Commercial services in most rural communities are under several profit-squeezes in their struggles to survive in rapidly changing economies. Merchants in Downtown Main Streets in particular often must compete with services in mall locations both within and outside their jurisdictions, leaving their Main Streets with vacant and /or boarded-up store fronts. 'Cross-Roads' analyses examine community commercial, professional, financial, and public services in order to understand which services in which locations are potentially more viable and which are more vulnerable. They also can suggest which services gaps may represent worthy investment opportunities, and which not worthy, as well as give local leaders low-cost strategies for achieving some local economic development goals.

The Cross-Roads and Main-Streets technique of analyses is one of the most useful tools in the kit of community and rural developers. It is useful because it involves the organization of people to collect the data, because the data collected are on important problems facing most rural communities, and provide understandable and meaningful approaches to resolving the problems. Losses of downtown businesses and guidelines for finding viable businesses to locate in rural down towns is of vital importance to local leaders, and the services provided by these businesses are vital to their citizens.

It is a study demonstrating a pattern (of relationships) among a set of community services in the communities of a county or region. The pattern shows communities' services in progressive order by community size and by "commonness" of a service. The pattern specifies 'holes' (above the scale-score line). Holes are potential business opportunities; errors are potential business losses. The analysis and pattern can also be applied to communities' public, professional, and financial services.

How the analysis is conducted

Some entity in a locality must take local leadership for implementing a cross-roads analysis, usually assisted by a university entity. This establishes a local participatory component. These entities have often been county Cooperative Extension educators. Others have included partnerships with Chambers of Commerce or local Economic Development Committees.

A team, (driver and recorder), drive to every "crossroad" and/or village and hamlet where there are business establishments or public facilities. The 'record" of every business for all cross-roads are entered into a statistical dataset (or spreadsheet). The data are manipulated until a Guttman-scale type of pattern is found (see Table 1).

Figure 1. Commercial Services Scale, Rural Communities of Cayuga County, 1996.

The Unique Boundaries for the Scale Pattern are Denoted by the Shading;
"Holes" are Above the Scale Boundary, and "Errors" Below It.

Interpretation

The patterned array provides a way for probing a deeper understanding of local commercial services. Figure 1 shows, among other things, a 'step-wise solid line.' This line is the 'boundary' of the Commercial Services 'scale score.' Output from a computer program using the services data collected by businesspersons driving the area determines this line and score.

The line-score is important because we know from previous studies that approximately two-thirds of the 'holes' (a blank in a cell above the 'scale line' where we 'expect' one or more services) will 'fill in' in the next 5 years; and three-quarters of the 'errors' (a number in a cell where we 'expect' a blank) will go out of business in the next 5 years.

Policies from the Predictions

Figure 1's pattern, then, 'predicts' that businesses develop in a knowable pattern; thus the pattern has local policy implications. 'Loss' of a business due to being an 'error' represents loss in access to the service, as well as loss in jobs, income, and assessed tax value to the community. 'Gain' of a service due to filling a 'hole' represents convenience of access to the service for the community's citizens, as well as jobs, income, and tax value.

The pattern exists because community services are largely functions of communities' population sizes and their citizens' income levels. The pattern also implies four Policy Jobs for community developers:

1) to assess whether a 'hole' is a real business opportunity (e.g. that no competition exists nearby);

2) to find resources (e.g. dollars as well as a trained entrepreneur) to invest in the 'opportunity';

3) to determine whether the 'errors' are in actual danger of going out of business; and

4) to determine means of keeping endangered businesses open (e.g. in terms of business-management training or in having a business move to a more viable location in order stay open).

Paul Eberts is a Professor in the Department of Rural Sociology at Cornell University.