively higher concentration of high rates in this area. [geofred id="9N6" esize="medium" height="900" width="1600"] The maps show a correlation between areas that suffer from high rates of premature death and areas that have a high rate of preventable hospital admissions, which is defined as stays in acute-care hospitals that could have been taken care of in ambulatory or ordinary inpatient settings, adjusted for socioeconomic factors. Examples are pneumonia, diabetes, and dehydration. A high rate of these admissions indicates that more people are lacking appropriate health options, likely leading to more preventable deaths. While regional trends and correlations do not indicate causation, a review of interconnected socioeconomic patterns over several years can be useful for understanding persistent problems in certain areas. Refer to GeoFRED for related maps on race, income inequality, homeownership, burdened homeowners, and disconnected youth. How these maps were created: Premature Death: From GeoFRED, click on “Build New Map.” Under the “Tools” menu, select “County” in the region type search bar. For the first map, enter “premature death” in the data search bar and then select “Age-Adjusted Premature Death Rate”; for the second map, enter “preventable hospital admissions” in the data search bar and then select “Rate of Preventable Hospital Admissions.” Suggested by Samantha Kiss and Christian Zimmermann.

Is college still worth it? Re-examining the college premium

A recent symposium held by the Center for Household Financial Stability at the St. Louis Fed looks at the question of whether the college premium is still increasing and positive, using new data from the Fed’s Survey of Consumer Finances. On an absolute level, college graduates earn more than high school graduates, as shown in the graph above. This is consistent with the understanding that the benefits of a college education are greater than the costs.
If we look at the college premium, we can see that it has always been positive, indicating that there is a positive benefit of graduating with a bachelor’s degree. This graph shows that, at the end of the first quarter of 2018, college graduates received weekly wages that were 80 percent higher than those of high school graduates.
However, there’s more to this story. Recent research shows that the college premium may or may not be very strong depending on birth year, family, and other inherited characteristics. When looking at the wealth premium instead of just the income premium, the college premium was weak for all races and ethnicities in the 1980s cohorts, whereas the college premium exists for cohorts in earlier decades. A potential reason for this result is the high and rising cost of college. Over the past decade, we see an increase in the dollar amount of total outstanding student loans per total number of college graduates in the labor force, reaching almost $27,000 per college graduate available for work at the end of the first quarter of 2018. High levels of student debt may affect the ability to accumulate wealth, resulting in the declining college wealth premium. This is just one of the reasons for further investigation into the college premium, rising tuition costs, and how education influences economic well-being. How these graphs were created: For the first graph, search for “wages bachelor’s degree” and select the quarterly data series to add to the graph. From the “Edit Graph” panel, go to “Add Line” and search for “wages high school” and select the corresponding series. To create the second graph, use the same steps to get to the “wages bachelor’s degree” series. Then under the “Customize data” section, search for “wages high school” and select the series. Then enter in the formula (a/b) – 1 to get the college premium. For the third graph, search for “student loans” and select the series for outstanding student loans. From the “Edit Graph” panel, go to “Customize data,” search for “bachelor’s labor force level” to add to the graph. Then in the formula bar, divide line 1 by line 2 and adjust units to show dollars (i.e., enter a/b*1000000). Suggested by Suvy Qin and Christian Zimmermann.
View on FRED, series used in this post: LEU0252917300Q, LEU0252918500Q, LNS11027662, SLOAS

Why is it so difficult to live where you work? Housing costs and homeownership in economic centers

