Economic Clusters
The term “economic cluster” at its most basic level refers to a group of firms that profit from their association with one another. These firms share certain types of inputs or outputs (e.g. raw materials, labor force, or customers), and within a cluster some firms will typically have close purchasing and sales links while others may be direct competitors.

The advantages offered by a cluster vary, but are usually linked to what are known as economies of agglomeration in inputs, outputs, and labor markets. Put more simply, if there are lots of firms in a region making, for example, computer processors, then firms selling raw materials to them can take advantage of a big market for their goods, and the processor manufacturers will likely have choices among raw materials suppliers giving them more power to keep prices down. In labor markets, having lots of companies depending on a similar skill set means that a regional labor pool develops with that skill set and workers can move more easily between firms. Less directly, clusters are understood to offer benefits from “knowledge spillovers” and dense networks of relationships that increase the transfer of best practices among firms. The shared labor pool and the inevitable shared social networks that result mean that companies can learn from one another more quickly than they would in isolation.

The geographic scope of economic clusters is a matter of considerable debate. While some definitions focus on geographic proximity as a defining element of cluster functions, other applications of the term see cluster benefits in attenuated value chains with global extents. In practice the geographic extent of clusters is typically defined by the region of interest of a local economic development agency, by expert opinion, by the limitations of data on industry linkages which are rarely collected at scales finer than county-level, or some combination of the above.

Economic clusters have risen to prominence as a focus of economic development efforts because of the perception that they can produce greater returns on investment in terms of jobs created and economic growth than less targeted efforts. Unfortunately, the difficulties in quantifying the relationships that define a cluster have made it difficult to pinpoint their existence, let alone assess their benefits. While there is significant anecdotal support for the efficacy of clusters as economic development engines, their generalized benefits are still hotly contested.

Income Inequality
Income inequality describes the extent to which income is distributed unevenly among residents of an area. High levels of inequality indicate that a small number of people receive most of the total income, and that most people receive only a small share of the total. Inequality can be a barrier to social cohesion and democratic participation when income determines access to social settings and political institutions. Income inequality is measured using a variety of methods, including comparing income shares across different quintiles of the income distribution, or using an index such as the Gini coefficient. By most measures, income inequality has increased steadily in the United States during the past four decades.

Poverty Measurement
The U.S. has historically relied on an absolute income measure of poverty to determine who is considered poor in our society. Absolute measures of poverty assume there is a specific income level under which a family cannot subsist at the most basic level. The U.S. adopted official poverty thresholds based on family size in the 1960′s, and this measure continues to be used today, though it is adjusted annually for inflation. Critics of an absolute measure emphasize that the cost of living varies geographically, and the notion of what is poor may vary across time and place. They propose a relative measure, which looks at poverty relative to a society’s existing level of development. A relative measure is more flexible than an absolute measure because it can be adjusted for changes in living standards.

The reliance on income as the standard for absolute and relative measures of poverty is often criticized. Poverty can also be conceptualized in other ways including self-reliance, social exclusion, and consumption measures. Self-reliance measures focus on the importance of earning capacities and a family’s ability to support a livelihood through their own means. This addresses the issue that some families are unable to meet a certain standard of living despite the complete participation of all adults in the workforce. Social exclusion measures the capacity to participate fully in society, and suggests that poverty can exclude people from the institutions, services, social networks, and employment opportunities available to mainstream society. Consumption poverty or material hardship considers a family’s level of consumption relative to a threshold. Measures that capture the essence of material hardship include basic needs and food insecurity, housing security, access to medical and health insurance, ability to pay utility bills, housing quality, and ownership of durable goods.

There are also suggestions to measure poverty using local perceptions, and to evaluate a society’s poverty gap – the magnitude of the difference between family resources and poverty thresholds. These various measures each shed light on different aspects of poverty and re-define who is considered poor. For this reason, poverty measurement can be a controversial and political process.

Social Capital
Social capital is a resource available to individuals or collectivities that can be invested in with the expectation of a future flow of benefits. Social capital is generated by the connections in a social network, and the trust, reciprocity, and resource-sharing qualities of those connections. It can be activated by individuals to gain social support or social leverage, or by collectivities to facilitate organization and collective action. Social capital is commonly viewed as a positive resource, but can become negative when used to exclude outsiders, place excessive restrictions on group members, or forward special interests that are detrimental to the greater good.

Social capital is often sub-divided into different types based on the qualities of network connections and the type of resources those connections make available. Many researchers argue, for example, that strong, internal ties are better at producing trust, whereas weak, external ties are better at providing access to new information. The possession of each type of social capital can therefore lead to diverse outcomes.

Social capital is thought to be associated with a variety of outcomes at both the individual and community level, including employment, education, crime, collective action, governance, and poverty. Research on the association between social capital and poverty suggests that social capital can help individuals obtain resources and job opportunities, and help communities facilitate economic development.

Urban and Rural
“Rural” and “urban” are fuzzy concepts – there is no consensus among researchers and policy makers about how to define and classify the two. Research shows that there are over two dozen definitions that are currently in use by various federal agencies, let alone those employed by researchers, organizations, and local governments. The use of various definitions reflects the multidimensionality of these concepts – the defining criteria can be population size, population density, administrative boundaries, proximity to urban settings, and economic activities. In addition, researchers and policy makers face several challenges when defining/classifying rural and urban, such as defining thresholds and building blocks (geographic unit), and data availability.

The most commonly used federal definitions are those by the Census Bureau, the Office of Management and Budget (OMB), and the Economic Research Service (ERS) of the United States Department of Agriculture (USDA). The Census Bureau defines an urban area as a continuously built up territory with a total population of 2,500 or more, that is comprised of census block groups and blocks with a population density of at least 1,000 persons per square mile and surrounding blocks with an overall density of at least 500 people per square mile. All territory outside urban areas is defined as rural. This scheme is close to popular perceptions of rural and urban, or what we see from the airplane.

The OMB groups counties into metropolitan and non-metropolitan (a new micropolitian system was added in 2003) based on population size in an urbanized area and outlying counties, and commuting patterns between them. Even though the OMB explicitly states that this classification does not equate to a rural-urban classification, many researchers and policy makers use metro/non-metro interchangeably with urban/rural because county is the smallest geographic unit for which annual socioeconomic statistics are available.

The ERS of the USDA probably has the most extensive definitions of rural. Some of the popular classification schemes are the Rural-Urban Continuum Code (RUCC), the Urban Influence Code (UI), and the Rural Urban Commuting Areas (RUCA). The RUCC and UI define rural and urban along county lines, while the RUCA uses the census tract as the building block for more precise information at a finer geographical scale. These classifications define counties/census tracts by size and their degree of urbanization or proximity to metro areas.

Depending on the definition, the shares of U.S. rural population and its socioeconomic characteristics vary substantially. The need for a clear definition to produce accurate research conclusions and efficient and well-targeted government programs has encouraged researchers to create more detailed and precise definitions that go beyond the metro/non-metro dichotomy and overcome the “county trap.” Isserman’s (2005) rural-urban density typology and Waldorf’s (2006) index of relative rurality are two illustrative examples.

With so many definitions out there, which one is the “best”? The key might be a multidimensional approach, taking into account both economic activities and geographic dimensions along with population density/size that clearly delineates the boundaries between rural and urban areas, but at the same time recognizes the continuum and integration between rural and urban. Urban and rural poverty have distinct characteristics and require different policies, but they are also interlinked.