The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Federal Reserve Bank of Richmond 2016 ANNUAL REPORT Understanding Urban Decline ABOUT THE COVER The burned-out mansion on the cover was built in 1885 for lumber baron Lucien Moore in Brush Park, Detroit’s wealthiest neighborhood at the time. Brush Park declined rapidly in the 1930s and 1940s, becoming one of the city’s most blighted areas by 1960. But in recent years, developers have begun to renovate some of the neighborhood’s grand old homes. The Lucien Moore mansion, for example, was rebuilt in 2006 and retrofitted for apartments in 2012. Economists may debate the merits of various approaches to urban revitalization, but one way or another, the cycle of development and redevelopment usually runs its course. Cover Photo: Camilo José Vergara Adjacent Photo: Michael G. Smith Illustration: The Calvert Lith. Co., Library of Congress ABOUT THE RICHMOND FED MISSION: As a regional Reserve Bank, we serve the public by fostering the stability, integrity, and efficiency of our nation’s monetary, financial, and payments systems. VISION: To be an innovative policy and services leader for America’s economy. KEY FUNCTIONS: We contribute to the formulation of monetary policy. We supervise and regulate banks and financial holding companies headquartered in the Fifth Federal Reserve District. We process currency and electronic payments for banks and provide financial services to the U.S. Treasury. We also work with a wide variety of partners to strengthen communities in the Fifth District. Contents Message from the Interim President.........................................................................2 FEATURE ESSAY Understanding Urban Decline..................................................................................4 Fifth District Economic Report. . .............................................................................. 21 Boards, Councils, Officers, and Senior Professionals.....................................................................26 Federal Reserve Bank of Richmond Board.............................................................................................27 Baltimore Branch Board.........................................................................................................................28 Charlotte Branch Board..........................................................................................................................29 Community Depository Institutions Advisory Council............................................................................30 Community Investment Council.............................................................................................................30 Payments Advisory Council....................................................................................................................31 Management Committee.......................................................................................................................32 Officers and Senior Professionals...........................................................................................................33 Federal Reserve Information Technology (FRIT) Management Council.................................................34 FRIT Officers and Senior Professionals...................................................................................................35 Financial Statements...............................................................................................................................36 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 1 MESSAGE FROM THE INTERIM PRESIDENT Why the Richmond Fed Studies Urban Issues A s a regional Reserve Bank, an important part of the Richmond Fed’s job is studying what contributes to—or impedes—economic vitality in our region. Metropolitan areas, which are home to the majority of our population and economic activity, are an especially important part of the equation, as Santiago Pinto and Tim Sablik discuss in this year’s Annual Report essay, “Understanding Urban Decline.” Here in the Fifth District, we have some of the nation’s most culturally and economically vibrant cities, but we also have cities suffering persistent decline. And even within areas experiencing rapid growth, there are pockets of deeply entrenched poverty. Learning about the forces that shape urban areas has been of interest to the Richmond Fed for many years. We recently have focused those efforts, with our Research, Outreach, and Community Development functions joining together to study not just why cities grow, but also why they decline. We’re working to measure the consequences of various policy responses and to identify strategies that might help ameliorate urban decline, particularly with respect to housing, income, and transportation. We also are studying the constraints and incentives faced by the low- and moderate-income residents of our District to gain insight into economic mobility and the persistence of poverty. To do so, we’re convening academic and policy experts, conducting original research, and engaging with community and business leaders from throughout the region. Of course, our job is more than studying—it’s also sharing what we learn with the people who live and work in our District. That’s the goal of this year’s essay, in which Santiago and Tim explain the underlying economics of city formation and decline and distill the existing research on urban revitalization efforts. I’ll leave the details to the authors, but I would like to highlight some key takeaways. First, as they note, a city’s decline wouldn’t necessarily be a cause for concern if the people who lived there were able to easily move to another city with better amenities or employment opportunities. Some people might want to stay in a declining city, for example, to remain close to family or friends, but often there are factors preventing people who would like to move from doing so. 2 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Second, in response to urban decline, community leaders may consider two general types of approaches: place-based policies and people-based policies. Place-based policies focus on revitalizing the city itself, for example, by providing tax credits and grants to potential employers and developers. A number of these programs have been tried in the United States, and in the Fifth District, but the evidence of how well they work—and whether they actually benefit the people they’re intended to—is mixed. People-based policies include helping people move to areas with better economic prospects or enabling them to invest more in their education so they can put their skills to use in a new industry or city. Research has found that moving can have long-term positive effects, especially on young children, and the benefits of education are likely to extend well beyond the individual. But as with place-based policies, people-based policies can have unforeseen consequences—and there’s no guarantee that what works in one city will work in another. The challenge is that all cities are different—just head up from Charleston, South Carolina, to Charleston, West Virginia, by way of Charlotte, North Carolina, to see what I mean. Every city in our District has its own character, its own challenges, and its own successes. Here at the Richmond Fed, we’re excited and honored to have the opportunity to learn from those challenges and successes and to provide community leaders with information and connections that will help them design the most effective programs for their unique places and people. Just as cities undergo changes, so do institutions. The Richmond Fed is preparing for an important change right now. At the time of this writing, our Board of Directors is in the midst of its search for our next president and chief executive officer. During this period, I can confidently report that the entire Richmond Fed team remains focused and committed to delivering effectively on our policy, banking supervision, payments, and community development responsibilities. Mark L. Mullinix Interim President and Chief Operating Officer Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 3 In 1881, Detroit’s Brush Park neighborhood was called “Little Paris,” a nickname that faded long before this streetscape was photographed again in 2011. The cover of this annual report shows the same neighborhood in the foreground but not the same house that appears below. Photos: top ©Burton Historical Collection, Detroit Public Library; bottom ©Michael G. Smith 4 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Understanding Urban Decline By Santiago Pinto and Tim Sablik O ver the past two centuries, the population of the United States has become increasingly concentrated in cities. In the 1800s, only 6 percent of people lived in urban areas. Today, nearly two-thirds of Americans live in cities, and these cities account for only 3.5 percent of available land in the country.1 Urbanization also is taking place around the globe. More than half of the world’s population lives in cities today, and the World Bank estimates that cities collectively will add another two billion people by 2045. Not only is population in the United States concentrated in cities, the nation’s economic activity is as well. Large cities accounted for roughly 85 percent of the country’s gross domestic product (GDP) in 2010.2 Concentrating economic activity in this way produces a number of benefits. Places with higher population density exhibit faster growth in productivity and per-capita GDP. Cities are also wellsprings of innovation, accounting for a disproportionate share of new patents.3 Clearly, cities matter. These benefits make it all the more puzzling that a number of prominent U.S. cities have experienced large population declines in recent decades. St. Louis, Detroit, Cleveland, and Pittsburgh, for example, each lost half or more of their populations between 1950 and 2010. Others, such as Baltimore, Chicago, and Minneapolis suffered smaller, though still substantial, population losses during the same period. If these changes merely reflected shifts in population from one city to another more desirable or more productive city, there wouldn’t necessarily be any cause for concern. However, evidence suggests that urban population outflows have hurt some lower-income people who have been left behind. Declining city centers frequently exhibit high and persistent poverty rates. For instance, in Detroit and Cleveland, 40.3 percent and 36.2 percent of the population, respectively, were below the poverty line in 2015. Meanwhile, the average income of the surrounding suburbs has risen.4 In fact, the metropolitan statistical areas (MSAs) surrounding many declining cities have grown in population since 1950. For example, the Detroit and Baltimore MSAs each added more than one million people between 1950 and 2010.5 As city centers decline, those people and firms who can leave do, and those who cannot (frequently low-income, low-skilled households) are stuck with dimming economic prospects. Urban policymakers in declining cities justifiably want to revitalize their cities and help the people who live there. To do so effectively, it is important to first understand what factors determine where people and firms locate, both within and across cities, and what might cause them to move. Second, it is important to understand what policies will be effective at reversing urban decline. The economic benefits that arise from people and firms living Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 5 Chicago generally is an exception to the rule that wealthier residents prefer to live outside city centers. and working together in a city (referred to by economists as agglomeration economies) suggest that even small-scale policy interventions could have outsized effects and potentially improve the welfare of many individuals living in a city, not just the original target group. But as this essay will show, policymakers must carefully consider which interventions will best assist the households they wish to help. The mixed record of any one type of urban revitalization policy suggests that a combination of “place-based” policies (which direct resources to help certain low-income areas) and “people-based” policies (which provide assistance to people regardless of where they live) may be more successful. This essay reviews evidence of the effectiveness of each approach. Why Do Cities Exist? In order to examine the effects of