Bernanke Urges Local, Small-Scale Development

Investors and businesses should look for opportunities at the community level, where an increasing amount of data is available, says Federal Reserve Chairman Ben S. Bernanke.

Bernanke said that by "making companies, entrepreneurs, and investors aware of new opportunities and by promoting competition in underserved areas, such information helps put market forces in the service of community development."

Bernanke said he has been impressed by the "professionalism" he has found in community development organizations around the country.

The Fed chairman compared the emphasis on community-level data and investment to a growing movement in international aid, in which emphasis is put on "micro-level, bottom-up approaches" built on local information and analysis.

"Goals should be modest at first," Bernanke said. "But knowledge is cumulative, and sometimes good results can be replicated at larger scales."

Bernanke spoke to the Greenlining Institute's Thirteenth Annual Economic Development Summit, Los Angeles, Calif., via satellite.

The chairman's remarks in their entirety:

By the Numbers: Data and Measurement in Community Economic Development

I would like to thank Greenlining for the opportunity to participate in today's conference. In my time at the Federal Reserve, I have had a number of opportunities to meet with community economic development leaders to discuss issues of mutual concern and learn about the valuable role that community development organizations play in economically distressed areas across the country. I have been particularly impressed, and heartened, by the increasingly high degree of professionalism in the field. In this area, as in social policy generally, good intentions are not enough. Successful community development requires knowledge--knowledge about the particular community in question and about what has worked in similar communities in the past--and community development organizations are working assiduously and with sophisticated tools to help develop that knowledge.

Of course, knowledge bearing on community economic development has both qualitative and quantitative aspects, and it can be gained through diverse channels, from talking to people in a neighborhood to performing a regression analysis. Today, I will focus on the progress that is being made on the quantitative side--in particular, the remarkable strides that have been made in developing and analyzing social and economic data at the community level. The information that can be extracted from detailed data profiles of individual communities supports economic development in several distinct ways. First, by making companies, entrepreneurs, and investors aware of new opportunities and by promoting competition in underserved areas, such information helps put market forces in the service of community development. Second, both government policymakers and community development organizations need the reality check that only hard data can provide. To know whether our policies and programs are delivering the desired results, we need to be able to measure inputs and outcomes, program by program and community by community. Better information increases accountability and promotes good governance in both the public and the nonprofit sectors. Third, the increased availability of community-level data facilitates independent research, which is vital to informing the public policy debate and to developing further community development efforts, both public and private.

Historically, government agencies have been the source of the most-comprehensive social and economic data bearing on community development. An important example is the data collected by the Federal Reserve under the Home Mortgage Disclosure Act (HMDA). The HMDA data set provides extensive information on home mortgage applications to virtually all U.S. lenders, including approval rates, the socioeconomic characteristics of applicants, and most recently, mortgage pricing information. As all good social scientists know, the data never "speak for themselves," and the HMDA information, like any data set, must be interpreted with care and insight. Still, for nearly three decades, the HMDA data have provided valuable information about mortgage lending patterns, contributed to significant changes in mortgage credit practices, informed regulatory policies, and supported fair-lending enforcement.

Although government agencies continue to be an important source of data on community development, data collection and data analysis in this area is increasingly becoming the province of the private and nonprofit sectors, notably including community development organizations themselves. In recent years, we have seen a series of data-collection initiatives outside the public sector, with objectives that include the improvement of development strategies, the identification of new opportunities, the quantification of risk, and the exertion of influence on the direction of public policy. Many of these efforts have already had significant payoffs.

In the rest of my remarks, I will discuss some specific ways data and quantitative measurement have been used in community development. To be clear, I do not believe that all aspects of economic development can or should be quantified; and, as I have already noted, the data never speak for themselves but must be interpreted with care. Still, improving the measurement of inputs and outcomes is critical to better development policy. In this regard, it is interesting to observe that we have seen some convergence between best practices in community economic development and in economic development policy at the international level. I will conclude by noting a few of those parallels and their implications.

Discovering Market Potential

Good data support community growth and development by helping to identify previously unrecognized market opportunities. Free markets can be a powerful source of economic development, but markets work less effectively when information about potential opportunities is absent or costly for private actors to obtain. Several noteworthy initiatives have helped to provide better information about the economic potential of lower-income and underserved communities. For example, the Local Initiative Support Corporation's (LISC) MetroEdge initiative seeks to demonstrate the market potential of diverse communities through customized data analyses of each community's demographics and buying power. Such analysis can provide investors with a different perspective when they assess a neighborhood's viability for investment. In one instance, a national home-improvement retailer used MetroEdge data as the basis for its decision to establish a store in inner-city Chicago, even though the retailer's own site-selection model presented discouraging indications of profit potential for that neighborhood. With access to new market data, the company could justify its investment in the community, and sales performance was triple what was expected within the first six months of operation.

