Since August 2006, a team of graduate students at Carnegie Mellon University’s Heinz School of Public Policy and Management has been researching the relationship between social innovation and economic development in the United States. We all know that there are qualitative examples of social innovation affecting economic development; we sought to add quantitative analysis to this important yet so far rather qualitative field in the hopes that our research can generate new knowledge for social entrepreneurs, foundations, governments, and investors to apply. For example, if we can identify the factors that create social innovation in a region, and can demonstrate a relationship between social innovation and economic development, then regions can identify which factors to invest in to thereby drive economic development. Simultaneously, a social entrepreneur deciding where to start a social enterprise can use the factors to identify which regions have the most fertile ground for social innovation.
We could not have done this alone. We have been fortunate to have consulted with the experts above and have incorporated their suggestions into our research, including statistics experts confirming our methods. We greatly appreciate their time and information.
There are many different schools of thought about defining social innovation and social entrepreneurship. Experts we consulted with recommended that we choose a definition that will work within our research parameters. We wanted our definition to include the Jerr Boschee, Greg Dees, and now the Sally Osberg, schools of thought. Although our definition above says that social innovations are organizations in any sector , given the limited time and the available resources more focused on nonprofits, the current version of our research focused on the non-profit sector. Our future research will include public and private organizations, as well as partnerships.
Using that definition of social innovation, we sought to demonstrate its relationship with economic development (e.g., median household income, number of people unemployed in a region). But how do you measure social innovation? We collected data for over 118 factors that could possibly influence social innovation (e.g., number of patents in a region, demographics ranging from voter breakdown to age breakdown, social needs in an area, education level, etc.) If we could demonstrate a statistical relationship between some of these factors and social innovation, and also demonstrate a significant relationship between social innovation and economic development, then policy makers and economic developers, for example, could invest in factors to affect social innovation and therefore to affect economic development. (note: this slide is animated)
This slide describes the three methods we employed to measure the relationship between social innovation and economic development?
In order to determine the number of socially innovative organizations in each city, we created a database of every socially innovative organization we could find (utilizing our definition as a parameter). We scoured different web sites and well-respected sources (listed above) and have a growing database of over 800 socially innovative organizations. This 800 number is worth thinking about for a moment. There are 1.4 million nonprofit organizations in the United States, and let’s say our database has half the organizations that it should have. That still implies that only 1600/1.4 million, or .11% of nonprofits are socially innovative. Is that number accurate? What are the implications? Is it important to increase the number of socially innovative organizations and if so, how do we do so?
After collecting the 800 organizations, we organized them by city region to obtain counts of how many organizations were in each city. This slide shows the range. The next slides show this information in map forms to analyze patterns.
This map displays the counts of organizations and supporting organizations. Notice areas where there are significantly higher counts of socially innovative organizations. Then notice the concentration of socially innovative organizations in the rust-belt area (east of Missouri, with high concentrations of brown circles that indicate organization count of 41-60). The rust-belt area produced significant wealth during the times when the manufacturing sector was responsible for most of the U.S.’s economic activities. Since the decline of the manufacturing sector, there has been increasing pockets of poverty and an increased need to revitalize this area. The concentration of socially innovative organizations in this area is believed to be caused by a higher need for creative solutions to social problems than other areas.
The population was divided by the total number of organizations and supporting organizations to come up with the number of people per organization. This is to determine if larger cities had a significantly lower ratio of population to organization. As can be seen, there is no great variation outside of Phoenix, Arizona (the one immediately to the right of Philadelphia.) Please note that only a selection of cities in alphabetical order are displayed.
Development life cycles: Boston began redevelopment from heavy industry (textiles) in the late eighties, to a center of high tech and life sciences. Seattle’s redevelopment began in the early 1990s based around computer-related technologies, and Pittsburgh began it’s redevelopment around the same period focusing on health care, banking, and high tech advancements. Pittsburgh and Boston have important similarities in that they are both former centers of heavy industry. Similarly, Boston and Seattle are both coastal cities and therefore retain some similarities with regard to trade and fisheries industries. Finally, while all three are in slightly different developmental periods, they are all bases for large foundation and non-profit communities.
A zoomed view of the sample and control block group sections. Block groups are the smallest census level that provide all the information from the long-form census sheet.
