Tag Archives: Environmental Protection Agency

How effective are HUD programs? No one knows.

The Department of Housing and Urban Development, or HUD, has been in the news lately due to its policy proposal to ban smoking in public housing. HUD usually flys under the radar as far as federal agencies are concerned so many people are probably hearing about if for the first time and are unsure about what it does.

HUD was created as a cabinet-level agency in 1965. From its website, HUD’s mission:

“…is to create strong, sustainable, inclusive communities and quality affordable homes for all.” 

HUD carries out its mission through numerous programs. On the HUD website over 100 programs and sub-programs are listed. Running that many programs is not cheap, and the graph below depicts the outlays for HUD and three other federal agencies for reference purposes from 1965 – 2014 in inflation adjusted dollars. The other agencies are the Dept. of Energy (DOE), the Dept. of Justice (DOJ), and the Environmental Protection Agency (EPA).

HUD outlays graph

HUD was the second largest agency by outlays in 2014 and for many years was consistently as large as the Department of Energy. It was larger than both the DOJ and the EPA over this period, yet those two agencies are much better known than HUD. Google searches for the DOJ, EPA and HUD return 566 million, 58.3 million, and 25.7 million results respectively.

For such a large agency HUD has managed to stay relatively anonymous outside of policy circles. This lack of public scrutiny has contributed to HUD being able to distribute billions of dollars through its numerous programs despite little examination into their effectiveness. To be fair HUD does make a lot of reports about their programs available, but these reports are often just stories about how much money was spent and what it was spent on rather than evaluations of a program’s effectiveness.

As an example, in 2009 the Partnership for Sustainable Communities program was started. It is an interagency program run by HUD, the EPA, and the Dept. of Transportation. The website for the program provides a collection of case studies about the various projects the program has supported. The case studies for the Euclid corridor project in Cleveland, the South Lake Union neighborhood in Seattle, and the Central Corridor Light Rail project in Minneapolis are basically descriptions of the projects themselves and all of the federal and state money that was spent. The others contain similar content. Other than a few anecdotal data points the evidence for the success of the projects consists of quotes and assertions. In the summary of the Seattle project, for example, the last line is “Indeed, reflecting on early skepticism about the city’s initial investments in SLU, in 2011 a prominent local journalist concluded, “It’s hard not to revisit those debates…and acknowledge that the investment has paid off”. Yet there is no benchmarking in the report that can be used to compare the area before and after redevelopment along any metric of interest such as employment, median wage, resident satisfaction, tax revenue, etc.

The lack of rigorous program analysis is not unique to the Sustainable Communities program. The Community Development Block Grant Program (CDBG) is probably the best known HUD program. It distributes grants to municipalities and states that can be used on a variety of projects that benefit low and moderate income households. The program was started in 1975 yet relatively few studies have been done to measure its efficacy. The lack of informative evaluation of CDBG projects has even been recognized by HUD officials. Raphael Bostic, the Assistant Secretary of the Office of Policy Development and Research for HUD from 2009 – 2012, has stated “For a program with the longevity of the CDBG, remarkably few evaluations have been conducted, so relatively little is known about what works” (Bostic, 2014). Other government entities have also taken notice. During the Bush administration (2001 – 08) the Office of Management and Budget created the Program Assessment Rating Tool (PART). Several HUD programs were rated as “ineffective” – including CDBG – or “moderately effective”. The assessments noted that “CDBG is unable to demonstrate its effectiveness” in developing viable urban communities and that the program’s performance measures “have a weak connection to the program purpose and do not focus on outcomes”.

Two related reasons for the limited evaluation of CDBG and other HUD programs are the lack of data and the high cost of obtaining what data are available. For example, Brooks and Sinitsyn (2014) had to submit a Freedom of Information Act request to obtain the data necessary for their study. Furthermore, after obtaining the data, significant time and effort were needed to manipulate the data into a usable format since they “…received data in multiple different tables that required linking with little documentation” (p. 154).

HUD has significant effects on state and local policy even though it largely works behind the scenes. Regional economic and transportation plans are frequently funded by HUD grants and municipal planning agencies allocate scarce resources to the pursuit of additional grants that can be used for a variety of purposes. For those that win a grant the amount of the grant likely exceeds the cost of obtaining it. For the others, however, the resources spent pursuing the grant are largely wasted since they could have been used to advance the agency’s core mission. The larger the grant the more applicants there will be which will lead to greater amounts of resources being diverted from core activities to pursuing grants. Pursuing government grants is an example of rent-seeking and wastes resources.

Like other federal agencies, HUD needs to do a better job of evaluating its abundant programs. Or better yet, it needs to make more data available to the public so that individual researchers can conduct and duplicate studies that measure the net benefits of its programs. Currently much of the data that are available are usually only weakly related to the relevant outcomes and often are outdated or missing.