Similarly, Social Compact's Neighborhood Market DrillDown methodology uses a multilayered research process to provide profiles of the market potential of high-density, lower-income communities. This approach focuses on business indicators--buying power, market size, unmet needs, and market risks--rather than on the deficiency statistics typically used to describe inner-city neighborhoods, such as rates of poverty, crime, and overcrowding. Social Compact, a coalition of business leaders, has applied its DrillDown approach to 101 neighborhoods over the past five years, beginning with Chicago neighborhoods and, most recently, in Santa Ana, California. By tapping existing public records and conducting intensive economic and demographic surveys, the DrillDown analyses of these 101 neighborhoods in eight cities have, in the aggregate, revealed additional income and buying power averaging nearly $6,000 per household, which is not captured by traditional sources of community-level data. Such information may attract private-sector investors to areas that had once been deemed untenable for investment. For example, following Social Compact's study of neighborhoods in Jacksonville, Florida, a developer announced plans to invest $45 million in a multi-use entertainment complex there. A DrillDown study in inner-city Houston revealed a population that was 25 percent larger than Census estimates, resulting in the redevelopment of a 750,000 square foot retail center that brought 2,000 jobs to a neighborhood that had not had new construction in fifty years. This shopping center is now one of the busiest retail centers in the city.

Work to improve the measurement of market potential in inner-city communities is continuing. In one such project, Social Compact and the Brookings Institution's Urban Markets Initiative group are collaborating in reviewing methods for measuring the size and composition of economies in urban areas around the world. The objectives of the review are to develop new tools for measuring economic activity at the local level and to identify areas for future research.

Informing Investors in Community Development

The growth and maturation of community development financial institutions (CDFIs) provide another impetus for data development and analysis at the community level. CDFIs are private-sector financial intermediaries with community development as their primary mission. Like banks and other more-conventional financial intermediaries, CDFIs are in the business of attracting funds and putting those funds to work in productive ways. Also like conventional intermediaries, CDFIs depend heavily on the production of accurate information both to guide investment decisions and to provide a basis for attracting new funding. It is difficult to overstate the importance of adequate and accurate information for attracting capital. Managers of pools of capital have many choices, and they tend to be extremely wary when they cannot fully assess the level of risk presented.

With an appreciation for the need for such information, managers and others with an interest in the CDFI industry have invested substantial effort in designing tools for data collection and analysis that focus on measuring the financial performance--the risks and returns--of CDFI portfolios. An important motivation for these efforts is the need to diversify funding sources for community development, which has relied heretofore largely on grants from government and foundations. To attract more return-oriented investors, including both conventional investors and those with social as well as financial goals, CDFIs must demonstrate financial viability as well as the ability to fulfill the broader development mission.

For example, the Opportunity Finance Network's CDFI Assessment and Rating System (CARS) gathers data to evaluate a CDFI's overall creditworthiness and its effectiveness in using its financial resources to achieve its development objectives. A CDFI is rated for its financial strength and performance in the areas of capital, assets, management, earnings, and liquidity, in a manner broadly analogous to the way a supervisory agency would rate a commercial bank. The financial analysis is supplemented by an evaluation of how well the CDFI is fulfilling its mission, including an assessment of its procedures for tracking the outcomes of its work. To date, more than forty CDFIs have chosen to be evaluated under the CARS, and thirty-one analyses have been completed. Thus far, fifteen potential investors have subscribed to the CARS database, including socially responsible investment funds, brokerage houses, large financial institutions, and national foundations. Although still in its early stages, this initiative, if successful, will have the double benefit of attracting more funds into community development and helping to ensure that those funds are effectively used.

More generally, the movement toward quantifying the performance, risk, and community impact of CDFIs is essential to the growth and sustainability of the field, in my view. By demonstrating both financial viability and social impact through hard data, CDFIs are better positioned to obtain the funding necessary to maintain their operations and to respond to emerging needs and opportunities. Indeed, progress has been made in recent years in the rating and securitization of community development portfolios, a development that should provide CDFIs with increased access to the capital markets and to new sources of liquidity. If the new data and evaluation methods of CDFI performance bear scrutiny, investors will gain confidence in using this information for matching their investment choices with their priorities and risk tolerances. In the community development field, to be sure, financial returns and social returns are not necessarily the same, which is why measurement should include both financial and social indicators. Potential investors, including public-sector and foundation sources of funds, will naturally differ on the weights they put on financial and social returns. To attract the widest range of funding, both types of information should be provided.

Evaluating Policy and Practice

Quantitative information plays yet another important role: increasing the effectiveness of policies and programs. The systematic collection and analysis of data on program inputs and outputs is an increasingly important part of learning about what works. For policymakers, data on program results help guide policy development and improve the allocation of scarce public funds. For community development organizations, participation in broad-based data-gathering serves at least two goals. First, in the long run, their analyses of the activities and the associated outcomes in diverse communities will help them achieve the greatest impact for resources expended. Second, such analyses help community development organizations demonstrate their effectiveness to public and private funders.