different urban policies, it is useful to first understand the benefits that cities provide. Cities arise because there are advantages to concentrating economic activity in one place, known as agglomeration economies. When businesses in the same industry cluster together, they can share inputs, such as tires for cars. The more carmakers that cluster in a region, the more demand they’ll generate for tires in that region, making it more attractive for tire makers to locate in the city as well. That agglomeration reduces costs for all the carmakers. Clustered firms in the same industry also can share a common pool of skilled labor. For example, the high concentration of tech companies in Silicon Valley attracts a lot of software engineers. This is particularly advantageous in the case of industries where any individual firm may experience sudden changes in demand. Workers can transition from shrinking firms to growing ones as demand fluctuates. Finally, firms may benefit from knowledge spillovers. Discovery of new ideas is facilitated by more people living and working in close proximity, and new ideas spread from firms through shared labor pools and supply chains.6 6 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT The preceding examples describe localization economies—benefits that accrue from clusters of firms in the same industry. But agglomeration benefits also arise from concentrations of different industries. A variety of firms can take advantage of general inputs such as transportation networks or banking and legal services. Many firms employ workers with similar skills, even if they are not in the same industry, and cities provide access to a larger pool of skilled labor. Firms also enjoy knowledge spillovers from businesses in different fields or from other institutions such as universities. These benefits that arise as a result of a diverse city are known as urbanization economies. Cities also provide a variety of production and consumption benefits to individuals who live there. One striking observation is that all else being equal, it appears that worker productivity and average wages are higher in more densely populated areas.7 Economists think these gains come from the fact that the larger the city, the more opportunities workers have to interact with other skilled workers and gain valuable experience that they carry with them throughout their careers.8 Concentrations of people also make a variety of amenities, such as restaurants or theaters, commercially viable. Of course, there are limits and costs to urbanization. Higher population densities come with higher cost of land (rents) as well as more congestion and crime. At some point, these costs will discourage further development. At the most basic level, households face a trade-off between land and transportation costs. What Do Cities Look Like? Agglomeration economies also affect where firms and households locate within a city. When there are benefits from locating close to each other, a variety of different spatial configurations can arise. In other words, agglomeration economies can lead to “multiple equilibria.” This provides insight into why we observe the variety of outcomes across cities that we do. For instance, suppose that firms must decide where to set up their facilities in a context in which they benefit from interacting with each other. These benefits, however, decline with distance. This leads to a city with a central business district (CBD) surrounded by a residential area. Simultaneously, some workers may either decide to live close to work, making the CBD a mixed-use commercial/residential area, or live in the suburbs in an entirely residential area. At the most basic level, households face a trade-off between land and transportation costs. Living and working in the CBD lowers commuting costs, but at the same time, housing will be more expensive if many people want to live there. Some households might choose to reside in locations that are more distant from the CBD if they are compensated by lower housing prices. In addition to the value of land, housing prices also reflect factors such as the quality of schools, access to parks, crime rates, and levels of environmental quality that make some locations within the city more or less attractive than others. For example, studies show that people are willing to pay more to live in neighborhoods with good schools. Housing prices rise approximately 1 percent to 2 percent when test scores, used to measure school quality, increase by 5 percent. In dollar terms, this amounts to an increase of roughly $4,000 on average.9 In most U.S. cities, wealthier households tend to live farther away from the city center, though there are a few notable exceptions (such as Chicago, Philadelphia, and Washington, D.C.).10 One explanation for this is that wealthier households prefer to occupy more land and Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 7 The main underlying assumption in these models is that residents can move freely within cities, but they balance the various trade-offs in such a way that leaves residents indifferent to moving. 8 therefore are willing to live in the suburbs despite higher commuting costs because the price of housing per square foot is lower. On the other hand, when a household’s income becomes sufficiently large, it may choose to move back to the city center to reduce time spent commuting. This type of trade-off could explain, for instance, why both very poor and very wealthy households are found living in some downtowns. Cities such as Boston, New Orleans, Atlanta, and Philadelphia are examples of this type of spatial pattern. Additionally, public transportation can help explain why poorer households live in the city center. Although the cost of housing per unit of land is higher in the city, public transportation allows poor households that don’t have access to cars to economize on transportation costs.11 Transportation may further explain the trend of households moving from city centers to the suburbs, often called suburbanization. Several studies suggest that the development of the highway system contributes to “urban sprawl.”12 One study estimated that just one highway passing through a central city reduces its population by 18 percent.13 Cities that experience such a decline in commuting costs do still tend to attract population, but that inflow typically causes the city to expand geographically more than it increases the number of people living in the city center. Certain amenities, such as schools, also may explain neighborhood sorting by income or race. For instance, as wealthier households move to the suburbs, the quality of schools and other public services provided there will tend to rise. As this process unfolds, lower-income households are left behind in the city center with limited access to high-quality local public services. This has been observed in the suburbanization that has taken place in many large U.S. cities starting around the mid-twentieth century. A prominent recent study, for example, relies on the school desegregation experience to examine how a change in the public school system affected the school choice and localization decisions of residents. The study finds that school desegregation led to a decline in white enrollment in central city public schools in the South, and this decline was linked to white suburban migration. In non-Southern districts, the response was an increase in white private school enrollment.14 These kinds of forces tend to exacerbate the initial income stratification across locations and help explain why neighborhood differences tend to persist. To study these various trade-offs, economists rely on models (known in the field as spatial equilibrium models) to examine the economic implications of how households (and/or firms) choose and move to their preferred locations. The main underlying assumption in these models is that residents can move freely within cities, but they balance the various trade-offs in such a way that leaves residents indifferent to moving. (See sidebar on page 9 and appendix on page 17.) Moving to a new neighborhood, for example, might provide the benefits of certain amenities at the cost of more expensive housing or longer commutes. Across cities, different characteristics will be reflected in local wages as well as housing costs. For example, evidence suggests that households would not only be willing to pay higher housing prices to live in more attractive cities, they also would accept lower wages.15 The basic spatial equilibrium model has implications for how cities might look in a setting where the underlying forces of technology and macroeconomic features are not changing over time. Cities are subject to all manner of dynamic forces, however, that can lead to shifts in urban populations and, in turn, city size and composition. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Willingness to pay for Commercial use Commercial/Residential District Border VA L U E O F L A N D Willingness to pay for Residential use City Border Willingness to pay for Agricultural use D I S TA N C E F R O M E M P L O Y M E N T C E N T E R Modeling Land Use T his diagram shows the land-price gradients for firms, households, and agriculture. The land-price gradients, also referred to as bid-rent functions, represent the maximum price economic agents are willing to pay at each location. Land prices, as indicated by the functions, are highest in the central business district (CBD), which, in this case, is the only employment center, and they decline as people and firms move farther away from the CBD. The negative slopes of the commercial/manufacturing and residential land-price gradients are a consequence of the trade-offs between transportation and land costs when people and firms are deciding where to locate their homes or production facilities. The shape of the residential landprice gradient can be explained as follows. Households commute to work in the CBD and decide where to reside. At a locational equilibrium, households should not have incentives to move. This means that households should obtain the same utility at all locations. Note, however, that residing at more distant locations entails higher commuting costs. For households to be willing to reside farther from the CBD, they will need to be compensated through lower land prices. Firms face similar trade-offs, so the same type of reasoning explains the downward slope of the firms’ land-price gradient. A more detailed explanation can be found in the appendix. The relative slopes of the land-price gradients determine where households and firms locate and how land is used at different locations. We can think about how land is allocated across different uses through a mechanism that works as follows. Suppose that (absentee) landlords own the land, and they rent it, at each location, to whoever offers the highest price through a bidding process. The diagram shows a case in which the firms’ land-price gradient is steeper than the residential-price gradient. As a result, firms outbid households at locations closer to the CBD, so land is allocated to commercial or manufacturing uses at those locations, and households outbid firms in the suburbs. The size of the city is determined in this case by the intersection of the residential land-price gradient and the horizontal line indicating agricultural land rent. An urban area arises when land rents for nonagricultural uses at locations closer to the CBD are higher than the agricultural rent. At the urban fringe, rents for nonagricultural and agricultural uses should be equalized. The observed market land rent is determined by the party with the highest willingness to pay at any location. Near the CBD this will be the commercial bid-rent curve until it intersects with the residential bid-rent curve. The residential curve will then determine the price of land until it intersects with the agricultural line. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 9 Sturdy row houses like these in Baltimore often become hotbeds of urban revitalization. 