Control groups – Dorchester area One can see that there is positive change over the 10 year period, and that the majority of block groups have jumped 1 income level – from the green level to the light blue level.
Sample group – downtown (Faneil Hall area): As compared to the control group, the block groups in the sample area have made significant changes in median household income, in many cases jumping 2 or more income levels (from yellow to royal blue) in the same amount of time as the control group. While we would not want to assume causation, nor is there a method that can prove causation with the presence of socially innovative organizations, there may be a strong a correlation with their location. Perhaps they were able to meet needs that the city was not, allowing the city to spend more time and money on other developmental efforts in this area, thus leading to increases in median household income.
The sample groups (groups with clusters of socially innovative organizations) for all three hubs exhibited significantly higher population growth than for the control groups, their city averages, the US average or the urbanized area average for the US. Increases in population can lead to increased vibrancy of a community.
The percent change in labor force measures the number of people not in the labor force as a percent of the entire population. These would include elderly people, young people, stay-at-home-parents, or people who had given up looking for work. This graph is important because it shows us that the people moving into the area are in the labor force. In both Boston and Seattle we see the labor force growing for the sample block group areas, and in stark contrast to the control groups, their city averages and the US average as a whole. In Pittsburgh, both the sample and control areas had slight, but negligible increases, also in contrast to the city’s 20% increase or the country’s 30% increase.
All three cities saw drastic increases in median household income for their sample groups over their control groups and city averages. Additionally, every city’s sample group exhibited larger average growth than for US urbanized areas, or the US as a whole.
Socially innovative organizations may serve an important role in economic development. These organizations can meet needs that perhaps city services cannot meet as effectively, suggesting that allocating city resources into these organizations would further development. Therefore, socially innovative organizations should be taken into consideration when cities create long-term development plans. Perhaps there is even room for regions or states to establish grant programs to assist socially innovative organizations as their success would actually save the region more money than the grant program might cost.
In order to provide a tool for social innovators, foundations, and government agencies to use to foster regional social innovation, we examined over 100 regional factors that might drive social innovation. Through research and interviews, the factors selected were those thought to help predict the level of social innovation that would be seen at a city wide level. The factors could then be used to generate a regression equation that our stakeholders could use to predict the level of social innovation a particular city would have. These factors spanned three categories, Ideas; Resources; and Need. Samples of each of the three categories are provided above, (this does not represent the complete list of factors examined). In order to keep population effects out of the data, all numbers are reported in per capita or percentage of the population terms. Note: this slide is animated and is most clear in presentation-screen view
Best subsets regression was used to determine our regression model. Best subsets regression examines every possible combination of the factors that could be used to predict the level of social innovation, and gives a listing of the highest ranking models based on adjusted r-squared. Adjusted r-squared is a measure of how accurate our model is. Our models showed r-squared values of approximately 90%. However, our regression analysis showed that several models had identical r-squared values. In order to select among these models, we employed two other descriptive statistics. The first of these is Mallows C-p, which measures biases associated with the number of predictors in a model. Too many or too few predictors yield a higher Mallows C-p. The ideal is near 0, and our ideal model had a -.3 Mallows C-p. Finally, we examined the sum of the residuals, which represents the total prediction error summed across variables.
This is the actual Fertile Ground Index. You can see in the slide above that the five factors that predict the number of socially innovative organizations are the number of supporting organizations, the Regional Gross Metropolitan Product (GMP), the patent count, the percent Republican, and the foundation count. The number of supporting organization and the foundation count both have positive coefficients, indicating that the more foundations and the more supporting organizations that we see, the more socially innovative organizations we expect to see. GMP, patent count, and percent Republican all have negative coefficients, meaning that the higher these numbers are, the lower the expected number of socially innovative organizations will be. We believe that regional GMP has a negative effect because cities with low GMP are cities in economic trouble and this variable represents the need for socially innovative organizations. Although we were surprised that patent count has a negative effect, one explanation is that it represents resource drain away from social innovation, both in terms of capital and talent. Two possible explanations for the percent Republican having a negative effect include: (1) it could indicate conservative social policies or (2) it could indicate that social innovation is fostered by having high numbers of other political affiliations and diversity within the region. In order to use the FGI, organizations will have to normalize their data by converting it into per capita or percentage of the population measures, insert this data into the equation, and solve it. The FGI is important because it provides a quantitative explanation that challenges qualitative intuitions. For example, many practitioners would expect number of start-ups or foundation dollars per capita or number or quality of universities to affect social innovation, and would therefore invest in those factors to drive social innovation. However, quantitative analyses suggests otherwise. Applying and utilizing the FGI will ensure an efficient use of resources.