A number of methods for evaluating community development projects are currently in use, with more in development. The NeighborWorks America's® Success Measures Data System documents the effect of community development programs throughout the country. Using forty-four indicators and a range of data-collection tools, the system quantifies the effects of housing, economic development, and community building programs at the individual, organization, and community levels. By sharing this knowledge, practitioners, funders, and policymakers can identify programs that achieve the best outcomes and gain insights into the reasons they work. Broad access to this information promotes replication of the most effective programs and may diminish the costs associated with trial-and-error learning.

Another tool available to CDFIs is the Community Investment Impact System developed by the Department of Treasury's CDFI Fund. This system collects detailed information on institutions and transactions, allowing the CDFI Fund to measure community effects and to associate those effects with institutions working in that area. These results can help inform funding decisions, develop programs, establish performance benchmarks, and communicate societal benefits attributable to specific policy. For example, using data from the system, the CDFI Fund found that in a recent year, CDFIs leveraged financial program awards by the fund at a ratio of 20 to 1, using multiple sources of debt and equity financing from banks, local and state governments, private investors, and borrower equity to structure project financing.

Each of these data-driven initiatives share the goal of increasing understanding of opaque markets to support investment, policy, and research. The need for data and tools is the driving force behind the Brookings Institution's Urban Markets Initiative. In establishing this policy center, Brookings acknowledged that limited access to data that captures the viability of urban communities constrains investment in these markets. The think tank is focusing on initiatives that can demonstrate untapped market potential. One such effort is the National Infrastructure for Community Statistics. It will include a central web-based repository that integrates data from federal, state, and local governments and from commercial sources. The ultimate goal of this project, which is under development in collaboration with more than 100 participants from government, nonprofits, and private-sector industries, is to aggregate and to make accessible the data needed to inform decisions about economic development activities.

Parallels to International Economic Development

The usefulness of microeconomic data in community development raises an interesting parallel to recent analyses of international economic development. Although the U.S. context is obviously different in important respects from that of developing countries, domestic community organizations and providers of international aid both face the challenge of fostering economic development in low-income areas. In the United States, our experience in community development over the past thirty years has resulted in an evolution from a centralized, federal-government-driven approach to a heavy reliance on the involvement of community-based organizations and agencies for project development and implementation. In light of this experience, it is quite interesting that some new thinking on international development has rejected the traditional approach to aid, with its emphasis on large-scale projects and top-down planning, in favor of micro-level, bottom-up approaches that use local information and systematic analyses of inputs and outcomes.

Critics of traditional development aid programs, such as New York University economist William Easterly, argue that such programs have not succeeded because those implementing the programs do not have the information necessary to make effective use of resources. For example, a World Bank report describes an irrigation project that was being designed by technical staff for an area of Nepal that was thought to be unirrigated. A delay in the project led to the discovery that, in fact, eighty-five fully functioning farmer-managed irrigation systems existed in the "unirrigated" area. Further, another irrigation program actually reduced productivity because it undermined pre-existing arrangements among farmers. Quite obviously, those planning these projects needed local input to make better use of the project resources.

Easterly advocates a more decentralized, grass-roots approach that involves local groups and emphasizes feedback and accountability. Illustrative of this point, a World Bank study of rural water supply projects found that, of those projects with a high level of participation by local beneficiaries, more than two-thirds were successful whereas, among those projects with little local beneficiary participation, only 12 percent were successful. Both feedback and accountability depend, of course, on accurate measurement of results. In practice, measuring results is easier at the local level, in part because comparisons can be drawn to other localities that have not received aid. Incentives also matter; and smaller, more-tailored projects for which responsibilities are well defined are likely to provide better incentives to the people who carry them out than those that large, diffuse projects will provide. Follow-up is important as well. Easterly criticizes, for instance, situations in which foreign aid has been used to build highly visible projects, such as new roads, without providing resources or incentives to do the less-glamorous work of maintaining them.

The themes emphasized by Easterly and other analysts of international aid programs are useful, I think, in the context of domestic community development. Although national initiatives have their place, often the most effective programs take place at the level of the individual community, using local information and local participation. Accountability and feedback, facilitated by data development and quantitative analysis as well as by more-qualitative information, are critical for success. Goals should be modest at first; but knowledge is cumulative, and sometimes good results can be replicated at larger scales. Research, both quantitative and qualitative, furthers learning. None of this is easy, particularly since the data have a way of challenging our views about what works and what doesn't. But a great deal is at stake both internationally and domestically and serious empirical analysis has no substitute. The development of more and better data on economically distressed communities, together with sophisticated tools for analyzing those data, is essential for continued progress in community economic development.