10 The Lifecycle of Cities Cities undergo long cycles of development and decay. When a city is new, buildings near the CBD are the most desirable and tend to be occupied by a mix of firms and wealthier households. But as those buildings age and deteriorate, those households may move to newer developments surrounding the city, leaving behind lower-income households. This process can repeat multiple times, pushing the city border outward as higher-income households retreat to the newest ring of development. Eventually, deteriorated buildings in the city center are redeveloped, once again attracting higher-income households back to the city and starting the cycle anew. This has taken place, for instance, in cities such as Chicago and Philadelphia.16 This process, however, has raised some controversies since transforming a neighborhood from low- to high-income may displace the low-income households who live there, a process called gentrification. In addition to the natural aging cycle, there may be other forces that contribute to gentrification as well. One view suggests that gentrification is more likely to be observed at locations that border richer neighborhoods.17 Richer neighborhoods attract more high-income households and expand into adjacent areas that are relatively poor. Another related view states that high-income neighborhoods are characterized by low crime rates that eventually attract additional richer households.18 Lower-income households are displaced by this sorting. Because buildings are durable goods, it can take a long time for a city to move through its lifecycle. When a city’s population is growing, it is profitable to construct new housing because demand and prices for housing are rising, and the city expands rapidly. But when the population declines, existing housing stock doesn’t simply disappear. It can take decades before it is profitable to refurbish or replace a building. The surplus of housing depresses house prices below the cost of construction, and the city stops growing.19 Moreover, falling rents may draw lower-skilled and lower-income households into the city, intensifying urban sorting by income. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Cities also experience shocks that alter their composition of firms or people. Cities such as San Francisco, Washington, D.C., New York, and Cleveland, for example, appear to have experienced fairly rapid changes in firm composition without population changes of the same magnitude. Discovery of a new technique by a firm in one city may shift the center of that industry, resulting in an exodus of firms from one city and an influx into another. This leads to sudden growth or decline across cities. Population loss can result as skilled workers follow firms to the new center of that industry.20 Alternatively, employment in a city could decline for other reasons, such as changes in technology leading to greater automation at major employers. This may lead to a city with a thriving CBD but a surrounding residential area that is too large for its current population, as seen in Detroit and other manufacturing cities in the Rust Belt.21 Urban policymakers faced with either a sudden shock or a steady decline have a natural inclination to revitalize their cities. But should they intervene? And if so, how? The powerful reinforcing effects of agglomeration economies mean that even small policy changes can trigger large transformations in a city over time. Justifying Policy Responses to Urban Decline It is not easy to directly test whether people are genuinely left indifferent about moving—by variation in the prices of housing and amenities—within an urban area, a key prediction of the spatial equilibrium model. (See the appendix on page 17 for more detail.) Nonetheless, there seems to be some evidence that supports this implication. Households do not migrate disproportionately to higher-wage cities, for example. Other factors, such as higher housing prices and/or city amenities and disamenities explain differences in wages across cities. Therefore, variations in wages and housing prices across cities should not be sufficient justification for policy intervention. For instance, high wages may be observed in relatively unattractive cities or in cities with higher housing prices.22 On the other hand, if households face hurdles to moving, policymakers may be able to help people who are “trapped” in declining areas. Often, higher-skilled and higher-income households can more easily move when a neighborhood declines, while poorer and lower-skilled individuals are left behind. Artificial barriers, such as zoning laws or minimum lot sizes, make the process of moving to thriving communities even more difficult. To the extent that such mismatch exists, policy actions to alleviate these frictions could be welfare-improving. The spatial equilibrium model also provides two justifications for the implementation of policies aimed at revitalizing a declining city.23 The powerful reinforcing effects of agglomeration economies mean that even small policy changes can trigger large transformations in a city over time, providing an argument in favor of promoting urban renewal. Higher concentrations of people and firms have, on balance, positive effects on almost everyone living in a city. Higher population density means greater learning and sharing of knowledge, more productive firms and workers, and more efficient supply lines and labor matching. Thus, investments may well “jumpstart” these forces in ways that could have benefits that clearly outweigh their costs. In theory, even an announcement that a neighborhood will be revitalized could by itself trigger a variety of positive effects before the government spends any money.24 As noted earlier, agglomeration effects create the possibility of multiple equilibria for a city when they are sufficiently strong. In this case, efforts to “push” a city from a low-employment/low-wage equilibrium (poverty trap) to a high-employment/high-wage equilibrium could be worthwhile. In practice, however, there is little evidence of cities that have moved Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 11 from one equilibrium to another. One study looked at the extreme case of the Japanese cities Hiroshima and Nagasaki, which were entirely destroyed in World War II. Following the war, they were rebuilt and returned to their long-run population trends, suggesting that a city’s spatial configuration is both unique and stable.25 If policymakers decide that some intervention is warranted, there are a number of different approaches they could consider. One option is to focus on helping households by giving them the tools to improve their situation. This could involve removing barriers that prevent households from relocating to thriving parts of the city, providing housing vouchers to help them move, or improving transportation networks to reduce commuting costs. An alternative approach is to focus on revitalizing the city itself. This includes revitalizing residential or commercial buildings that have declined or offering incentives to employers to locate in the city and hire local people. Economists have labeled these different approaches people-based and place-based policies, respectively. The ultimate goal of either approach is, presumably, to help people. One of the key responsibilities of policymakers is to consider how effective any given policy might be at achieving that goal. Additionally, the presence of agglomeration economies and social interactions tend to magnify the impact of policies in the context of cities and could, potentially, end up benefitting everyone in the city. However, it remains a challenge to precisely identify and implement those policies that fully exploit and take advantage of the external effects that characterize urban areas. Revitalizing Places Enterprise Zones (EZs) are one of the most widely used, and widely studied, forms of placebased policy intervention. EZs designate an area for assistance, typically in the form of tax credits for employers and grants for various development projects. EZs have been implemented on both the state and federal level. Connecticut established the first state-based EZ in 1982, and forty states had some type of EZ by 2008. On the federal level, Empowerment Zones (which are similar to EZs on the state level) were used from 1993 to around 2009.26 Under this program, local governments could apply to the Department of Housing and Urban Development for benefits similar to state EZs. Eleven cities were selected for Empowerment Zones in the first phase of the program, including Atlanta, Baltimore, Chicago, Detroit, New York, and Philadelphia.27 Investment in these various programs has been substantial, but measuring their impact has been challenging. One problem is that targeted areas often don’t align neatly with census tracts, zip codes, or other standard geographical boundaries used for collecting data. Another challenge is controlling for other factors that influence local economic conditions and finding appropriate control cases for comparison. A third difficulty is that any benefits attributed to place-based interventions may come at a cost to other regions. For example, persuading a business to relocate from one city to another city benefits the latter at the expense of the former. On the other hand, promoting development in one part of the city may spur private investment that benefits nearby, nontargeted areas. Any empirical study looking to measure the benefits of these programs must account for these positive and negative spillover effects. 12 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Given these challenges, it is not surprising that studies have found mixed effects from state and federal EZ programs. At the state level, an examination of California’s EZ program found no evidence of a significant impact on employment, while an analysis of Texas’ EZs found a positive effect, particularly in lower-paying industries.28 At the federal level, some studies have found positive, statistically significant effects on employment and wages from Empowerment Zones.29 On the other hand, there is also evidence of significantly negative spillover effects on areas geographically near or economically similar to Empowerment Zones, suggesting that at least some of the “gains” may simply be shifted economic activity.30 Whether these programs actually help their intended recipients is also controversial. While more recent EZs require employers to hire locally in order to receive the benefits, not all programs have had this stipulation, meaning some benefits may have accrued to workers who moved to the city with firms rather than to the original target group of individuals. Other place-based urban policies focus on improving residential buildings and infrastructure. Examples of federal urban renewal programs include the Housing Act of 1949, which provided loans to cities to acquire and redevelop decaying neighborhoods, and the Model Cities Program of the 1960s, which focused more on renewal of neighborhoods rather than wholesale reconstruction. Evidence on the impact of these projects is also inconclusive. Studies don’t suggest they had a meaningful impact on population growth or per-capita income, but that may be due to their relatively limited funding.31 Urban renewal projects do seem to generate higher land values—even in nearby neighborhoods not directly targeted, as found in one study of the Neighborhoods-in-Bloom program in Richmond, Virginia.32 One of the main findings of that study is that after accounting for all the external effects generated by this kind of program, the overall benefits may more than compensate for the costs of implementation. To the extent that urban renewal programs generate higher land values, many of the benefits may accrue to landowners rather than low-income households if those households are mostly renters. For example, one study of the federal Empowerment Zone program found that it had no effect on poverty and employment for residents but a large effect on property prices.33 Successful urban renewal projects also may end up displacing those households if neighborhoods become more desirable because of new construction.34 An influx of Hiroshima was totally destroyed during World War II, but the city eventually returned to its long-run population trend. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 13 Photo: ©SuperStock/GettyImages When Pittsburgh’s population peaked in the 1950s, the city’s economy relied heavily on the steel industry. higher-income households may bid up rents and price out the low-income households the renewal projects were intended to help, raising the problem of gentrification described earlier. Policymakers undertaking a place-based approach to reversing urban decline should be mindful of unintended consequences such as these. Investing in People Another criticism of urban renewal programs is that they encourage households to remain in neighborhoods with few opportunities. That is, those areas may have declined for a reason, and applying a fresh coat of paint may merely address the symptoms and not the causes of decline. Enticing firms to locate in those areas through EZs could solve this problem, but only if the firms seek the skills possessed by households living in those areas. In light of these challenges, some economists have said that the best thing households in declining city neighborhoods can do is move.35 And to the extent that they are constrained from leaving, policymakers should help them. In the mid-1990s, the U.S. Department of Housing and Urban Development took this approach with the Moving to Opportunity (MTO) program. The program was implemented in five cities—Baltimore, Boston, Chicago, Los Angeles, and New York—and it provided housing vouchers to families living in high-poverty neighborhoods to help them move to low-poverty neighborhoods. While participation in the program was voluntary, the eligible applicants who received vouchers were chosen randomly, making it a good case study of this policy approach. Some households (the treatment group) received housing vouchers that could only be used in census tracts with poverty rates below 10 percent; others received vouchers with no geographical restrictions (Section 8 vouchers); and the control group received no vouchers and continued receiving public assistance. Some research has found that for adults, the program seems to have had no lasting effect on earnings or economic self-sufficiency.36 Moreover, of the households offered a voucher to move to lower-poverty neighborhoods, less than half accepted, and some that did move later moved back.37 For children, the evidence was mixed. Those who were younger than thirteen when they moved to a lower-poverty area seemed to benefit. Compared with children in the control group, they had incomes that were about $3,500 (31 percent) higher on average in their mid-twenties, were more likely to attend college, and were less likely to become single parents. On the other hand, children who were older than thirteen when they moved suffered 14 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT worse long-term outcomes, possibly because they already had established social networks in their old neighborhoods and disrupting those networks caused more harm than good.38 More recent research examines the outcomes of similar housing voucher programs and finds more favorable evidence of this type of policy. From a research standpoint, the fact that participation in the MTO experiment and the use of the housing vouchers was voluntary introduces potential self-selection bias into the results of the experiment. Some children could have benefitted from the relocation to a low-poverty neighborhood, but since their parents were not really motivated to move, they did not participate in the program. To overcome this problem, one study focuses on the outcomes of a specific program that involved the mandatory relocation of households to other neighborhoods because the public housing where they lived was set to be demolished. This study concludes that the relocation of households had large and positive effects on children (they were more likely to be employed and earned higher wages as adults), and these effects were substantially larger than those found in the MTO experiment. This suggests that the children who did not participate in the MTO experiment were very likely those who could have benefitted most.39 Rather than attempting to move residents to more prosperous areas, another people-based approach is to improve residents’ human capital. Certainly investing in education and human capital has benefits on the individual level. But having a more educated, more productive urban population has positive spillover effects on the city as a whole, too. In fact, these spillover effects seem to play a key role in defining modern successful cities. In the early postWorld War II era, city growth was tied to high concentrations of physical capital, like the car factories of Detroit or the steel mills of Pittsburgh. But since 1980, human capital has become a more reliable indicator of a thriving city. Average wages in cities with highly educated populations, such as Boston or San Francisco, are much higher for both college graduates and high school graduates than in cities with low levels of college education.40 There may be other spillover benefits as well. Individuals with more education are significantly less likely to commit crimes. Thus, increasing high school graduation rates (for men in particular) seems to provide substantial social savings to cities in the form of less crime.41 Raising human capital levels and the number of high-skilled jobs in a city also has a multiplier effect. One study found that for each new job in an innovative field added to a city, five additional jobs were created. This includes high-skilled jobs (such as lawyers, teachers, or nurses), and low-skilled jobs (such as waiters, baristas, or taxi drivers).42 Pittsburgh’s population is less than half what it was in the 1950s, but the city’s economy is more diversified. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 15 When considering policies, something to bear in mind is that cities are far from identical. The forces that gave rise to the movie industry in Los Angeles or the auto industry in Detroit may not be replicable elsewhere. Santiago Pinto is a senior policy economist and Tim Sablik is an economics writer in the Research Department at the Federal Reserve Bank of Richmond. The authors would like to thank Kartik Athreya, Thomas Lubik, Ray Owens, Pierre Sarte, and John Weinberg for many helpful comments. The views expressed are those of the authors and not necessarily those of the Federal Reserve Bank of Richmond or the Federal Reserve System. 16 Cities with higher levels of human capital also tend to attract more individuals with high levels of human capital, creating clusters of innovation, such as Silicon Valley near San Jose, California, or the Research Triangle in Raleigh, Durham, and Chapel Hill, North Carolina. What is less certain is whether policymakers in declining cities can create new innovation clusters by investing in universities or other research institutions. Empirical evidence does suggest that such investments could have lasting benefits for a city, but many of these studies examine the effects of well-established universities on a region. It is more difficult to say for certain what leads individuals and firms to cluster around certain institutions and not others, and it is unclear whether attempting to create such clusters out of whole cloth would succeed. Moreover, the greater mobility of highly educated individuals may reduce the ability of cities to fully benefit from investments in human capital. If a declining city doesn’t already have an innovative sector to employ newly trained individuals, those individuals may choose to leave the city after completing their education, taking their human capital with them. Taking a Balanced Approach The urban economics literature has much to say about our increasingly urban world. First, agglomeration economies are powerful forces that have led to the dramatic urbanization of the world’s population over the past two centuries. These forces have fed into each other to generate remarkable economic growth. These feedback effects mean that even small changes to a city can have a large impact over the long run. Urban policies are often thought of in terms of large-scale projects: building a new sports complex or business center, redeveloping whole neighborhoods, or adding new public transportation infrastructure. But spatial models of cities suggest that small-scale projects could be just as effective at promoting city growth. Creating growth through new industries may be more challenging than maintaining or restoring existing industries in a city, however. Because agglomeration economies arise organically, it is hard to say what incentives could attract new firms to locations they previously avoided.43 When considering policies, something to bear in mind is that cities are far from identical. The forces that gave rise to the movie industry in Los Angeles or the auto industry in Detroit may not be replicable elsewhere. Moreover, policies that generated a positive response in one city have no guarantee of doing the same in another. As the studies we’ve highlighted illustrate, policies implemented in the complex social environment of cities may trigger all sorts of unanticipated responses. This doesn’t mean that policymakers can draw no lessons from experiments in other cities, but the key lesson is to proceed with caution. Finally, while this essay has presented examples of both place-based and people-based policies, these should not be viewed as mutually exclusive. There may be practical limitations to how far city leaders can take any one approach. Emptying out a declining neighborhood may seem like the efficient choice in an economic model, for example, but it may not be a realistic solution. Rather, policymakers should consider a mix of responses that would be most appropriate for their respective cities. Two main questions should guide their choices. First, are there policies that, in the spatial context of cities, can potentially improve the well-being of nearly all residents? Second, to what extent do more targeted policies help their intended recipients? n Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Appendix Basic Urban Equilibrium Location decisions of households Consider a linear city, where identical households commute between their place of residence and place of work, the central business district (CBD). The distance from a household’s residence to the CBD is x (the CBD is located at x = 0). The commuting cost per mile is denoted by t, which is assumed constant. Total commuting costs for a consumer residing x miles from the CBD is tx. All households earn an exogenously given income w. Disposable income for a household residing x miles from the CBD is w − tx. Households consume two goods: a nonhousing good, c, and land, denoted by ℓ. The simplest version of the model assumes that residential consumption of land is given and equal to one, i.e., ℓ = 1, and preferences are represented by the utility function v(c) = c. The price of the nonhousing good is equal at all locations and it is normalized to one, and land can be rented at r per acre. The budget constraint is w − tx = c + r. Households are perfectly mobile within an urban area (and across cities), which implies that at a locational equilibrium all households achieve the same level of utility. In our model, this means that u = w − tx − r for all x, where u is taken as exogenous. From this equilibrium condition, we obtain the land-price gradient r ––– r (x, w, t, u) = w − tx − u. The latter is usually referred to as the residential land bid-rent function since it actually represents the maximum rent a household is willing to pay for land at different locations. Note that r/ x = − t < 0, so that residential land rent declines as distance from the CBD increases. This condition has the following interpretation. As households move one mile farther away from the CBD, commuting costs increase by t. Households would be indifferent between staying and moving to the new location if the decline in land rents fully compensate for the additional commuting costs. Location decisions of firms Suppose that each firm produces a fixed amount of output q employing one unit of land and structure capital. The price of q, denoted by p, and the cost of structure, denoted by C, are given and do not change with x. Firms pay the land rent rf , where rf changes with x. Firms can set up their production facilities at any location x. However, the products have to be taken to the port or transportation terminal, located at the CBD, for delivery to their final destinations. The cost of transporting one unit of q per mile is tq , so total transportation costs to the CBD for a firm located at x are tq x q. Profits at location x are consequently given by p = pq − C − tq xq − rf. Perfect competition and free entry of firms drive profits to 22 zero at every location, so the highest land rent a firm is willing to pay at x is: rf ––– rf (x, p, q, C, tq ) = (p − tq x)q − C. The slope of the firm’s bid rent is r f / x = − tq q. s of the bid rent functions, as a result, determine Land use and equilibrium city size