Abstract
We contend that political context is important to consider when analyzing social capital and that context has an important but neglected impact on understanding the consequences of civic activity. Our focus is on the influence of rural, local leadership in two Minnesota communities and policies that these elites have developed to bring Internet connectivity to their citizens. One city developed a community electronic network and the other opted for an individualistic, entrepreneurial approach to information technology. Using a quasi-experimental research design and four-wave panel data, we find that elite policy approaches interact with civic activity to predict technology use among citizens, even long after the policies’ initial implementation. In the city with a community network, residents who are integrated into civic life are able to harness these political resources to become more technologically sophisticated.
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Notes
For instance, when the public computer lab opened at the Native American elementary school, the school distributed fliers throughout the community. Focus group meetings with parents of Native American children indicate that the parents learned about the program through their children, through their children’s teachers, and through their involvement in the local PTA. Teachers reported hearing about the program at local school board meetings, through the district’s technology committee, and in the local newspaper.
Data and analyses from the Congressional Budget Office, as well as major think tanks like the Economic Policy Institute and the Center on Budget and Priorities, have all tracked a rise in income inequality since the 1970s. According to the Congressional Budget Office, over the past 20 years the income of the top 1% has risen by 200%, the income of the middle fifth has risen by 15%, and the income of the bottom fifth has risen by only 9%.
The project was later renamed ItascaNet (after Itasca County) as the project began providing Internet access to the surrounding county.
Project organizers specify this goal in their literature. For instance, GrandNet’s 1998 website featured the following quotation from the Report of the Commission on Freedom and Equality of Access to Information: “Knowledge is power. How freely and how equally citizens have access to knowledge determines how freely and how equally they can share in the governing of our nation and in the work and rewards of our society.”
DATANET is an online information system maintained by the State of Minnesota’s Land Management Information Center. Its website is www.lmic.state.mn.us.
See Appendix 1.
See Appendix 2, Table 8.
See Appendix 2, Table 9.
MCSR did not conduct the first data collection in 1997 but did conduct all subsequent collections.
In order to evaluate how well our sample represents populations of Grand Rapids and Detroit Lakes, we compared our respondents’ demographic characteristics to the 1990 U.S. Census. In both communities, our respondents were better educated and had higher incomes: 39.5% of Grand Rapids respondents and 37.4% of Detroit Lakes respondents held a college degree (or higher), compared to 14.4% of Grand Rapids residents and 15.9% of Detroit Lakes residents. Moreover, 60.5% of Grand Rapids respondents and 50.8% of Detroit Lakes respondents had household incomes of $35,000 or higher, compared to 29.8% of Grand Rapids residents and 21.9% of Detroit Lakes residents. There is certainly a socio-economic bias in our samples, but the level of bias is similar in both cities and should not affect our ability to draw valid comparisons.
The survey we conducted in 1999 differs slightly from the other survey rounds. In 1997, our research team sent questionnaires to a random sample of households in Grand Rapids and Detroit Lakes in order to gather baseline data for these two communities. In 1999, we conducted a follow-up survey of households in these two cities, sending questionnaires to those who responded in 1997 and to a new group of residents in each city. Due to a random data collection error regarding identification numbering for the Detroit Lakes panel list, some of the respondents from whom we received questionnaires in 1997 were not sent questionnaires in 1999. To remedy this problem, we sent questionnaires to the 1997 households that had been missed in the 1999 round. This round of data collection we term “Time 2 \(\frac{1}{2}\)” was conducted from September 22 to November 27, 2000 (mailing and data collection for the original round we term “Time 2” were conducted from October 28, 1999 to February 7, 2000).
Note that the data collection error resulted from a problem with our identification number variable, and it had nothing methodic to do with other variables in our dataset. Because the data collection error was random and only a short span of time elapsed between the two surveys, the groups do not differ systematically. In extensive tests, we find that there are no statistically significant differences between respondents surveyed in 1999 and those a bit later in 2000. We tested for differences between the two groups on variables we measure in 1997, as well as those we measure in 1999. Based on our results, we are confident in our ability to pool respondents from Time 2 and Time 2 \(\frac{1}{2}\).