The first weakness is that the model only counts 100 cities, which is still a relatively small data set, and as more organizations and cities are added, the predictive ability of the model will increase. Secondly, while the number of organizations shows a fairly scalar increase, there is wide variance in the number of socially innovative organizations across the cities. Third, while the model is fairly accurate, it still has the ability to predict only 90% of the variation in data. The fourth weakness is that there are biases associated with the use of large cities such as New York and Los Angeles in the data set. The fifth weakness is that our data collection method is not perfect. It is very difficult to get an accurate count of the number of organizations within a city. Our last weakness is the scale of the city. Counts can fluctuate based on where the lines around the city are drawn.
Understanding that there is always room for improvement on any project, we have made a number of recommendations for future research to advance this study. The first would be to update the database of socially innovative organizations to include more cities and more socially innovative organizations. As previously mentioned, due to our time constraint, we focused our study primarily on nonprofit organizations that are socially innovative. One suggestion in updating this database would be to include private sector companies that are also socially innovative. Second, to check this model’s applicability in other countries, it would be interesting to apply the model to international cities. If it applies, then it would be helpful to analyze the impact in other countries; if it does not apply, then we will need to determine how to make it applicable to other countries. This recommendation also applies to the economic analysis with additional hubs of social innovation. Fourth, conduct additional qualitative research. Conduct interviews and surveys with social entrepreneurs and social innovators to cross check all outcomes and gain insights into the field from the practitioner’s standpoint. Lastly, to examine the attrition rates in socially innovative cities. What causes the reduction, if any, of the amount of socially innovative cities? Is there a threshold for the maximum number of socially innovative organizations to exist in a hub? Is there a point where this number peaks and more socially innovative organizations will no longer increase the social impact?
Two future directions for this project are (1.) to publish this research to reach target audience for feedback, to foster understanding, and to spread knowledge of the importance of social innovations; and (2.) to provide policy recommendations for our target audiences: Foundations - to urge their support in directing funding for education and promotion of social innovation. Utilizing the FGI, provide them with the understanding and recognition of where funding will have the biggest social impact in their funding jurisdictions; Social Entrepreneurs – utilize the FGI to find the most fertile ground for social innovation or help build upon potential grounds; Researchers – continue to work on this research and find various methods to build towards the intended outcome. Then, compare and contrast for the best model to implement. Also, track and evaluate how the FGI is being utilized and its accuracy overtime and continue to disseminate this research to the intended audiences; Economic Developers – recognize and understand the impact of social innovation on economic development, attract more social innovations when planning revitalization efforts and see social innovators as partners in the cause; and Government Leaders – be educated and educate others on the impact of social innovation and support and build relationships between social innovators, economic developers, and public-private partnerships. Utilizing the FGI, help to create a more fertile seedbed in the region. Also, assist in developing or stimulating the market for social innovation.
Please send any questions to Vivien Luk at firstname.lastname@example.org or to Amy Lazarus at email@example.com
Creating Seedbeds for Social Innovation Advanced Policy Research 2006-2007 H. John Heinz III School of Public Policy and Management Carnegie Mellon University Pittsburgh, Pennsylvania USA
Heather Arnet - Women and Girls Foundation • Terry Beggy - Pittsburgh Social Venture Partners • Marie Coleman – Institute for Social Innovation, Carnegie Mellon University • Diana Bucco - The Forbes Funds • Courtney Ehrlichman - Friendship Development Associates • Colin Lacon - Northern California Grantmakers • Al Mercer – Social Enterprise Accelerator • Elizabeth Lynn - The McCune Foundation • Harold Miller - PittsburghFuture.com • Jerry Paytas - GSP Consulting • P aul Shoemaker – Social Venture Partnership • George Duncan – Heinz School of Public Policy and Management , Carnegie Mellon University • Don Smith – University Partnerships • Richard Florida – George Mason University, The Richard Florida Creative Group • S teven Pedigo – Greater Washington Initiative • Kirsten Gagnaire – Social Enterprise Group • Gregg Behr – The Grable Foundation • Perla Ni – Great Nonprofits, founder of Stanford Social Innovation Review • Denise Rousseau – Institute for Social Innovation, Carnegie Mellon University • Tim Zak – Institute for Social Innovation , Heinz School of Public Policy and Management (Australia) • Tim White – University Partnerships • Donald Bonk – Carnegie Mellon University
Discuss the socially innovative organization database
Correlate social innovation with economic development
Launch the Fertile Ground Index
Share conclusions and facilitate Q & A session
Definition of Social Innovation Social innovations are organizations in any sector that are motivated by a social mission and that are financially sustainable in one or both of the following ways: creating program-generated income or receiving resources from a Support Organization – whose mission is to support and promote Social Innovation (e.g., Ashoka, Echoing Green, foundations, Venture Philanthropic Partners)
Social innovation’s impact on the economy Economic Development Can we demonstrate this relationship? Social Innovation Factors Can we demonstrate this relationship?