how the land is used. The diagram shows a tion in which Land the slope of the firms’ function isor steeper than the slope of the can be rented to bid-rent firms, households, used for agriculture. Agricultural land rent is given by rA . ential bid-rent function,landlords i.e., 𝑡𝑡𝑡𝑡𝑞𝑞𝑞𝑞 /𝑞𝑞𝑞𝑞rent > 𝑡𝑡𝑡𝑡.the Theland twotocurves intersect at to 𝑥𝑥𝑥𝑥 = 𝑥𝑥𝑥𝑥�, which defines theThe relative slopes of the Absentee whoever is willing offer the highest bid. bid-rent functions, as The a result, land is is used. The on page 9 shows a situation er between firms and households. size determine of the city how in thethe diagram given bydiagram the distance in which the slope of the firms’ bid-rent function is steeper than the slope ofofthe residential bid-rent the CBD to the city-rural boundary, denoted by 𝑥𝑥𝑥𝑥̅ , and it is determined by the intersection function, i.e., tq / q > t. The two curves intersect at x = ˜x, which defines the border between firms and esidential bid-rent function and the horizontal line 𝑟𝑟𝑟𝑟𝐴𝐴𝐴𝐴 . As a result, all locations 𝑥𝑥𝑥𝑥 < 𝑥𝑥𝑥𝑥� are households. The size of the city in the diagram is given by the distance from the CBD to the city-rural pied by firms, all locations 𝑥𝑥𝑥𝑥� ≤ 𝑥𝑥𝑥𝑥 ≤ 𝑥𝑥𝑥𝑥̅- are occupied by households, and all locations 𝑥𝑥𝑥𝑥 > 𝑥𝑥𝑥𝑥̅ are boundary, denoted by x, and it is determined by the intersection of the residential bid-rent function ted to the agricultural use. Specifically, sizeall 𝑥𝑥𝑥𝑥̅ is determined by occupied by firms, all locations ˜x ≤ x ≤ x- are and the horizontal line rA . the As acity result, locations x < ˜x are occupied by households, and all locations x > x are devoted to the agricultural use. Specifically, the city (1) 𝑟𝑟𝑟𝑟(𝑥𝑥𝑥𝑥̅ , 𝑤𝑤𝑤𝑤, 𝑡𝑡𝑡𝑡, 𝑢𝑢𝑢𝑢) = 𝑟𝑟𝑟𝑟 . size x- is determined𝐴𝐴𝐴𝐴 by r (x , w, t, u) = rA (1) in equilibrium, the city’s population, 𝑁𝑁𝑁𝑁, should fit in the city, or Also, in equilibrium, the city’s population, N, should fit in the city, or 𝑥𝑥𝑥𝑥̅ (2) 𝑥𝑥𝑥𝑥𝑥𝑥𝑥𝑥𝑥𝑥𝑥𝑥==N. (2) 𝑁𝑁𝑁𝑁. ∫0 xdx Consider a situation in which there is costless migration across citiesto(usually der a situation in which there is costless migration across cities (usually referred as an referred to as an “open city model”), so the city’s population adjusts until utility is equalized at utility level u. n city model”), so the city’s population adjusts until utility is equalized everywhere at everywhere utility - , it follows that x- = (w − u − r )/ t, and from (2), N = x- 2 / 2. Substituting r (x, w, t, u) into (1) and solving for x A 𝑢𝑢𝑢𝑢. Substituting 𝑟𝑟𝑟𝑟(𝑥𝑥𝑥𝑥, 𝑤𝑤𝑤𝑤, 𝑡𝑡𝑡𝑡, 𝑢𝑢𝑢𝑢) into (1) and solving for 𝑥𝑥𝑥𝑥̅ , it -follows that 𝑥𝑥𝑥𝑥̅ = (𝑤𝑤𝑤𝑤 − 𝑢𝑢𝑢𝑢 − 𝑟𝑟𝑟𝑟𝐴𝐴𝐴𝐴 )/𝑡𝑡𝑡𝑡, and 2 Note that since r (x, w, t, u) − r (x , w, t, u) = t (x − x ), then the equilibrium residential bid-rent function is (2), 𝑁𝑁𝑁𝑁 = 𝑥𝑥𝑥𝑥̅ /2. Note that since 𝑟𝑟𝑟𝑟(𝑥𝑥𝑥𝑥, -𝑤𝑤𝑤𝑤, 𝑡𝑡𝑡𝑡, 𝑢𝑢𝑢𝑢) − 𝑟𝑟𝑟𝑟(𝑥𝑥𝑥𝑥̅ , 𝑤𝑤𝑤𝑤, 𝑡𝑡𝑡𝑡, 𝑢𝑢𝑢𝑢) = 𝑡𝑡𝑡𝑡(𝑥𝑥𝑥𝑥̅ − 𝑥𝑥𝑥𝑥), then the equilibrium r (x, w, t, u) = rA + t (x − x). ential bid-rent function is rences 𝑟𝑟𝑟𝑟(𝑥𝑥𝑥𝑥, 𝑤𝑤𝑤𝑤, 𝑡𝑡𝑡𝑡, 𝑢𝑢𝑢𝑢) = 𝑟𝑟𝑟𝑟𝐴𝐴𝐴𝐴 + 𝑡𝑡𝑡𝑡(𝑥𝑥𝑥𝑥̅ − 𝑥𝑥𝑥𝑥). Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 17 Endnotes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 18 Cohen (2015). Manyika et al. (2012). Moretti (2013, 84) and Jaffe, Trajtenberg, and Henderson (1993). More recently in the 2000s, following the nationwide trend, poverty has been rising in the suburbs as well. However, poverty is still a phenomenon highly concentrated in inner cities. The Census Bureau began tracking “standard metropolitan areas” with the 1950 census. These were later renamed MSAs, and the boundaries have changed over the decades following the fluctuations of cities. Data are from the1950 Census and 2010 Census. Audretsch and Feldman (1996) and Glaeser (1999). Moretti (2004). De la Roca and Puga (2016). Black (1999) and Bayer, Ferreira, and McMillan (2007). Rosenthal and Ross (2015). Glaeser, Kahn, and Rappaport (2008). Baum-Snow (2007) and Duranton and Turner (2012). Baum-Snow (2007). Baum-Snow and Lutz (2011). Other studies focus on different explanations for “white flight,” such as rising crime rates in cities. See Cullen and Levitt (1999) and Boustan (2010). Glaeser, Kolko, Saiz (2001) and Albouy (2012). Brueckner and Rosenthal (2009). Guerrieri, Hartley, and Hurst (2013). O’Sullivan (2005). Glaeser and Gyourko (2005). Duranton (2007). Owens, Rossi-Hansberg, and Sarte (2017). Glaeser and Gottlieb (2008). Kline and Moretti (2013). Kline and Moretti (2014). Davis and Weinstein (2002). The Empowerment Zone designation generally expired in 2009, though it was subject to a series of case-by-case extensions. Ham, Swenson, Imrohoroğlu, and Song (2011). Neumark and Kolko (2010) and Freedman (2013). Busso, Gregory, and Kline (2013) and Ham, Swenson, Imrohoroğlu, and Song (2011). Hanson and Rohlin (2013). Glaeser and Gottlieb (2008). Rossi-Hansberg, Sarte, and Owens (2010). Hanson (2009). Several studies show that EZ programs could have generated demographic changes in affected neighborhoods. Neumark and Simpson (2015). Glaeser (2005). Kling, Liebman, and Katz (2007). Clampet-Lundquist and Massey (2008). Chetty, Hendren, and Katz (2016). Chyn (2016). Moretti (2004) and Moretti (2011). Lochner and Moretti (2004). Moretti (2013). Rosenthal and Strange (2006). Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Albouy, David. May 2012. “Are Big Cities Bad Places to Live? Estimating Quality of Life across Metropolitan Areas.” Working paper. References Audretsch, David B., and Maryann P. Feldman. June 1996. “R&D Spillovers and the Geography of Innovation and Production.” American Economic Review 86 (3): 630–640. Baum-Snow, Nathaniel. May 2007. “Did Highways Cause Suburbanization?” Quarterly Journal of Economics 122 (2): 775–805. Baum-Snow, Nathaniel, and Byron F. Lutz. December 2011. “School Desegregation, School Choice, and Changes in Residential Location Patterns by Race.” American Economic Review 101 (7): 3019–3046. Bayer, Patrick, Fernando Ferreira, and Robert McMillan. August 2007. “A Unified Framework for Measuring Preferences for Schools and Neighborhoods.” Journal of Political Economy 115 (4): 588–638. Black, Sandra E. May 1999. “Do Better Schools Matter? Parental Valuation of Elementary Education.” Quarterly Journal of Economics 114 (2): 577–599. Boustan, Leah Platt. February 2010. “Was Postwar Suburbanization ‘White Flight?’ Evidence from the Black Migration.” Quarterly Journal of Economics 125 (1): 417–443. Brueckner, Jan K., and Stuart S. Rosenthal. November 2009. “Gentrification and Neighborhood Housing Cycles: Will America’s Future Downtowns Be Rich?” Review of Economics and Statistics, 91 (4): 725–743. Busso, Matias, Jesse Gregory, and Patrick Kline. April 2013. “Assessing the Incidence and Efficiency of a Prominent Place Based Policy.” American Economic Review 103 (2): 897–947. Chetty, Raj, Nathaniel Hendren, and Lawrence F. Katz. April 2016. “The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment.” American Economic Review 106 (4): 855–902. Chyn, Erik. October 2016. “Moved to Opportunity: The Long-Run Effect of Public Housing Demolition on Labor Market Outcomes of Children.” Working paper. Clampet-Lundquist, Susan, and Douglas S. Massey. July 2008. “Neighborhood Effects on Economic Self-Sufficiency: A Reconsideration of the Moving to Opportunities Experiment.” American Journal of Sociology 114 (1): 107–143. Cohen, Darryl T. March 2015. “Population Trends in Incorporated Places: 2000 to 2013.” United States Census Bureau Report P25–1142. Cullen, Julie Berry, and Steven D. Levitt. May 1999. “Crime, Urban Flight, and the Consequences for Cities.” Review of Economics and Statistics 81 (2): 159–169. Davis, Donald R., and David E. Weinstein. December 2002. “Bones, Bombs, and Break Points: The Geography of Economic Activity.” American Economic Review 92 (5): 1269–1289. De la Roca, Jorge, and Diego Puga. July 2016. “Learning by Working in Big Cities.” Review of Economic Studies 84 (1): 106–142. Duranton, Gilles. March 2007. “Urban Evolutions: The Fast, the Slow, and the Still.” American Economic Review 97 (1): 197–221. Duranton, Gilles, and Matthew A. Turner. October 2012. “Urban Growth and Transportation.” Review of Economic Studies 79 (4): 1407–1440. Freedman, Matthew. Spring 2013. “Targeted Business Incentives and Local Labor Markets.” Journal of Human Resources 48 (2): 311–344. Glaeser, Edward L. September 1999. “Learning in Cities.” Journal of Urban Economics 46 (2): 254-277. Glaeser, Edward L. September 2005. “Should the Government Rebuild New Orleans, Or Just Give Residents Checks?” The Economists’ Voice 2 (4): 1–6. Glaeser, Edward L., and Joshua D. Gottlieb. October 2008. “The Economics of Place-Making Policies.” National Bureau of Economic Research Working Paper No. 14373. Glaeser, Edward L., and Joseph Gyourko. April 2005. “Urban Decline and Durable Housing.” Journal of Political Economy 113 (2): 345–375. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 19 References Glaeser, Edward L., Matthew E. Kahn, and Jordan Rappaport. January 2008. “Why Do the Poor Live in Cities? The Role of Public Transportation.” Journal of Urban Economics 63 (1): 1–24. Glaeser, Edward L., Jed Kolko, and Albert Saiz. January 2001. “Consumer City.” Journal of Economic Geography 1 (1): 27-50. Guerrieri, Veronica, Daniel Hartley, and Erik Hurst. April 2013. “Endogenous Gentrification and Housing Price Dynamics.” Journal of Public Economics 100: 45–60. Ham, John C., Charles Swenson, Ayşe Imrohoroğlu, and Heonjae Song. August 2011. “Government Programs Can Improve Local Labor Markets: Evidence from State Enterprise Zones, Federal Empowerment Zones and Federal Enterprise Community.” Journal of Public Economics 95 (7-8): 779–797. Hanson, Andrew. November 2009. “Local Employment, Poverty, and Property Value Effects of Geographically-Targeted Tax Incentives: An Instrumental Variables Approach.” Regional Science and Urban Economics 39 (6): 721–731. Hanson, Andrew, and Shawn Rohlin. January 2013. “Do Spatially Targeted Redevelopment Programs Spillover?” Regional Science and Urban Economics 43 (1): 86–100. Jaffe, Adam B., Manuel Trajtenberg, and Rebecca Henderson. August 1993. “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations.” Quarterly Journal of Economics 108 (3): 577–598. Kline, Patrick, and Enrico Moretti. November 2013. “Local Economic Development, Agglomeration Economies, and the Big Push: 100 Years of Evidence from the Tennessee Valley Authority.” Quarterly Journal of Economics 129 (1): 275–331. Kline, Patrick, and Enrico Moretti. August 2014. “People, Places, and Public Policy: Some Simple Welfare Economics of Local Economic Development Programs.” Annual Review of Economics 6: 629–662. Kling, Jeffrey R., Jeffrey B. Liebman, and Lawrence F. Katz. January 2007. “Experimental Analysis of Neighborhood Effects.” Econometrica 75 (1): 83–119. Lochner, Lance, and Enrico Moretti. March 2004. “The Effect of Education on Crime: Evidence from Prison Inmates, Arrests, and Self-Reports.” American Economic Review 94 (1): 155–189. Manyika, James, Jaana Remes, Richard Dobbs, Javier Orellana, and Fabian Schaer. April 2012. “Urban America: U.S. Cities in the Global Economy.” McKinsey Global Institute. Moretti, Enrico. 2004. “Human Capital Externalities in Cities.” Handbook of Regional and Urban Economics 4: 2243–2291. Moretti, Enrico. 2011. “Local Labor Markets.” Handbook of Labor Economics 4b: 1237–1313. Moretti, Enrico. 2013. The New Geography of Jobs. New York: Mariner Books. Neumark, David, and Jed Kolko. July 2010. “Do Enterprise Zones Create Jobs? Evidence from California’s Enterprise Zone Program.” Journal of Urban Economics 68 (1): 1–19. Neumark, David, and Helen Simpson. 2015. “Place-Based Policies.” Handbook of Regional and Urban Economics 5: 1197–1287. O’Sullivan, Arthur. January 2005. “Gentrification and Crime.” Journal of Urban Economics 57 (1): 73–85. Owens, Raymond III, Esteban Rossi-Hansberg, and Pierre-Daniel Sarte. February 2017. “Rethinking Detroit.” National Bureau of Economic Research Working Paper No. 23146. Rosenthal, Stuart S., and Stephen L. Ross. 2015. “Change and Persistence in the Economic Status of Neighborhoods and Cities.” Handbook of Regional and Urban Economics 5: 1047–1120. Rosenthal, Stuart S., and William C. Strange. 2006. “The Micro-Empirics of Agglomeration Economies.” In A Companion to Urban Economics, edited by Richard J. Arnott and Daniel P. McMillen, 7–23. Oxford, U.K.: Blackwell Publishing. Rossi-Hansberg, Esteban, Pierre-Daniel Sarte, and Raymond Owens III. June 2010. “Housing Externalities.” Journal of Political Economy 118 (3): 485–535. 