[geofred id="9Nu" esize="medium" height="900" width="1600"] In some areas of the U.S., housing has become so expensive that people find it difficult or impossble to afford housing anywhere near where they work. The recent focus on the homeless population in Los Angeles highlights the most extreme form of this situation: Many of the homeless in that area are not only employed, but also are experiencing homelessness for the first time. Unaffordable housing and long commutes are particularly burdensome for low-income individuals, but these issues have consequences for all Americans. (Check out a previous FRED blog post on the distribution of commute times in the U.S. for more information.) [geofred id="9Nv" esize="medium" height="900" width="1600"] The maps in this post show U.S. county-level data from 2016 for two concepts: the homeownership rate and burdened households. Both depict a spatial representation of affordable housing in the U.S. Homeownership is clustered away from urban centers. Counties such as Los Angeles, Suffolk, and Cook (home to the cities of L.A., Boston, and Chicago) report homeownership rates of 48, 37, and 54 percent, respectively, significantly lower than the national average of 70 percent. Burdened households lack access to affordable housing, which the U.S. Department of Housing and Urban Development defines as “housing for which the occupant(s) is/are paying no more than 30 percent of his or her income for gross housing costs, including utilities.” The maps show that the least affordable housing, represented by low homeownership rates and a high density of burdened households, lies in urban areas rich with economic opportunity. Living close to work has a significant beneficial impact on employment and happiness. If they can choose to, individuals are likely to live closer to where they work; and workers with accessible jobs are more resistant to joblessness and long periods of job searching. Proximity matters the most for low-income residents, who are more constrained by housing and commuting costs. Hence, accessibility to employment increases the chances not only of working but also of escaping welfare. More affordable housing has the potential to increase efficiency and optimization, key concepts in the study of economics: Low-income residents might gain greater economic mobility, and more high-skilled, talented individuals might move into urban areas to help maximize the economic potential of those areas. Also, the average American might simply be able to cut down on time spent in traffic getting to work. How these maps were created: From GeoFRED, click “Build New Map.” From the “Tools” menu, select “County” as “Region Type” and expand the “Data” selection. Under “Data,” search for “Homeownership Rate” and then “Burdened Households.” Suggested by Elizabeth Tong and Christian Zimmermann.

Why does cost of living vary so much? Housing, housing, housing

[geofred id="9ys" esize="medium" height="900" width="1600"] If the map above looks familiar, either you're experiencing déjà vu or you read our post last year about regional price parities (RPPs), which measure cost of living in metropolitan areas. Cost of living is generally persistent over time, which is why our updated map of the 2016 RPPs looks eerily similar to last year’s map. (The data are released on a two-year lag, by the way.) A reminder: The national average cost of living is set equal to 100. So, an RPP above 100 means an area is more expensive than the national average and an RPP below 100 means it's less expensive than the national average. Of the 349 metro areas in the data, 94 fall within 5 percent of the national average. As we showed last year, high cost of living remains concentrated in the Northeast and on the West Coast. As of 2016, San Jose, CA, takes the title of most-expensive metro area, with a cost of living 27 percent above the national average. The Midwest and South are still the least-costly places to live. In the cheapest metro area, Morristown, TN, the cost of living is more than 20 percent below the national average. And why are some metro areas more expensive than others? Housing. The single largest consumer expenditure category is housing, and that drives most differences in cost of living (source). The map below shows the RPPs for rents, which range from nearly 50 percent below the national average to over 200 percent above. Because households spend about 20 percent of income on housing, high rent prices beget high cost of living overall. It’s no coincidence that San Jose also has the highest rent RPP. [geofred id="9yz" esize="medium" height="900" width="1600"] In contrast, the goods RPPs on the next map show much less regional variation. Unlike housing, goods are more easily tradeable, so arbitrage tends to suppress regional price differences. For example, if a laptop in San Jose costs more than the same laptop in Morristown, a consumer in San Jose may just buy the cheap laptop online from Morristown and have it shipped. To compete with its rivals in Morristown, retailers in San Jose would have to cut prices. Consequently, goods prices are much more uniform nationwide: All metro areas fall within 15 percent of the national average. [geofred id="9yy" esize="medium" height="900" width="1600"] The regional variation in goods prices that does exist likely results from goods that are more difficult to buy online, like fresh foods. For these items, businesses in areas with higher rent costs may charge higher prices to consumers to compensate, while businesses in areas with lower rent costs may charge lower prices. That said, some economists have found that most variation in food prices may be due to measurement error (source). How these maps were created: From GeoFRED, click “Build New Map,” open the tool bar in the top left corner, and select “Choose Data.” For the first map, under “Region Type” choose “Metropolitan Statistical Area” and under “Data” search for “Regional Price Parities: All Items.” For the other maps, simply change “Data” to “Regional Price Parities: Services: Rents” or “Regional Price Parities: Goods.” Use the “Edit Legend” tool bar to edit as you see fit. The data are also available at the state level. To view the maps of state RPPs, change the “Region Type” to “State.” Suggested by Charles Gascon and Andrew Spewak.


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