Tag Archives: EPA

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.

Environmental Injustice at the EPA

This past week, the EPA’s science advisory board held a public hearing on efforts to measure the “environmental justice” (EJ) impacts of EPA rules. EJ refers to adverse human health and environmental effects of government policies on minority and low income populations in the US. The EPA has released draft guidance to agency analysts who measure these effects, and this hearing was intended to find ways to improve the guidance before it is finalized.

While holding a public hearing is a sign that the EPA is committed to getting this issue right, significant improvements need to be made to the EJ guidance if the EPA does not want the entire EJ project to backfire. Specifically, closer attention should be paid to the costs EPA rules impose on low income and minority populations. Further, improvements in the transparency of agency procedures will help ensure that those with modest incomes are allowed to participate in decisions that will have significant impacts on their health and well-being.

Currently, the EPA is focusing far more on the benefits of its rules to low income and minority groups than on the costs. As evidence, the 81-page draft guidance document contains only two pages related to costs of EPA regulations. In those two pages, the agency argues that costs are often not relevant to environmental justice issues, saying:

Consideration of the distribution of costs in the context of EJ is not always necessary. Often the costs of regulation are passed onto consumers as higher prices that are spread fairly evenly across many households.

This is a striking statement because regulatory costs are regressive exactly in the instances that the EPA describes in this statement. Any time costs of a policy are spread evenly across all citizens, the dollar amount paid to implement a regulation consumes a larger percentage of a poor person’s income than a wealthy person’s income. This is precisely why sales taxes are regressive.

Additionally, as incomes fall due to the costs imposed on citizens complying with regulations, people have fewer resources available to use toward risk reduction and outlays related to improving health. Meanwhile, there is evidence that private risk reduction can be much more effective than public methods of risk reduction, especially when regulations are addressing very small risks that are dwarfed by the other risks individuals face in their everyday lives.

A step in the right direction would be to ask analysts to identify the distribution of costs of EPA regulations, especially for rules that increase the prices of products that EJ populations purchase (e.g. rent, fuel, food, electricity).

Another important component of EJ is to gather meaningful feedback from low income and minority persons before implementing policies. The notice announcing last week’s public hearing was published in the Federal Register on Christmas Eve, making it unlikely that many in the EJ community, especially those with little political influence and low alertness to EPA actions, will even be aware this hearing is taking place, let alone will participate in the event.

If the EPA’s science advisory board is truly committed to improving the lot of the less well-off, it should tell the EPA to do more to measure the costs of environmental rules on low income and minority persons, and to improve transparency of agency procedures so those with less political clout can participate equally in the democratic process.

“Regulatory Certainty” as a Justification for Regulating

A key principle of good policy making is that regulatory agencies should define the problem they are seeking to solve before finalizing a regulation. Thus, it is odd that in the economic analysis for a recent proposed rule related to greenhouse gas emissions from new power plants, the Environmental Protection Agency (EPA) cites “regulatory certainty” as a justification for regulating. It seems almost any regulation could be justified on these grounds.

The obvious justification for regulating carbon dioxide emissions would be to limit harmful effects of climate change. However, as the EPA’s own analysis states:

the EPA anticipates that the proposed Electric Generating Unit New Source Greenhouse Gas Standards will result in negligible CO2 emission changes, energy impacts, quantified benefits, costs, and economic impacts by 2022.

The reason the rule will result in no benefits or costs, according to the EPA, is because the agency anticipates:

even in the absence of this rule, existing and anticipated economic conditions will lead electricity generators to choose new generation technologies that meet the proposed standard without the need for additional controls.

So why issue a new regulation? If the EPA’s baseline assessment is correct (i.e. it is making an accurate prediction about what the world would look like in absence of the regulation), then the regulation provides no benefits since it causes no deviations from that baseline. If the EPA’s baseline turns out to be wrong, a “wait and see” approach likely makes more sense. This approach may be more sensible, especially given all the inherent uncertainties surrounding predicting future energy prices and all of the unintended consequences that often result from regulating.

Instead, the EPA cites “regulatory certainty” as a justification for regulating, presumably because businesses will now be able to anticipate what emission standards will be going forward, and they can now invest with confidence. But announcing there will be no new regulation for a period of time also provides certainty. Of course, any policy can always change, whether the agency decides to issue a regulation or not. That’s why having clearly-stated goals and clearly-understood factors that guide regulatory decisions is so important.