If the respondent left the item blank, we scored the item 0.
The assumption of sphericity is important to repeated measures ANOVA, and we find that our analyses do not significantly violate this assumption. This assumption states that variances at each time period of the repeated variable are equal and that the covariance (and hence correlations) between each time period of the repeated variable are equal. In addition, these variances and covariances must be the same for each level of the between-subjects variable. In analyses with several time periods, this assumption is difficult to meet. When the sphericity assumption is not met, this method is biased and tends to produce statistically significant results that would disappear if the assumption were met. According to Weinfurt (2000), scholars can measure the degree to which this assumption is violated by calculating the value of “epsilon” (e). A value of e = 1.0 indicates the assumption of sphericity is met and a value of e = 1 /(k−1) where k equals the number of within subjects levels indicates the worst possible violation of sphericity. In the present study, the worst violation of sphericity would be when epsilon e = .50 for three time periods and e = .33 for four time periods. In our analysis, epsilon was equal to .73 when analyzing Internet use, .93 when analyzing public computer use, .99 when analyzing electronic network awareness, .91 when analyzing community attitudes, and 1.00 when analyzing public information access. In the following analyses, we use a slightly more conservative estimator of epsilon, the Greenhouse-Geisser estimate. This estimate helps us to adjust the F tests accordingly (Weinfurt, 2000).
A natural question raised by one reviewer is whether, in these communities, community attitudes impacted Internet use or vice versa? A preliminary analysis showed that community attitudes consistently correlate with civic membership and civic membership correlates with Internet use. There are almost no correlations between community attitudes and Internet use in either community. Hence, if there is a relationship between community attitudes and Internet use, it is likely only an indirect one operating through civic membership.
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Acknowledgments
The authors would like to thank Greg Belshe, Libby Dresel, Amy Gangl, Monica Schneider and Marc Wagoner for their research assistance. We would also like to thank Jamie Druckman and Chris Federico for helpful comments on earlier drafts of this paper. In addition, we extend our thanks to Frank Allen, Larry Buboltz, Ben Hawkins, Sandy Layman, Milda Hedblom, and the communities of Grand Rapids and Detroit Lakes, Minnesota, for their cooperation.
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This research was supported in part by grants from the Center for Urban and Regional Affairs at the University of Minnesota, NSF Grant #SBR9619147, funding from the University of Minnesota’s College of Liberal Arts to Eugene Borgida and John L. Sullivan, assistance from the Carlson Chair in American Politics to John L. Sullivan and assistance from the Fester-Lampert Chair in Urban and Regional Affairs to Eugene Borgida. Alina Oxendine was supported in part by a 2003–2004 Rural Poverty Research Institute Dissertation Fellowship and a 2004–2005 Doctoral Dissertation Fellowship from the University of Minnesota Graduate School.
Appendices
Appendix 1: Selecting Comparison Communities
The following table illustrates the variables used to select a control community for Itasca County, home of Grand Rapids (Sullivan et al., 2002b). The article’s appendix explains, “...[We] performed a cluster analysis of all Minnesota counties using the variables listed in the table below. We standardized the data values using z-score transformations, used squared Euclidean distances as the proximity measure, and used the average linkage between groups as the clustering method. The first time Itasca County was placed in a cluster was when it was added to one that already contained two counties: Becker and Carleton. That cluster later added six more counties, but the core of the cluster was Becker, Carleton and Itasca Counties.” The research group finally chose Becker County, home of Detroit Lakes, as the comparison community for Itasca County, home of Grand Rapids.
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Oxendine, A., Sullivan, J.L., Borgida, E. et al. The Importance of Political Context for Understanding Civic Engagement: A Longitudinal Analysis. Polit Behav 29, 31–67 (2007). https://doi.org/10.1007/s11109-006-9016-3
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DOI: https://doi.org/10.1007/s11109-006-9016-3