Category Archives: General

Pros and cons of spatial targeting

An earlier post described how some residents of inner-city Baltimore in the 1960s were excluded from the benefits of certain programs because they lived outside the areas in which these programs were targeted. The programs had identified areas with the highest proportions of those in need and limited their services to those areas and their residents. Such spatial targeting has both benefits and costs, which I will consider here.

First, a definition: By spatial targeting, I mean a program for the delivery of services to some group of people, typically disadvantaged, that begins by identifying an area having the highest concentration of those people in need. The services of the program are then provided by facilities that are located in the target area. The targeting of those services can be more or less exclusive. More restrictive targeting limits eligibility for the service to those residing within the target area. Less restrictive policies make the service available to all who would otherwise be eligible, though obviously obtaining the service will be less convenient for those living farther from the target area.

Targeting has advantages. It is efficient in the sense of placing the service locations closest to the greatest numbers of intended beneficiaries. The more restrictive form of targeting can eliminate the need for what might be considered to be obtrusive means tests by restricting eligibility to the residents of the target area. (This is essentially equating “need” with target-area residence.)

A more subtle potential benefit of spatial targeting could come from the effect of the service in improving the overall levels of well-being within the area. The concentration of disadvantge in an area can have negative effects for all area residents (what economists would call a negative externality). By improving conditions for some residents of the area, these adverse neighborhood effects may be reduced, yielding further benefits that accrue to all residents.

Of course these benefits do not come without costs. A targeted program effectively discriminates against those living outside the target area. Those with equal need not living in the area may not be able to receive the services, either because of a policy restricting benefits to target-area residents or because distance to the target area makes the services inaccessible, even without a residence requirement.

With strict targeting substituting a residence requirement for a means test, some service can be provided to those with lesser need. Arguably this could be seen as being less efficient in that not as much service is being provided to those with the greatest need.

Finally, spatial targeting can limit political support for the provision of the service. To be sure, if the service is aimed at the disadvantaged, they will not constitute a majority of the electorate in any event. But by restricting the service to a limited areas, those in areas outside, both disadvantaged and not, may be less inclined to support an activity that does not assist those in need outside the target area. And spatial targeting by its very nature emphasizes the fact that the service is being provided for a relatively small segment of the population.


Images of Infinite Suburbia

Several years ago, I did a series of posts discussing how articles and books critical of urban sprawl were inevitably illustrated with overhead views of fairly dense single-family development (first post), while work promoting new urbanism and smart growth more frequently showed those environments from the ground-level perspective of their residents (second post). And indeed, some of those new urbanist developments looked surprisingly similar to the sprawling areas when vieweed from above (third post).

The new book Infinite Suburbia (edited by Alan Berger and Joel Kotkin) contains scores of articles looking at suburban development from a wide range of perspectives. (I will post some additional comments on this book when I have finished reading it. (The book is huge–over 700 pages, about 6 pounds!) Many different points of view are expressed in the various pieces. The book is certainly not an unambiguous critique of urban sprawl.

But despite taking a balanced view of suburban development, the book gives in to the idea that such development should be illustrated primarily with views from above. The book includes many 2-page color spreads (nearly 50) of aerial photos of suburban areas from around the world, frequently showing areas discussed in the accompanying articles. The pictures are gorgeous, but they reinforce the notion that suburban patterns should be perceived and understood from above, not from the points of view of their inhabitants.

2-page aerial view from Infinite Suburbia

2-page aerial view from Infinite Suburbia

To be sure, many of the articles include additional images of the suburban environments being discussed. But these are much smaller than the 2-page aerial-view spreads. (Some of the largest of the pictures within the articles were one-sixth the size (in terms of area) of the overhead images.) And many of these were additional overhead images, not views from the perspective of residents of the areas. The message, unfortunately, is unambigous: Suburban development is best viewed from above.

Talk about place-based discrimination

In the previous post I described the way in which people in Baltimore were frustrated that programs were being targeted to specific areas and excluded their neighborhoods and themselves. A New York Times Upshot article describes a truly horrible proposal passed by one house of the Michigan legislature that would establish place-based discrimination in an indefensible manner.

At issue is the establishment of a work requirement as a condition of eligibility for Medicaid. Whatever one thinks about the appropriateness of such a requirement, I think everyone would accept that some Medicaid recipients should be exempted due to an inability to work. The proposal would exempt recipients from the work requirement if they lived in a county with an unemployment rate greater than 8.5 percent. The rationale is that people should not be required to work if they live in areas in which jobs are hard to find.

It is of course entirely reasonable to exempt Medicaid recipients from the work requirement if it is impossible for them to find a job. And the level of unemployment is certainly one indicator of the difficulty. Of course, so also might be a measure of the number of discouraged workers who have given up looking for jobs and who therefore do not contribute to the unemployment rate. And why the unemployment rate in a county as opposed to some other area, such as the city where the recipient lives? (The Times article raises this.) Further, the skill requirements for available jobs versus the qualifications of the Medicaid recipients is clearly relevant to their ability to find work.