Additionally, there are still costs to regulating, even if the EPA has decided not to count these costs in its analysis. Just doing an economic analysis is a cost. So is using agency employees’ time to enforce a new regulation. News outlets suggest “industry-backed lawsuits are inevitable” in response to this regulation. This too is a cost. If costs exceed benefits, the rule is difficult to justify.

One might argue that because of the 2007 Supreme Court ruling finding that CO2 is covered under the Clean Air Act, and the EPA’s subsequent endangerment finding related to greenhouse gases, there is some basis for the argument that uncertainty is holding back investment in new power plants. However, if this is true then this policy uncertainty should be accounted for in the agency’s baseline. If the proposed regulation alleviates some of this uncertainty, and leads to additional power plant construction and energy creation, that change is a benefit of the regulation and should be identified in the agency’s analysis.

The EPA also states it “intends this rule to send a clear signal about the current and future status of carbon capture and storage technology” because the agency wants to create the “incentive for supporting research, development, and investment into technology to capture and store CO2.”

However, by identifying the EPA’s preferred method of reducing CO2 emissions from new power plants, the agency may discourage businesses from investing in other promising new technologies. Additionally, by setting different standards for new and existing power plants, the EPA is clearly favoring one set of companies at the expense of another. This is a form of cronyism.

The EPA needs to get back to policymaking 101. That means identifying a problem before regulating, and tailoring regulations to address the specific problem at hand.

Do We Need Greater Congressional Oversight of Agency Rulemaking?

Katherine McFate, president of the Center for Effective Government, writes in the Hill that all regulations are based on congressional law, implying that efforts aimed at greater oversight of agency rulemaking are unnecessary. Technically, she is correct – agencies cannot regulate unless they are authorized to do so by congressional statutes. But her assertion is highly misleading. In fact, agencies have considerable discretion to determine policy and to publish rules that fit their as opposed to Congress’s agenda. Thus, Congress is fully justified in its efforts to push for greater agency accountability.

Scholars have long realized that the traditional rulemaking model (or the “transmission belt” model as Richard Stewart called in his seminal article) in which Congress determined policy through legislation and agencies simply filled in the details was far from reality. While constrained by congressional statutes, agencies nonetheless can substantively shape the policies within their jurisdiction.

Agencies have two sources of power in the rulemaking process: the first mover advantage and expertise. Congress over time delegated considerable policymaking powers to agencies through broad open-ended statutes, which meant that agencies did not need to seek congressional approval in order to regulate. In many cases, they can point to existing statutes as a source of their authority. Rather than initiate policy, Congress ends up reacting to the bureaucracy’s regulatory actions. Yet, given how difficult it is for Congress to agree on any legislation, it may be an uphill battle for Congress to overturn a regulation, letting agency decision stand by default.

Expertise is the second source of agency power. Congress defers to agency expertise on many complex regulatory issues. However, agencies engage in what Wendy Wagner called “the science charade” – masking policy decisions as matters of science. As she explains in her article and an edited volume, scientific analysis often drives policy decisions. Through selective use of evidence or assumptions, agencies can push the scientific analysis towards the answer that would yield their preferred policy alternative.

The EPA’s and DOE’s use of social cost of carbon (SCC) in their rulemaking estimates demonstrates the point. The SCC is an estimate of economic damages caused by greenhouse gas emissions. Agencies use the SCC to estimate the benefits of rules aimed at reducing greenhouse gas emissions and consequently to decide whether the rules’ benefits justify the costs. Higher SCC estimate would justify more expansive and costly regulations.

Even though the agencies claim that they derived the SCC through scientific analysis, critics point to policy choices embedded within the analysis that pushed the estimated cost higher. For example, the agencies chose to estimate global rather than domestic impacts of carbon. Similarly, they omitted from their analysis the recent scientific literature, which pointed to a lower impact of greenhouse gases on climate. These and other choices pushed the SCC higher (almost double the previous estimate), allowing agencies to push for more stringent and costly regulations.

Despite the major policy impact of the SCC’s use in rulemaking, the agencies did not have to consult Congress. They could chose to use the SCC estimates under the powers already delegated to them, even in the face of stiff opposition from Congress. In the meantime, congressional efforts to reassert its authority on the major environmental policy issue have stalled. The GOP-led House passed a bill that would prevent the EPA from factoring in the SCC in its economic analysis. Yet, the measure’s fate in the Senate is uncertain and it would still face the presidential veto.