HUD also needs to specify what results it expects from the various grants it awards. Effective program evaluation starts with specifying measurable goals for each program. Without this first step there is no way to tell if a program is succeeding. Many of the goals of HUD programs are broad qualitative statements like “enhance economic competitiveness” which are difficult to measure. This allows grant recipients and HUD to declare every program a success since ex post they can use whatever measure best matches their desired result. Implementing measurable goals for all of its programs would help HUD identify ineffective programs and allow it to allocate more scarce resources to the programs that are working.

Local land-use restrictions harm everyone

In a recent NBER working paper, authors Enrico Moretti and Chang-Tai Hsieh analyze how the growth of cities determines the growth of nations. They use data on 220 MSAs from 1964 – 2009 to estimate the contribution of each city to US national GDP growth. They compare what they call the accounting estimate to the model-driven estimate. The accounting estimate is the simple way of attributing city nominal GDP growth to national GDP growth in that it doesn’t account for whether the increase in city GDP is due to higher nominal wages or increased output caused by an increase in local employment. The model-driven estimate that they compare it to distinguishes between these two factors.

Before I go any further it is important to explain the theory behind the author’s empirical findings. Suppose there is a productivity shock to City A such that workers in City A are more productive than they were previously. This productivity shock could be the result of a new method of production or a newly invented piece of equipment (capital) that helps workers make more stuff with a given amount of labor. This productivity shock will increase the local demand for labor which will increase the wage.

Now one of two things can happen and the diagram below depicts the two scenarios. The supply and demand lines are those for workers, with the wage on the Y-axis and the amount of workers on the X-axis. Since more workers lead to more output I also labeled labor as L = αY, where α is some fraction less than 1 to signify that each additional unit of labor doesn’t lead to a one unit increase in output, but rather some fraction of 1 unit (capital is needed too).

moretti, land use pic

City A can have a highly elastic supply of housing, meaning that it is easy to expand the number of housing units in that city and thus it is relatively easy for people to move there. This would mean that the supply of labor is like S-elastic in the diagram. Thus the number of workers that are able to migrate to City A after labor demand increases (D1 to D2) is large, local employment increases (Le > L*), and total output (GDP) increases. Wages only increase a little bit (We > W*). In this situation the productivity shock would have a relatively large effect on national GDP since it resulted in a large increase in local output as workers moved from relatively low-productivity cities to the relatively high-productivity City A.

Alternatively, the supply of housing in City A could be very inelastic; this would be like S-inelastic. If that is the case, then the productivity shock would still increase the wage in City A (Wi > W*), but it will be more difficult for new workers to move in since new housing cannot be built to shelter them. In this case wages increase but since total local employment stays fairly constant due to the restriction on available housing the increase in output is not as large (Li > L* but < Le). If City A output stays relatively constant and instead the productivity shock is expressed in higher nominal wages, then the resulting growth in City A nominal GDP will not have as large of an effect on national output growth.

As an example, Moretti and Hsieh calculate that the growth of New York City’s GDP was 12% of national GDP growth from 1964-2009. But when accounting for the change in wages, New York’s contribution to national output growth was only 5%: Most of New York’s GDP growth was manifested in higher nominal wages. This is not surprising as it is well known that New York has strict housing regulations that make it difficult to build new housing units (the recent extension of NYC rent-control laws won’t help). This makes it difficult for people to relocate from relatively low-productivity places to a high-productivity New York.

In three of the most intensely land-regulated cities: New York, San Francisco, and San Jose, the accounting contribution to national GDP growth was 19.3%. But these cities actual contribution to national output as estimated by the authors was only 6.1%. Contrast that with the Rust Belt cities (e.g. Detroit, Pittsburgh, Cleveland, etc.) which contributed -28.5% according to the accounting method but +6.1% according to the author’s model.

The authors conclude that less onerous land-use restrictions in high-productivity cities New York, Washington D.C., Boston, San Francisco, San Jose, and the rest of Silicon Valley could increase the nation’s output growth rate by making it easier for workers to migrate from low to high-productivity areas. In an extreme migration scenario where 52% of American workers in 2009 lived in a different city than they actually did, the author’s calculate that GDP per worker would have been $8,775 higher in 2009, or $6,345 per person. In a more realistic scenario (only 20% of workers lived in a different city) it would have been $3,055 more per person: That is a substantial increase.

While I agree with the author’s conclusion that less land-use restrictions would result in a more productive allocation of labor and thus more stuff for all of us, the author’s policy prescriptions at the end of the paper leave much to be desired.  They propose that the federal government constrain the ability of municipalities to set land-use restrictions since these restrictions impose negative externalities on the rest of the country if the form of lowering national output growth. They also support the use of government funded high-speed rail to link  low-productivity labor markets to high-productivity labor markets e.g. the current high-speed rail construction project taking place in California could help workers get form low productivity areas like Stockton, Fresno, and Modesto, to high productivity areas in Silicon Valley.

Land-use restrictions are a problem in many areas, but not a problem that warrants arbitrary federal involvement. If federal involvement simply meant the Supreme Court ruling that land-use regulations (or at least most of them) are unconstitutional then I think that would be beneficial; a broad removal of land-use restrictions would go a long way towards reinstituting the institution of private property. Unfortunately, I don’t think that is what Moretti and Hsieh had in mind.