It might be argued that the county unemployment rate exemption could be reasonable as one criterion for exemption from a work requirement along with other criteria relating to the inability to find a job. But since any other conditions are necessarily going to be more involved, they will serve as a higher threshold for those Medicaid recipients living in the nonexempt counties.

And as the Times article points out, the counties and Medicaid recipients exempted are predominantly white and rural. It would be difficult not to conclude that this proposal reflects racially biased stereotypes about the deserving versus the undeserving poor.

“We’ve been screwed again”

Fifty years ago (!) I was working as a VISTA Volunteer with the antipoverty program in Baltimore. The Community Action Agency provided services from neighborhood centers in their target areas on the east and west sides of the inner city. The neighborhood center at which I worked was at the southwest corner of the west side target area. In fact, the center was actually one block outside of the target area in an old, unused branch public library building.

This detail on location is important. People came into our center looking for help in getting a job. A variety of programs were available that provided employment assistance and job training. But many of these programs had as a condition of eligibility that the recipient live within the antipoverty program target area. Someone who came in from down the block, living outside the target area, could not participate in those programs.

At least they were not supposed to. To get them help, we would suggest that they use the address of someone they knew a few blocks over, inside the target area. Not exactly proper. But it was a way of getting help for people who needed it.

This obviously seemed more than a little unfair to us and to the residents living near the neighborhood center who were confronted with this residence requirement. But a later event showed that the unfairness was perceived more widely.

A Baltimore city planner was undertaking a subcity district planning effort for an area of southwest Baltimore extending out from the center of the city. He had worked with a local priest to bring together the leaders of numerous community organizations to provide input to the planning process. (This was pretty revolutionary stuff for 1968.)

Baltimore had been approved for participation in the new Model Cities program and was planning the election of citizen advisory boards, the first step in implementation. The Model Cities program was restricted (by statute) to spatially limited areas. Baltimore’s Model Neighborhood encompassed an area on the west side similar to the antipoverty program target area.

The city planner briefed the community representatives he was working with about the Model Cities program. This was seen as a big deal by both city officials and neighborhood residents as it promised to bring major federal resources to the Model Neighborhood. (The Model Cities program failed to meet this promise but that was later.)

But there was one problem. The Model Neighborhood included only the inner portion of the planning area from which the community representatives had come. Those representing neighborhoods inside the Model Neighborhood were excited about the prospects for the program. Then a representative of one of the neighborhoods outside the Model Neighborhood got up to express the sentiments of the others, obviously thinking about the antipoverty program targeting as well: “We’ve been screwed again.”

Category: General
Tags: antipoverty, Model Cities, citizen participation, spatial targeting, VISTA, volunteer

Brookings map shows MSAs mapped the right way

The previous post discussed the problems with maps of Metropolitan Statistical Areas that showed the extent of the counties included. It included this map from the Census giving the misleading impression that the area of the greatest population increase was in the southwestern United States, the large dark purple area:

Percentage Change in Metropolitan and Micropolitan Statistical Area Population: 2000 to 2010. Source: Metropolitan and Micropolitan Statistical Area Population: 2000 to 2010. Source: U.S. Bureau of the Census. 2011. Population Distribution and Change: 2000 to 2010. 2010 Census Briefs.

Source: U.S. Bureau of the Census. 2011. Population Distribution and Change: 2000 to 2010. 2010 Census Briefs.

In that post I also presented an alternative map of MSAs that avoids this problem and referred to a note with a more extensive discussion of these issues. That note ended with my doubts about whether the way MSAs were mapped would change. I was wrong.

A recent post by William Frey of the Brookings Institution looked at recent population changes from a new census report (US population disperses to suburbs, exurbs, rural areas, and “middle of the country” metros). It includes a map illustrating population changes from 2016 to 2017 for the 100 largest metropolitan areas:

Source: William H. Frey analysis of U.S. Census Population Estimates, released March 22, 2018

Source: William H. Frey analysis of U.S. Census Population Estimates, released March 22, 2018

Hooray! Point symbols are used to map the MSAs and their population changes. The map does not give the misleading impression of the Census map shown above.

One very minor suggestions to Brookings for further improvement: The points appear to be located at the centers of the MSAs. It would be even better if they were located in the vicinity of where most of the populations of the MSAs were located.

The problem with maps of Metropolitan Statistical Areas

Metropolitan Statistical Areas (MSAs) are one of the most important statistical units for the reporting of data by federal agencies. It is common to see maps of showing those areas, such as this map from the census illustrating changes in population from 2000 to 2010:

Percentage Change in Metropolitan and Micropolitan Statistical Area Population: 2000 to 2010. Source: Metropolitan and Micropolitan Statistical Area Population: 2000 to 2010. Source: U.S. Bureau of the Census. 2011. Population Distribution and Change: 2000 to 2010. 2010 Census Briefs.