Measuring the relationship between social innovation and economic development
Build the database of socially innovative organizations
Analyze the impact of social innovation on a region’s economic development
Create the Fertile Ground Index – a model that indicates a region’s potential for social innovation
Discuss the socially innovative organization database
Correlate social innovation with economic development
Launch the Fertile Ground Index
Share conclusions and facilitate Q & A session
Building the database of socially innovative organizations
Center for the Advancement of Social Entrepreneurship
Common Good Ventures Nonprofit Partnerships
Draper Richards Foundation
Fast Company Social Capitalist Award 2005-2007
REDF (The Roberts Enterprise Development Fund)
Social Enterprise Alliance/Community Wealth Ventures
Social Enterprise Accelerator
Social Enterprise Group
Social Innovation Forum
Social Venture Network
Venture Philanthropy Partners
Yale-Goldman Sachs Business Plan Competition for Nonprofit Organizations 2002-2005
- Over 800 organizations in the United States - Sources include:
Ranking of cities in increasing count of socially innovative organizations Foundations only funding socially innovative organizations Networks only supporting socially innovative organizations Akron, OH Arlington, TX Aurora, CO Birmingham, AL Honolulu CDP, HI Mesa, AZ Oklahoma City, OK Omaha, NE Rocky Mount, NC San Antonio, TX Savannah, GA St. Petersburg, FL Stockton, CA Toledo, OH Tulsa, OK Wichita, KS Anchorage, AL Buffalo, NY Durham, NC Jacksonville, FL Jersey City, NJ Las Vegas, CA Lexington, KY Long Beach, CA Phoenix, AZ Rochester, NY Tallahassee, FL Bethesda, MD Charlotte, NC Colorado Springs, CO Concord, CA Dallas, TX El Paso, TX Nashville, TN Raleigh, NC Sacramento, CA San Jose, CA Erie, PA Indianapolis, IN Louisville, KY Memphis, TN Miami, FL San Diego, CA Santa Clara, CA Santa Cruz, CA Albuquerque, NM Columbus, OH Detroit, MI New Haven, CT Newark, NJ St. Paul, MN Tampa, FL Tucson, AZ Houston, TX New Orleans, LA Palo Alto, CA Austin, TX Berkeley, CA Cambridge, MA Denver, CO Cincinnati, OH Portland, OR Kansas City, MO St. Louis, MO Baltimore, MD Oakland, CA Arlington, VA Philadelphia, PA Los Angeles, CA Minneapolis, MN Portland, OR Alexandria, VA Milwaukee, WI Cleveland, OH Atlanta, GA Seattle, WA Pittsburgh, PA Chicago, IL Washington, DC Boston, MA San Francisco, CA New York, NY Total: 82 0 1-10 11-20 21-40 41-70
Support education and promotion of social innovation. Utilizing the FGI, understand and recognize where funding will have the biggest impact in the region.
Utilize the FGI when assessing region to start social innovation.
Update this research via various methods. Track and evaluate applications of the FGI and continually update the data and disseminate updated information.
Attract more social innovations in revitalization efforts, see social innovators as partners.
Support local social innovators and economic developers and help facilitate relationship between the two. Create a more fertile seedbed in the region utilizing the FGI and investing in the appropriate factors.