20 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Fifth District Economic Report Fifth District Economy Grew Moderately in 2016 A necdotal reports and economic data on the Fifth District in 2016 revealed a regional economy that continued to grow throughout the year. Employment rose at a moderate pace with more robust growth coming out of North and South Carolina. Wage growth picked up overall, including, according to survey responses, for starting wages across a number of firms. Services firms generally reported expanding activity throughout the year, while manufacturers began the year with less universally positive reports and ended the year on somewhat more optimistic notes. Real estate markets continued to improve in 2016, but real estate professionals expressed concerns about regulatory delays and shortages of buildable lots and skilled workers. Labor Markets Labor market conditions continued to tighten over the course of 2016. Payroll employment expanded 1.4 percent in the Fifth District as employers added 201,800 jobs. Among the District’s jurisdictions, North Carolina posted the largest year-over-year growth rate in December of 2.2 percent, followed by South Carolina’s 1.8 percent. The Carolinas were the only Fifth District states to outpace the national rate of 1.6 percent. Employment grew in the other Fifth District jurisdictions except for West Virginia, which reported a 0.8 percent decline as the state continued to shed a large number of jobs from the mining and logging sector. In the District overall, the most jobs were added over the year to education and health services (50,200 jobs) followed by trade, transportation, and utilities (35,200 jobs) and professional and business services (35,000 jobs). The only industry to lose jobs in 2016 was information, which shed 3,100 jobs (1.3 percent); information is the smallest industry in the District, and employment in the sector has generally been declining or flat since 2000. Although construction employment grew in 2016 and has been growing at a healthy pace since the beginning of 2014, total employment in the industry has not returned to its prerecession level. (See Figure 1.) In fact, in December 2016 the industry’s total employment stood at 82.9 percent of the level reported in December 2007. Similarly, employment in the manufacturing and information industries has yet to return to prerecession levels. Employment in mining and logging had returned to its prerecession levels, but the industry began to shed jobs again in 2012 and is now below its December 2007 level. Employment in mining has fluctuated, primarily in response to changes in coal and natural gas markets. Despite the fact that a few industries still have not returned to their prerecession employment levels, employment in the District as a whole exceeded its December 2007 level in April 2014 and has remained above that level. Moreover, the professional and business services and leisure and hospitality industries surpassed their prerecession marks in 2011. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 21 Figure 1: Fifth District Employment Growth by Industry INDEX, DECEMBER 2007=100 120 Educational and Health Services 110 Leisure and Hospitality Professional and Business Services 100 TOTAL 90 Manufacturing 80 Information Construction 70 Mining and Logging 60 2008 2009 2010 2011 2012 2013 2014 2015 2016 YEARS SOURCE: Current Employment Statistics, Bureau of Labor Statistics NOTES: Gray area indicates the recession. Industries that closely followed the overall growth trend (the total line) have been omitted from this graph. They are: trade, transportation, and utilities; financial activities; and other services. Government employment growth also is not shown because it was essentially flat. The education and health services industry and the government sector were the only two categories that did not decline either during or since the recession. In 2016, the unemployment rate in the Fifth District declined from 5.0 percent in December 2015 to 4.6 percent in December 2016—the lowest rate since early 2008. All of that 2016 improvement came in the first half of the year; the rate has remained at 4.6 percent since June. At the jurisdiction level, unemployment rates declined in every state except Virginia, where the 4.1 percent rate in December 2016 matched the rate reported in December of the previous year. The greatest improvement came from South Carolina, where the jobless rate declined 1.2 percentage points to end the year at 4.3 percent. Anecdotes throughout the Fifth District indicated that as the labor market tightened in 2016, employers increasingly struggled to find skilled workers. In particular, there were consistent reports of challenges in finding tradespeople, construction workers, hospitality workers, and IT professionals. Anecdotal information also suggested that the tight labor market might be driving up starting wages. According to a survey of employers conducted by the Federal Reserve Bank of Richmond in May 2016, more than 60 percent of respondents said they were raising starting wages. The same result was obtained when the survey was repeated in November 2016. Furthermore, the number of respondents who reported raising wages for “most” jobs (as opposed to “some” jobs) was higher in November than in May. Data on wage growth from the Bureau of Labor Statistics supported the anecdotal information. Using the most recent data from the bureau’s Quarterly Census of Employment and Wages, Figure 2 shows that Fifth District wage growth over the trailing four quarters through September 2016 slightly outpaced the same four quarters of the prior year. Additionally, 22 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Figure 2: Year-over-Year Wage Growth in the Fifth District Total Construction Manufacturing Trade, Transportation, and Utilities Information Financial Activities Professional and Business Services Education and Health Services Leisure and Hospitality Other Services Government 0 ■ 2016 ■ 2015 1 2 3 4 5 PERCENT INCREASE SOURCE: Richmond Fed calculations using Quarterly Census of Employment and Wages, Bureau of Labor Statistics NOTES: Calculations are based on nominal average wages from the trailing four quarters through September of 2014, 2015, and 2016. Calculations for manufacturing and construction do not include data from the District of Columbia. For the trailing four months ending September 30, 2016, the District of Columbia accounted for 2.1 percent of construction employment and 0.1 percent of manufacturing employment in the Fifth District. the largest wage growth came from the construction industry (4.9 percent), which was also the industry with the most persistent reports of difficulties in finding workers. Wage growth accelerated in most other industries as well, with the most notable deceleration coming in the financial services industry. Business Conditions Manufacturing activity was mixed in 2016, but it ended on a generally optimistic note. The Richmond Fed maintains a composite manufacturing index based on the Bank’s Fifth District Survey of Manufacturing Activity. It is a diffusion index, meaning that a positive reading indicates that the share of firms reporting expansion exceeds the share of firms reporting contraction. As was the case last year, the index fluctuated between positive and negative readings throughout the year; however, 2016 ended with two consecutive months of positive index values and included more comments expressing optimism. Throughout the year, anecdotal reports indicated that some segments of manufacturing were more consistently positive. For example, manufacturers of automobiles, auto parts, and aerospace products reported growth, fairly robust at times, for most of the year. Conversely, some manufacturers continued to be adversely affected by sluggish global demand and declining commodity prices. According to the Federal Reserve Bank of Richmond Service Sector Survey, conditions were generally upbeat for services firms throughout the year. The revenues index for nonretail service firms was positive in every month except February. For 2016, the average index reading was six, a survey measure that indicated a slow and steady expansion for services firms. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 23 Anecdotally, tourism and hospitality services generally experienced robust growth as contacts reported rising demand at hotels and restaurants. Retail firms experienced a somewhat more varied business climate in 2016. According to the Richmond Fed survey, the revenues index for retail services remained above zero for the majority of the year but experienced a few months of negative values, including a sharp decline in August. Throughout the year, brick-and-mortar retailers commented on the challenges of competing with online retailers and reported unreliable foot traffic. The surveys’ measures of employment and wages were generally consistent with labor market data from other sources. Throughout the year, most manufacturers reported more employees, with a notable exception in September, when the index fell steeply into negative territory. The index for manufacturing wages has been positive since early 2010, but the average index value has trended higher in recent years. In 2016, the average index value was sixteen compared with thirteen in 2015—both up from nine in 2010. Similarly, in the service sector, the index for number of employees remained above zero for all of 2016, and the average index value rose slightly compared to the prior year. As was the case with the revenues index, the nonretail services subsector followed the overall trend while the retail subsector was more volatile, with four months of negative employment index values. Average wages, on the other hand, mirrored the manufacturing industry, with persistently positive index readings in 2016 that were just slightly higher than in 2015 and considerably higher than 2010 through 2014. Furthermore, this trend was consistent among both retail and nonretail services. Real Estate Fifth District housing markets continued to improve in 2016, but they lagged national averages in some metrics. According to CoreLogic Information Solutions, District house prices grew 4.1 percent on a year-over-year basis, a rate that lagged the national average of 6.3 percent. The sharpest increase in the District was in South Carolina, where home prices rose 5.9 percent, while West Virginia reported the lowest house-price growth of 2.6 percent. Real estate agents throughout the District also reported rising prices and home sales throughout the year, but results varied by region and price level. In some cases, low inventory levels simultaneously reined in sales growth and spurred on new construction. On balance, residential construction continued to accelerate in 2016 but was somewhat constrained by the availability of lots and a tight market for construction workers. Again, reports varied by location, with some reports of lot shortages driving up lot prices and, in at least one case, contributing to an increase in multifamily leasing and rents. Jurisdictions across the Fifth District issued a combined 138,900 residential building permits in 2016, which was a 1.3 percent increase over the previous year. About 27 percent of those permits were for multifamily units, down slightly from the 31 percent share of total units in 2015. Growth in single-family housing starts was more robust at 4.0 percent. Commercial real estate (CRE) activity generally expanded throughout 2016. Groceryanchored retail centers, hotels, health care facilities, and schools were, anecdotally, some of the faster-growing segments. Demand for construction in the retail segment also picked up; however, the square footage of a typical project was smaller than in the past. Office and 24 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT industrial leasing also rose on the whole, but there were some reports of supply constraints, particularly among the highest quality, Class-A properties. Banking Conditions Amid industry consolidation and earnings pressures from the continued low interest rate environment in 2016, Fifth District banks experienced steady loan growth, particularly in the CRE sector, and overall improvement in credit-quality indicators. Although 94 percent of Fifth District banks were profitable in 2016, earnings remained under pressure, contributing to the industry’s consolidation, mostly among smaller banks. The District’s overall bank population