Arbitrary federal involvement in striking down local land-use regulations would further infringe on federalism and create opportunities for political cronyism. Whatever federal bureaucracy was put in charge of monitoring land-use restrictions would have little local knowledge of the situation. The Environmental Protection Agency (EPA) already monitors some local land use and faulty information along with an expensive appeals process creates problems for residents simply trying to use their own property. Creating a whole federal bureaucracy tasked with picking and choosing which land-use restrictions are acceptable and which aren’t would no doubt lead to more of these types of situations as well as increase the opportunities for regulatory activism. Also, federal land-use regulators may target certain areas that have governors or mayors who don’t agree with them on other issues.

As for more public transportation spending, I think the record speaks for itself – see here, here, and here.

Will The EPA’s Environmental Justice Agenda Backfire?

In May, the Environmental Protection Agency (EPA) proposed new guidelines for incorporating “environmental justice” into its rulemaking procedures. Environmental justice is the idea that all people, regardless of income or race, should be treated equally with respect to environmental laws, regulations and policies. Sounds pretty good, right? Unfortunately, it’s not quite as simple as waving a magic wand and making low income and minority communities cleaner and safer. Instead, EPA’s new guidelines may have unintended effects that harm the very people they are supposed to protect. Perversely, the poor may end up paying for a cleaner environment they won’t get to enjoy, while wealthier people enjoy it instead.

In the new guidelines, the EPA talks at length about how lower income and heavily minority areas are often correlated with environmental problems and a whole host of health risks. It may be that the poor are less politically organized than wealthier people and interest groups. Perhaps for this reason there are companies that purposefully pollute in poor and minority neighborhoods because the companies know they can get away with it. That seems plausible.

But there may be other reasons that poor people tend to live in more polluted areas. For example, the poor may move to polluted areas because those areas are less expensive to live in. I myself am a case in point.

When I was in my younger twenties, I tried to make a go at being a musician in New York City. I spent most of my time practicing with my band, so I generally worked odd jobs at off hours, living month to month and paycheck to paycheck. That way I could focus most of my time on music. When I first moved to New York, I lived in the Lower East Side of Manhattan. Then I moved to Harlem because it was cheaper. Finally, to save even more money, I moved to Greenpoint, a neighborhood in northern Brooklyn.

Most people don’t know this, but Greenpoint sits over one of the largest oil spills in North American history. In fact, there was a sign down the road from my house that signaled the end of my street was a “dumping station” where there was a physical pipe coming out of the ground dumping waste into a river, Newtown Creek. You could literally smell the oil in the air.

So why would I live like this? I paid $480 a month for a gigantic room with huge bay windows in a 3-bedroom apartment that was $1,300 a month in total, dirt cheap by NYC standards. My two roommates, also musicians, paid even less in rent than I did. We knew it wasn’t the best neighborhood; we weren’t stupid. We just thought the tradeoff was worth it.

So what does the EPA think will happen if it cleans up a neighborhood like Greenpoint? Would starving artists like myself and my old roommates benefit as we are finally freed from the exploitation of evil oil companies? Maybe, maybe not. The reasoning should be obvious to anyone who has ever lived in New York City.

Greenpoint is prime real estate situated extremely close to Manhattan. When the neighborhood gets cleaned up, real estate prices will rise as wealthier people, who previously wouldn’t tolerate the pollution, move in. Rents will go up as well. Those who own land will be better off, and some of them may currently be poor. But most low income people rent and don’t own property. Those people will have to move out as the area gets more expensive, or if they stay they will face a higher cost of living. Perhaps if they move, they will choose another polluted area to live in. Almost certainly, they will have a much longer commute if they work in Manhattan like I did.

The point of this story isn’t to say we should never clean up polluted areas. We should. But the EPA shouldn’t claim it is helping low income and minority populations when it is far from clear that they are the primary groups that will benefit from EPA rules. Regulations claiming to help the poor can be highly misleading if analysts don’t make an attempt to forecast (and analyze retroactively) adjustments in human behavior that result from regulatory changes.

There’s even more to the story. While the benefits to low income groups as a result of regulations justified on the basis of “environmental justice” can be highly uncertain, the costs are all too real. Costs of regulations are often spread evenly across everyone in society, much like the costs of regressive sales taxes are spread evenly across everyone who purchases a taxed product, and this includes the poor. We could end up in a perverse situation where the poor are actually paying to gentrify their own neighborhoods.

This is far from the only problem with the EPA’s new environmental justice guidelines, but it may be the most perverse. If the EPA really wants to help the poor, the agency should pay closer attention to changes in prices and human behavior that will result from its actions. The EPA should also seek feedback from, and provide detailed information on expected benefits and costs to, impacted communities before moving forward with a regulation. Otherwise, the EPA could be wasting a whole bunch of time with little to show for its efforts.