Source: U.S. Bureau of the Census. 2011. Population Distribution and Change: 2000 to 2010. 2010 Census Briefs.

The MSAs are colored with various shades of purple, darker for greater percentage population change. (The green areas are the Micropolitan Statistical Areas, the smaller analogues of the MSAs.)

A large area of MSAs can be seen in the southwestern United States, extending from eastern Arizona to the Pacific (and up through California). But much of this area is hardly metropolitan in any meaningful sense. It is sparsely settled mountains and desert land. The only reason this is within MSAs is that MSAs are composed of entire counties. And many counties in the West are very large, resulting in such empty areas being included along with the portions of the counties that are truly metropolitan. (See my earlier post on not calculating densities for MSAs for further discussion.)

This not only affects perceptions of the extent of metropolitan areas, it can also be quite misleading when MSAs are used to map data. Consider the map shown above. The darkest purple areas are the MSAs that had the greatest percentage population increase over the decade. One immediately observes the very large dark purple area extending across southern California, Arizona, and even into Nevada and Utah. So is this where large population gains were most dominant? Looking more closely at the map, you might notice that numbers of the MSAs in Texas are also dark purple. In comparison, these areas look small and scattered. But they include all of the largest MSAs in Texas and many smaller ones. And the Texas MSAs in this highest growth category had a total population in 2010 that was over twice that of the highest-growth MSAs in the southwest. Sizes of MSAs can be very misleading.

But MSAs do include entire counties, so what is the alternative for mapping these areas? One possibility is to use point symbols in place of the county areas for showing the presence and location of MSAs. And if one wants to show some characteristic of the MSAs, the sizes of those symbols can be varied. This is an alternative map of MSAs with the symbols graduated to show the population sizes of the areas:

Metropolitan Statistical Areas by Population 2010.

Metropolitan Statistical Areas by Population 2010.

A more extensive discussion is in the note, “The Problems with Maps of Metropolitan Statistical Areas,” which can be downloaded here.

What you can’t say about variation within metropolitan areas

The New York Times recently published an article titled (on the web) “Why Outer Suburbs in the East and Midwest Have Stopped Booming.” It’s a curious piece, based on data by county across the U.S., showing those counties in which deaths have exceeded births. Indeed, the article includes an animated map of the entire country showing such counties from 1991 to 2016 and notes that over 1,200 counties had more deaths than births in 2016. This map of the counties, by the way, provides absolutely no way of identifying the counties in the outer suburbs of metropolitan areas or, for that matter, metropolitan area counties in general.

But the primary focus of the article, from the first paragraph to the last, is on what the title refers to as “outlying suburbs.” The article states that “about one in four outer-ring suburbs were experiencing more deaths than births, including 18 of 30 such counties in New York, New Jersey and Pennsylvania,” equating the outer-ring suburbs with counties. I won’t quibble about the lack of any definition of how a county is considered to be an “outer-ring suburb.” This is, after all, a piece of journalism, not a scholarly article.

The problem with drawing the conclusion about regional variation in births and deaths in outlying counties (claimed to be an issue in the Northeast and Midwest) is that this requires the existence of such “outer-ring suburb” counties. And the reporter’s view does not go that far west of the Hudson River, as is so often the case with the New York Times. Note the reference to the counties in New York, New Jersey, and Pennsylvania.

In order for a metropolitan area to have outer suburban counties, the metropolitan area must have a sufficient number of counties so that these can be distinguished from the inner suburban counties and from the urban area counties. For this to be the case, the metropolitan area must be fairly large and the counties must be quite small. And this is the problem. The Northeast and Midwest have numerous metropolitan areas that meet these criteria, for which one can draw such conclusions about “outer-ring suburbs.” Some metropolitan areas in the South also meet these criteria–the Atlanta metropolitan area would be a prime example, consisting of many counties.

But metropolitan areas in the West, where counties tend to be larger–often much larger–have few, if any counties that could be considered to be “outer-ring.” The San Diego Metropolitan Statistical Area (MSA) consists of one county. The Los Angeles MSA has 2 counties. San Francisco-Oakland MSA has 4 counties, but none could reasonably be considered to be “outer-ring suburbs.” If one took a more expansive view of the metropolitan areas than the MSA definition (which I would advocate), it might be plausible to identify a few counties as being outlying suburban counties. But even then, these would be few and isolated. Nowhere near the number of counties for which you could draw conclusions like the 18 of 30 counties in New York, New Jersey, and Pennsylvania.

Bottom line: It may be the case that “outer-ring” suburban counties in the Northeast and Midwest are now experiencing more deaths than births (though this is necessarily a weak conclusion given the lack of a definition of those counties). But stating this conclusion implies that this is not the case in the South and West. And it is impossible to draw any conclusions about “outer-ring” suburbs in most of the West based on county-level data.

You cannot say anything meaningful about variation within metropolitan areas across the U.S. using county level data. Differences in the sizes of counties and the very large counties in the West make this impossible.