declined by 8 percent, double the national consolidation rate. This trend helped some District banks improve earnings by achieving economies of scale. The District’s median return on average assets (ROAA) of 0.73 percent improved slightly from a year ago with about half of District banks reporting an increasing ROAA. However, the District’s median ROAA continued to lag the nation’s by 0.20 percentage points due, in part, to higher than average overhead and personnel expense. Earnings improvements were mostly attributable to higher noninterest income and declining overhead costs related to greater efficiency gains at mid- and large-sized banks. While interest rates have risen, rate increases have yet to augment Fifth District margins, which remained compressed due to competition and the low-rate environment. Balance sheets expanded, with median loan growth of 7.3 percent outpacing the national median of 5.8 percent. The fastest-growing segments were in CRE categories, namely nonowner-occupied CRE, and construction, land, and development. CRE loan growth led to higher CRE loan concentrations, a continuing trend in the District, and weighed on capital ratios. Credit quality indicators improved, on average, with the Fifth District’s level of nonperforming loans declining, but nonperforming loans remained above prerecession levels and 0.25 percentage points higher than the national average. While overall loan delinquencies improved, some District banks’ provision expenses ticked up from the previous year. Conclusion Overall, 2016 was a year of continued economic expansion in the Fifth District. Employment expanded and wage growth accelerated slightly. However, the tightening labor market may have made it more difficult for businesses to find appropriately skilled employees. In some industries, such as construction, these difficulties could have constrained growth. Residential and commercial construction activity did pick up, though, as house prices rose and new home inventories continued to dwindle. Meanwhile, both manufacturers and services firms across the District reported positive developments in 2016 and were optimistic about the near future. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 25 Boards, Councils, Officers, and Senior Professionals FEDERAL RESERVE BANK OF RICHMOND BOARD OF DIRECTORS The Bank’s Board of Directors oversees management of the Bank and its Fifth District offices, provides timely business and economic information, participates in the formulation of national monetary and credit policies, and serves as a link between the Federal Reserve System and the private sector. Six directors are elected by banks in the Fifth District that are members of the Federal Reserve System, and three are appointed by the Board of Governors. Directors who are not bankers appoint the Bank’s president and first vice president with approval from the Board of Governors. The Bank’s Board of Directors annually appoints the Fifth District’s representative to the Federal Advisory Council, which consists of one member from each of the twelve Federal Reserve Districts. The council meets four times a year with the Board of Governors to consult on business conditions and issues related to the banking industry. BALTIMORE AND CHARLOTTE BRANCHES BOARDS OF DIRECTORS The Bank’s Baltimore and Charlotte branches have separate boards that oversee operations at their respective locations and, like the Richmond Board, contribute to policymaking and provide timely business and economic information about the District. Four directors on each of these boards are appointed by the Richmond directors, and three are appointed by the Board of Governors. COMMUNITY DEPOSITORY INSTITUTIONS ADVISORY COUNCIL Created in 2011, the Bank’s Community Depository Institutions Advisory Council advises the Bank’s management and the Board of Governors on the economy, lending conditions, and other issues from the perspective of banks, thrifts, and credit unions with total assets under $10 billion. The council’s members are appointed by the Bank’s president. COMMUNITY INVESTMENT COUNCIL Established in 2011, the Community Investment Council advises the Bank’s management about emerging issues and trends in communities across the Fifth District, including low- and moderate-income neighborhoods in urban and rural areas. The council’s members are appointed by the Bank’s president. PAYMENTS ADVISORY COUNCIL Created in 1978, the Payments Advisory Council serves as a forum for communication with financial institutions about financial services provided by the Federal Reserve. The council helps the Bank respond to the evolving needs of its banking constituency. Council members are appointed by the Bank’s first vice president. THANK YOU Thank you to those directors who completed their service in 2016: Russell C. Lindner, Charles R. Patton, and C. Richard Miller Jr. of the Richmond Board; Samuel L. Ross of the Baltimore Board; and Deborah AguiarVélez, Elizabeth A. Fleming, and Paul E. Szurek of the Charlotte Board. Also, Kelly S. King completed his service as the Fifth District’s representative to the Federal Advisory Council. In 2017, the Bank welcomed seven new directors: Calvin G. Butler Jr., Ángel Cabrera, and William A. Loving Jr. joined the Richmond Board; Wayne A.I. Frederick joined the Baltimore Board; and Michael D. Garcia, Michelle A. Mapp, and R. Glenn Sherrill Jr. joined the Charlotte Board. Listings of boards and councils on the following pages include members and titles as of December 31, 2016, unless otherwise noted. 26 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Board of Directors—Federal Reserve Bank of Richmond From the left: Kathy J. Warden, C. Richard Miller Jr., Catherine A. Meloy, Charles R. Patton, Margaret G. Lewis, Russell C. Lindner, Susan K. Still, Thomas C. Nelson, Robert R. Hill Jr. CHAIR Russell C. Lindner Executive Chairman and Chief Executive Officer The Forge Company Washington, D.C. DEPUTY CHAIR Margaret G. Lewis Retired President HCA Capital Division Richmond, Virginia Robert R. Hill Jr. Chief Executive Officer South State Corporation and South State Bank Columbia, South Carolina Catherine A. Meloy President and Chief Executive Officer Goodwill of Greater Washington and Goodwill Excel Center Washington, D.C. C. Richard Miller Jr. President and Chief Executive Officer Woodsboro Bank Woodsboro, Maryland Thomas C. Nelson Chairman, President, and Chief Executive Officer National Gypsum Company Charlotte, North Carolina Charles R. Patton President and Chief Operating Officer Appalachian Power Company Charleston, West Virginia Kathy J. Warden Corporate Vice President and President, Mission Systems Northrop Grumman Corporation Linthicum, Maryland FEDERAL ADVISORY COUNCIL REPRESENTATIVE Kelly S. King Chairman and Chief Executive Officer BB&T Corporation Winston-Salem, North Carolina Susan K. Still President and Chief Executive Officer HomeTown Bankshares Corporation and HomeTown Bank Roanoke, Virginia Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 27 Board of Directors—Baltimore Branch From the left: Mary Ann Scully, Kenneth R. Banks, Laura L. Gamble, Christopher J. Estes, Susan J. Ganz, Austin J. Slater Jr., Samuel L. Ross CHAIR Samuel L. Ross Chief Executive Officer Bon Secours Baltimore Health System Baltimore, Maryland Kenneth R. Banks President and Chief Executive Officer Banks Contracting Company Greenbelt, Maryland Christopher J. Estes President and Chief Executive Officer National Housing Conference Washington, D.C. Laura L. Gamble Regional President Greater Maryland PNC Baltimore, Maryland 28 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Susan J. Ganz Chief Executive Officer Lion Brothers Company, Inc. Owings Mills, Maryland Mary Ann Scully Chairman, President, and Chief Executive Officer Howard Bancorp Ellicott City, Maryland Austin J. Slater Jr. President and Chief Executive Officer Southern Maryland Electric Cooperative, Inc. Hughesville, Maryland Board of Directors—Charlotte Branch From the left: Claude Z. Demby, Elizabeth A. Fleming, Michael C. Crapps, Laura Y. Clark, Paul E. Szurek, Jerry L. Ocheltree CHAIR Laura Y. Clark Elizabeth A. Fleming Michael C. Crapps Jerry L. Ocheltree Claude Z. Demby Paul E. Szurek Chief Impact Officer United Way of Central Carolinas Charlotte, North Carolina President and Chief Executive Officer First Community Bank Lexington, South Carolina Vice President and General Manager Cree, Inc. Durham, North Carolina Former President Converse College Spartanburg, South Carolina In Memoriam: Deborah Aguiar-Vélez Sistemas Corporation Charlotte, North Carolina President and Chief Executive Officer Carolina Trust Bank Lincolnton, North Carolina Former Chief Financial Officer Biltmore Farms, LLC Asheville, North Carolina Resigned on September 1, 2016 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 29 Community Depository Institutions Advisory Council CHAIR Robert A. DeAlmeida* President and Chief Executive Officer Hamilton Bank and Hamilton Bancorp, Inc. Towson, Maryland Suzanne S. DeFerie President and Chief Executive Officer Asheville Savings Bank and ASB Bancorp, Inc. Asheville, North Carolina Michael P. Fitzgerald Vice Chairman United Bank, Inc. Washington, D.C. William A. Loving Jr. Ronald D. Paul Judy Tharp Gary R. Mills Jan Roche Michael O. Walker R. Arthur Seaver *In 2016, Robert A. DeAlmeida served as the Fifth District’s representative on the Community Depository Institutions Advisory Council at the Board of Governors of the Federal Reserve System. President and Chief Executive Officer Pendleton Community Bank, Inc. Franklin, West Virginia President and Chief Executive Officer First Community Bank Bluefield, Virginia David L. Morrow President and Chief Executive Officer CresCom Bank Charleston, South Carolina Chairman and Chief Executive Officer EagleBank and Eagle Bancorp, Inc. Bethesda, Maryland President and Chief Executive Officer State Department Federal Credit Union Alexandria, Virginia Chief Executive Officer Southern First Bank Greenville, South Carolina Community Investment Council CHAIR Mary M. Hunt Senior Program Officer The Claude Worthington Benedum Foundation Pittsburgh, Pennsylvania MaryAnn Black Associate Vice President, Office of Community and Local Government Relations Duke University Health System Durham, North Carolina Tamea L. Franco President and Chief Executive Officer Global Metal Finishing, Inc. Roanoke, Virginia Earl F. Gohl Federal Co-Chair Appalachian Regional Commission Washington, D.C. 30 Deborah L. Hooper Deborah McKetty Jody Keenan Paul Phillips Chief Operating Officer Greensboro Partnership Greensboro, North Carolina State Director Virginia Small Business Development Center Fairfax, Virginia John Maneval Deputy Director, Multifamily Housing and Business Lending Maryland Department of Housing and Community Development Lanham, Maryland Charles Martin Administrative Vice President, Regional Community Reinvestment Officer M&T Bank Baltimore, Maryland Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Executive Director CommunityWorks Carolina Greenville, South Carolina President and Chief Executive Officer Freedom First Federal Credit Union Roanoke, Virginia Kent R. Spellman Executive Director The West Virginia Community Development Hub Stonewood, West Virginia President and Chief Executive Officer Piedmont Advantage Credit Union Winston-Salem, North Carolina President and Chief Executive Officer Benchmark Community Bank and Benchmark Bankshares, Inc. Kenbridge, Virginia Payments Advisory Council CHAIR E. Stephen Lilly Executive Vice President and Chief Operating Officer First Community Bancshares, Inc. Bluefield, Virginia William E. Albert Senior Vice President First Century Bank Bluefield, West Virginia Tim Boike Robert E. Dael President and Chief Executive Officer MACHA—The Mid-Atlantic Payments Association Hanover, Maryland Peter Davey Vice President and Director, Enterprise Payments Capital One Bank Richmond, Virginia Jeff W. Dick Senior Vice President Wells Fargo and Company Minneapolis, Minnesota Chairman and Chief Executive Officer MainStreet Bank Fairfax, Virginia Ronald L. Bowling Kristi A. Eller President and Chief Executive Officer First Peoples Bank Mullens, West Virginia Karen Buck Head, Commercial, Retail and Payment Operations TD Bank Mount Laurel, New Jersey Kim L. Bunn Senior Vice President and Operations Executive Bank of America Jacksonville, Florida Richard Chin Senior Vice President and Treasurer Pentagon Federal Credit Union Alexandria, Virginia John Kevin Cranford Senior Vice