Rotary Membership Analysis 7: Targeting Geographic Areas for Growth

by Quentin Wodon

Service clubs that articulate a great value proposition should be able to grow. At the club level and even more so at the district and zone levels, it makes sense to target public relations and marketing efforts to geographic areas with the largest potential for growth. How can such areas be identified? This post suggests one way to do so. The approach involves three steps: (1) Calculating membership rates; (2) Estimating expected membership rates; and (3) Computing potential membership gains from shifting low performing areas to their expected membership rate (for details, as for other posts in this series on membership analysis, please see my book on Rotary).

Calculating Membership Rates

Most Rotarians are well-to-do. A simple way to calculate membership rates by geographic area consists therefore in dividing the number of Rotarians in an area by the number of well-to-do households. A proxy for well-to-do households is the number of households with incomes above a certain threshold in an area. There is no income eligibility threshold to become a Rotarian, but since as discussed earlier in this series membership is not free, it is reasonable to assume that membership will be observed mostly among households with relatively high incomes.

In a series of briefs that I wrote on districts in zone 33 (see the briefs & papers page on this blog), I computed membership rates by area in the zone considering households with incomes above US$100,000 per year as the reference group (income data were obtained from the American Community Survey of the US Census Bureau). The same income threshold was used for all geographic areas except those in districts 7610 and 7620 where income levels and the cost of living are especially high. In those two districts I considered an income threshold of $150,000. The geographic areas were counties and large (administratively autonomous) cities. The average membership rate in the various districts in zone 33 turned out to be 2.8 percent. In other words, for every 100 high income households there were on average three members of Rotary in a typical district.

Estimating Expected Membership Rates

Membership rates are however not by themselves good measures of how well different areas and districts are performing in terms of their ability to attract members. This is because there is a strong negative relationship between the number of high income households in an area and the corresponding membership rate. In the figure below each dot represents an area (a county or autonomous city) within zone 33. Membership rates on the vertical axis tend to be lower in areas with a larger number of high income households on the horizontal axis (in logarithm to reduce the influence of extreme values). The negative relationship is statistically significant. The red dot line represents the expected membership rate given an area’s high income population. By construction about half of the geographic areas have membership rates above expectations while the other half has membership rates below expectations.

Membership Rates

Why is there a negative relationship between membership rates and the concentration of high income households in an area? Several explanations could be suggested. In areas with many high income households, work pressures and time availability to participate in Rotary may be more constrained. The prestige associated with membership may also be lower in those areas, and opportunities to be involved in service work through other organizations may be more numerous. Whatever the causes of the negative relationship, it should not be ignored when estimating expected membership rates by area, and thereby in assessing whether various geographic areas are performing above or below expectations.

Computing Potential Membership Gains

The next step in the analysis consisted in simulating potential gains in membership by area from raising the membership rates of the areas whose rate was below expectations. Two simulation scenarios were conducted. First, all areas with a lower membership rate than expected rate were assumed to be able to reach the expected rate. Second, only half of the gap between actual and expected membership rates was eliminated for areas with lower than expected rates. In both simulations the areas with a higher membership rate than the expected rate kept their membership rate constant(no gain in membership under the two scenarios).

Overall, in zone 33, membership (using 2010 data) was simulated to increase from 36,539 to 47,436 under the first scenario, and to 43,205 under the second scenario. The exercise suggested substantial potential for growth. In addition, it also suggested geographic areas that could be targeted for growth by districts and the zone. The simulations identified areas with strong potential for growth because they combined a comparatively low membership rate (versus the expected rate) and a substantial number of high income households. In my district for example, the analysis suggested that there was substantial potential for growth in five top areas: Montgomery County, Price Georges County, Baltimore County, the District of Columbia, and the city of Baltimore.

This type of analysis and simulations should be considered as indicative only. Alternative modeling approaches could be used to calculate membership rates and assess the membership growth potential of geographic areas. Each alternative method would yield different results. Still, the point being made here is that it does make sense for districts and for zones, as well as for clubs in some cases, to identify promising geographic areas for growth so that this information can be used together with other relevant factors for designing and implementing coherent and targeted membership growth strategies.

Note: This post is part of a series of 10 on Rotary Membership Analysis. The posts with links are as follows: 1) Introduction, 2) The Challenge; 3) Why Do members Join?; 4) Volunteer Time; 5) Giving and the Cost of Membership; 6) What Works Well and What Could Be Improved; 7) Targeting Geographic Areas for Growth; 8) Initiatives to Recruit Members; 9) Fundraising Events; and 10) Telling Our Story.

One thought on “Rotary Membership Analysis 7: Targeting Geographic Areas for Growth

  1. Dan,

    From the “value proposition” language to his population analysis, I think you will enjoy reading this guy’s work. He is a frequent e-mailer, but he definitely seems to me to know what he’s talking about.

    As you know from our short discussion at the District Team Training Seminar, I’ve been talking for some time about segmenting our district into at least three areas for purposes of public relations targeted at attracting new members: Burlington, Upper Valley and Quebec. There are others, but these are the population centers.

    I love the idea of analyzing households with incomes $100,000+ vs. percentage of membership as Quentin suggests in his article below. It’s dense to me, but probably a quick enough read for you. You can subscribe if you wish.


    Bruce Pacht

    51 Mascoma Street

    Lebanon, NH 03766-2642

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