Contrary to McFate’s assertion, agencies do not simply implement congressional policy. As the SCC example demonstrates, agencies can drive major policy decisions without congressional approval. Thus, Congress needs better tools for more effective oversight of agency regulations.

Nudges or Shoves?

David Brooks writes in the New York Times:

I’d say the anti-paternalists win the debate in theory but the libertarian paternalists win it empirically. In theory, it is possible that gentle nudges will turn into intrusive diktats and the nanny state will drain individual responsibility.

But, in practice, it is hard to feel that my decision-making powers have been weakened because when I got my driver’s license enrolling in organ donation was the default option. It’s hard to feel that a cafeteria is insulting my liberty if it puts the healthy fruit in a prominent place and the unhealthy junk food in some faraway corner. It’s hard to feel manipulated if I sign up for a program in which I can make commitments today that automatically increase my charitable giving next year. The concrete benefits of these programs, which are empirically verifiable, should trump abstract theoretical objections.

I agree with Brooks that arguments over nudges should be based on empirical evidence rather than a purely theoretical discussion. So let’s examine the evidence.

As I pointed out in a recent op-ed:

On the federal level, energy efficiency regulations costing billions of dollars are justified by claiming to correct consumers’ irrational choices. Regulators claim that given the lifetime energy savings, rational consumers would demand more efficient vehicles and appliances voluntarily. They take the fact that many consumers are willing to forego efficiency in favor of other attributes, such as style, safety or lower upfront costs, as a clear proof that consumers are irrational. Hence, regulators force consumers to save by reducing their choices.

Below is a list of recent major federal regulations that use behavioral economics arguments to justify government intervention in markets. While far from exhaustive, it should give you some idea as to the magnitude of “intrusive diktats” that are justified using the nudge philosophy. Note that these regulations are not nudges. This is hard paternalism. Federal regulations do not gently push you towards better choices or give you a chance to opt-out. Contrary to Brooks’ assertion, it is not only in theory that “gentle nudges turn into intrusive diktats.”

For comparison, I can think of no major federal policy that actually nudges. When one looks at the evidence, federal regulators give consumers few nudges but plenty of shoves.

Agency Rule Cost (millions)
EPA/DOT Control of Greenhouse Gases from Light-Duty Vehicles

$176,995

EPA/DOT Greenhouse Gas & Fuel Efficiency for Medium/Heavy Duty Vehicles

$9,600

DOE Energy Conservation Program: Small Electric Motors

$514

DOE Energy Efficiency Standards for Pool Heaters and Direct Heating Equipment and Water Heaters

$1,012

DOE Energy Efficiency Standards for Commercial Clothes Washers

$23

DOE Energy Efficiency Standards for Residential Refrigerators and Freezers

$1,849

DOE Energy Efficiency Standards for Microwave Ovens

$1,341

DOE Energy Conservation Program: Energy Conservation Standards for Fluorescent Lamp Ballasts

$425

DOE Energy Conservation Program: Energy Conservation Standards for Distribution Transformers

$289-$351

DOE Energy Conservation Program: Energy Conservation Standards for Battery Chargers and External Power Supplies

$247

DOE Energy Conservation Standards for General Service Fluorescent Lamps and Incandescent Reflector Lamps

$77-$139

————————-

Addendum: Some of these costs are annualized; some are total. There is no consistency in the way they are reported. Agencies report one or the other but not both. In addition, in an earlier version of this post, two figures were transposed. I have now corrected this.

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.

New Year Brings New Fees for Residents and Non-Profits

Leery of raising taxes and issuing more debt some state and local governments are turning to fees to extract revenues from residents as well as hitherto exempt non-profits. The Wall Street Journal reports that cities from Houston to  Richmond, and Minneapolis are applying fees to non-profits for fixing street-lights, sewer drainage, and other services. The cost for some services has increased due to new EPA regulations for storm-water runoff, and real estate development. The cost of repairing storm systems in the U.S. is estimated at $250 billion by the American Society of Civil Engineers. Non-profits are protesting the fees in some places. Dade City, Florida and Chicago scrapped plans to levy fees on non-profits after being sued.