President BB&T Corporation Charlotte, North Carolina Chief Information Officer and Executive Vice President, Operations Yadkin Bank Statesville, North Carolina Margo D. Foust Senior Vice President, Operations and Process Improvement American National Bank and Trust Company Danville, Virginia Terry Garner Senior Vice President, Deposit Operations Southern First Bank Greenville, South Carolina Jamin M. Hujik Executive Vice President CresCom Bank Charleston, South Carolina Adrian S. Johnson Steve Shuford Alison Lyewski Woody Shuler Senior Vice President and Chief Financial Officer MECU of Baltimore, Inc. Baltimore, Maryland Senior Vice President, EIS Transaction Operations SunTrust Bank Orlando, Florida Carla A. Nealy Executive Vice President and Chief Operating Officer The Harbor Bank of Maryland Baltimore, Maryland Tracy J. Nelms Executive Vice President TowneBank Suffolk, Virginia Holly Pingatore Senior Vice President and Director of Deposit Operations South State Bank Charleston, South Carolina Rick Rhoads Senior Vice President, E-Services State Employees’ Credit Union Raleigh, North Carolina Susan G. Riel Senior Executive Vice President and Chief Operating Officer EagleBank Bethesda, Maryland Senior Vice President and Director, Treasury Management Paragon Bank Raleigh, North Carolina Vice President, Finance SRP Federal Credit Union North Augusta, South Carolina Steve Stone Executive Vice President United Bank Charleston, West Virginia Chris Tolomeo Senior Vice President, Banking Services M&T Bank Amherst, New York Paul Trozzo Senior Vice President PNC Bank Pittsburgh, Pennsylvania David Willis Senior Vice President, Debit Card and Funds Services Navy Federal Credit Union Vienna, Virginia Gayle Youngblood Assistant Vice President, Product Management State Employees Credit Union of Maryland Linthicum, Maryland D.J. Seeterlin Chief Information Officer Chesapeake Bank Kilmarnock, Virginia Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 31 Management Committee From the left: Roland Costa, David E. Beck, Michelle H. Gluck, Becky C. Bareford, Mark L. Mullinix, Kartik B. Athreya, Jeffrey M. Lacker, Jennifer J. Burns, Janice E. Clatterbuck, Matthew A. Martin, Michael D. Stough Jeffrey M. Lacker President Mark L. Mullinix First Vice President and Chief Operating Officer Kartik B. Athreya Executive Vice President and Director of Research Becky C. Bareford Senior Vice President, Human Resources and Finance David E. Beck Senior Vice President and Baltimore Regional Executive Jennifer J. Burns Executive Vice President, Supervision, Regulation, and Credit Janice E. Clatterbuck Senior Vice President and Chief Information Officer Roland Costa Senior Vice President and Chief Technology Officer Michelle H. Gluck Executive Vice President, General Counsel, and Chief Risk Officer Matthew A. Martin Senior Vice President and Charlotte Regional Executive Michael D. Stough Senior Vice President and General Auditor Listings include officers, senior professionals, and titles as of December 31, 2016. 32 Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Officers John A. Weinberg Senior Vice President and Special Advisor to the President Huberto M. Ennis Group Vice President Thomas A. Lubik Group Vice President Lisa T. Oliva Group Vice President Michael L. Wilder Group Vice President and Chief Financial Officer Hattie R.C. Barley Vice President Christy R. Cleare Vice President Todd E. Dixon Vice President Kevin W. Fergusson Vice President and Medical Director Joan T. Garton Vice President Richard B. Gilbert Vice President Rebecca Goldberg Vice President Anne C. Gossweiler Vice President Bruce E. Grinnell Vice President Mattison W. Harris Vice President Kathleen R. Houghtaling Vice President and Chief Diversity Officer Cathy I. Howdyshell Vice President Gregory A. Johnson Vice President and Assistant General Auditor Senior Professionals H. Julie Yoo Vice President Niranjan Chandramowli Borys M. Grochulski Patricia A. Perry Cary B. Crabtree Assistant Vice President Robert L. Hetzel Bary M. Dalton Assistant Vice President Jeffrey B. Deibel Assistant Vice President Jacqueline R. Draper Assistant Vice President Senior Economist and Research Advisor Markus A. Summers Assistant Vice President Christian Matthes Adam M. Drimer Alexander T. Swartz Assistant Vice President Urvi Neelakantan Gina E. Friese Sandra L. Tormoen Raymond E. Owens III Kimberley D. Fuller Assistant Vice President Keith R.G. Goodwin Assistant Vice President Jennifer J. Hall Assistant Vice President Pierre-Daniel G. Sarte Ann S. Harrison BALTIMORE BRANCH Nicholas Trachter Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant General Counsel Assistant General Counsel Assistant Vice President Senior Economist Senior Policy Economist James Trotta Senior Economist and Policy Advisor Lauren E. Ware Senior Policy Economist Santiago M. Pinto Senior Advisor Chad K. Harper Senior Economist Steven T. Bareford Senior Economist and Policy Advisor Kerri A. Coard Zhu Wang John R. Walter Senior Economist CHARLOTTE BRANCH Lisa A. White Senior Economist and Policy Advisor Terry J. Wright SUPERVISION, REGULATION, AND CREDIT Assistant Vice President D. Keith Larkin Assistant Vice President Chuck Lewis Assistant Vice President Steve V. Malone Assistant Vice President Randal C. Manspile Assistant Vice President Jonathan P. Martin Assistant Vice President Diane H. McDorman James T. Nowlin Dennis H. Ott Jr. Brielle M. Stanley Christopher J. Palumbo Vice President John Bailey Jones Diane R. Knapp Johnnie E. Moore Alexander L. Wolman Michael J. Seifert Assistant Vice President Assistant Vice President Andrew S. McAllister Vice President Senior Advisor Pinkaj R. Klokkenga Cheryl R. Moore Vice President Andreas L. Hornstein Assistant Vice President Assistant Vice President Bennie R. Moore Vice President Jason C. Schemmel Senior Economist and Research Advisor James R. Hart Ann B. Macheras Vice President Melanie M. Rose Senior Economist Vice President Malissa M. Ladd Vice President RESEARCH Assistant Vice President and Corporate Secretary Laura H. Mayer Vice President Christin L. Patel Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Senior Vice President Group Vice President and Charlotte Deputy Regional Executive Roy H. Webb Azamat Abdymomunov Lead Financial Economist Jeremy B. Caldwell Eliana Balla Kerri R. O’Rourke-Robinson Craig S. Edwards Richard F. Westerkamp Jr. Craig W. Frascati Ronald G. Barnes D. Keith Maglinger Joshua R. Daulton Hemangini R. Parekh Jeffrey R. Gerlach Stanley F. Poszywak Kelly J. Stewart Todd M. Ryan Lead Financial Economist Vice President Vice President Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President In Memoriam: Large Bank Principal Examiner Large Bank Principal Examiner Large Bank Principal Examiner Large Bank Principal Examiner Large Bank Principal Examiner Large Bank Principal Examiner Steven D. Sanderford Large Bank Principal Examiner P.A.L. “Trish” Nunley Deputy General Counsel Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 33 Federal Reserve Information Technology (FRIT) Management Council From the left: Matthew D. Larson, Kathryn K. Smith, Scott C. Furman, Robert I. Turner, Lyn McDermid, David N. Alfano, Devon A. Bryan, James A. Lammers Lyn McDermid System Chief Information Officer Devon A. Bryan Executive Vice President and Chief Information Security Officer James A. Lammers Senior Vice President and Chief Administrative Officer Scott C. Furman Senior Vice President, Treasury Services Executive Vice President and Chief Technology and Strategy Officer Matthew D. Larson Robert I. Turner Kathryn K. Smith Executive Vice President and Chief Operating Officer 34 David N. Alfano Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT Senior Vice President, End User Services Senior Vice President, Project and Program Delivery Federal Reserve Information Technology (FRIT) Officers Senior Professionals Jeffrey F. Crow Jon C. Jeswald James A. Caulfield Elise P. Ott Donovan O. Harper II Frederick B. Johnson William C. Conway II Andy Hendrickson Carie L. Kelleher John F. Crabtree Gerald L. Moreno Vicki L. Kosydor Michael S. Everett Christopher A. Tignor S. Craig Minyard William H. Fenerty Kevin J. Craig Mahnaz Moosa Lisa Marie Gravely Gary M. Patton A. Vinton Myers III Gary A. Helfrich Paul R. Sans Peter J. Purcell M. Polly Helm Scott D. Auble R. Nathan Ragan Peter B. Holleran Nicole E. Bennett Victoria F. Riendeau M. Brannon Howle Reginal L. Bryant Joyce M. Romito Bradley M. Joiner Jane Y. Burk Apurva A. Shah John T. Lines Gerry P. Collins Hunter R. Shomo Garland H. McKenzie Michael E. Cortese Stephen B. Silverman Ellen D. Mitchell Albert M. D’Avanzo Joshua N. Snell Arthur J. Papa Jr. Fay T. Donahue Sherri L. Thorne Heidi R. Patterson Frank J. Doto Jeannie L. Willette Irina V. Piven Valerie A. Freund Abigail T. Baker Kevin A. Reed Mark A. Hamilton Michael L. Bellanti Stephanie T. Shetterly Kristofer K. Hogan Jeffrey S. Borneman Christopher T. Szymonik Christine M. Holzem Cynthia S. Bullington Thomas J. Weber Tamera S. Hornsby-Fink Melissa E. Butler Senior Vice President Senior Vice President Senior Vice President Senior Vice President Senior Vice President Group Vice President Group Vice President Group Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Chief Application Integration Engineer Michael T. Shaughnessy Chief Application Integration Engineer L. Sergio Altomare Business Architect (Treasury) Ian W. Beirnes Business Architect (Treasury) Pedro E. Fong Business Architect Devin D. Gordon Business Architect M. Scott Hannah Business Architect Robert B. Klank Business Architect Darren L. Knutson Business Architect Donald H. Larmee Business Architect Susan L. Perlmutter Information Architect Eric B. Stanley Information Architect Listings include officers, senior professionals, and titles as of December 31, 2016. Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Assistant Vice President Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT 35 Financial Statements T he audited annual financial statements of the Federal Reserve Bank of Richmond as of and for the years ended December 31, 2016, and December 31, 2015, are incorporated here by reference. They are available at the Board of Governors of the Federal Reserve System at www.federalreserve.gov/aboutthefed/files/richmondfinstmt2016.pdf. That public disclosure also provides: Notes to Financial Statements, Management’s Report on Internal Control over Financial Reporting, and the Independent Auditors’ Report. The Board of Governors’ Statement of Auditor Independence is provided below. Statement of Auditor Independence The Federal Reserve Board engaged KPMG to audit the 2016 combined and individual financial statements of the Reserve Banks.1 In 2016, KPMG also conducted audits of internal controls over financial reporting for each of the Reserve Banks. Fees for KPMG services totaled $6.7 million. To ensure auditor independence, the Board requires that KPMG be independent in all matters relating to the audits. Specifically, KPMG may not perform services for the Reserve Banks or others that would place it in a position of auditing its own work, making management decisions on behalf of the Reserve Banks, or in any other way impairing its audit independence. In 2016, the Bank did not engage KPMG for any non-audit services. ____________________________________ 1 36 In addition, KPMG audited the Office of Employee Benefits of the Federal Reserve System (OEB), the Retirement Plan for Employees of the Federal Reserve System (System Plan), and the Thrift Plan for Employees of the Federal Reserve System (Thrift Plan). The System Plan and the Thrift Plan provide retirement benefits to employees of the Board, the Federal Reserve Banks, the OEB, and the Consumer Financial Protection Bureau. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT The Federal Reserve Bank of Richmond 2016 Annual Report was produced by the Research Department, Publications Division. MANAGING EDITOR: Karl Rhodes DESIGN: Janin/Cliff Design GROUP PHOTOGRAPHY: Larry Cain PRINTING: Federal Reserve Bank of Richmond Special thanks to Gina Karp, Lisa Kenney, Susan Maxey, and Joseph Mengedoth. The 2016 Annual Report also is available on the Bank’s website: www.richmondfed.org. Federal Reserve Bank of Richmond l 2016 ANNUAL REPORT FIFTH FEDERAL RESERVE DISTRICT OFFICES RICHMOND 701 East Byrd Street Richmond, Virginia 23219 (804) 697-8000 www.richmondfed.org BALTIMORE 502 South Sharp Street Baltimore, Maryland 21201 (410) 576-3300 CHARLOTTE 530 East Trade Street Charlotte, North Carolina 28202 (704) 358-2100