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Individuals and Communities: the Importance of Neighbors Volunteering

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Abstract

In this analysis, I examine the effects of community-level volunteering on an individual’s choices regarding time – whether to work and whether to volunteer. In order to better explain the decision to volunteer, a classic pure public goods structure is contrasted with a less restrictive impure public goods model that admits other possible private motivations. The results of this study undermine the neoclassical notion that volunteering can be understood solely as a pure public good that is provided less when others are seen to be contributing. In fact, individuals are found to be more, not less, likely to volunteer when others in their communities do so. An innovative instrumental variables strategy is used to account for reflection bias and the possible endogeneity caused by selective sorting of individuals into neighborhoods, which allows for a causal interpretation of these results. Employment regressions provide preliminary evidence that average volunteering relates, to some extent, with the decision of whether to participate in the labor force. Variations in the effect of average volunteering across age and gender are also explored. The present work is unique by virtue of its use of a large and representative dataset, along with rigorous statistical testing. I use United States Census 2000 Summary File 3 and Current Population Survey (CPS) 2004–2007 September Supplement file data and control for various individual and community-level characteristics.

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Notes

  1. See for example Taniguchi and Tomas’s 2011 study detailing how religious volunteering is related to non-religious volunteering, and can be explained by religious feeling motivations and the desire for religious inclusiveness.

  2. The investment framework has, unsurprisingly, been quite popular among economists as a private value structure. For example, Day and Devlin (1997; 1998) found, for a sample of individuals in Canada, that the wage returns to volunteering were greater for men than for women. Framing the decision to volunteer as a decision to ultimately build human capital is in keeping with a more neoclassical approach. It is also true that the present analysis compares volunteering’s impact on labor supply as stratified by age and gender in the spirit of Day and Devlin’s classic work.

  3. While these regressions were not used in the final analysis, I initially employed a yearly breakdown to discern additional patterns in the data. The yearly approach is retained for the summary statistics to show that patterns of volunteering and other characteristics are relatively consistent over time in the dataset.

  4. While the Probit model may be superior in terms of variance, the linearized model will be preferred in recovering the causal effect (Angrist 1991, 2001). See also Angrist and Pischke (2009) for an in-depth discussion of this topic and the problems associated with employing limited dependent variables in particular contexts.

  5. The standard probability weighting procedure was used, with observations weighted in the regression structure based upon their likelihood of being included in the sample. Further explanation on these and other weighting procedures is provided in Kreuter and Valliant (2007).

  6. I have opted here for a more formal test of the exclusion restriction rather than the more typical informal tests of the correlation between the instrument and observables lending evidence to the relationship with unmeasured covariates—See Bifulco et al. (2011) for an example of this methodology. It is also true that, given the lagged nature of the instrument, the tests employed here are more appropriate.

  7. Appendix Table 6 further rejects the null of exogeneity at the 1 % level to provide further evidence in this regard. Regressions (not shown) were run to replicate Table 4 using the Probit structure, once again showing a similar pattern of results and rejecting the null of exogeneity at the 1 % level.

  8. The baseline OLS calculations referred to here are those for the top right portion of Table 3.

  9. To be exact, the instrument should decrease sorting based on the preference for the public good, as well as highlighting the public good motivation. It has been assumed thus far that the sorting will be positively associated with an individual’s likelihood of giving, however, this may not strictly be correct, since individuals who are unwilling to volunteer may be more likely to sort into areas with high levels of public goods. This is mentioned as a possibility that would also be in keeping with the current results.

  10. I used a standard test for the difference of coefficients across regressions (Clogg Petkova Haritou 1995) to construct the Z of the regression. It is essentially \( Z=\frac{\beta_1-{\beta}_2}{\sqrt{{\left(SE{\beta}_1\right)}^2+{\left(SE{\beta}_2\right)}^2}} \)

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Acknowledgments

I would like to thank Rebecca Blank, David Card, Daniel Hamermesh, Esteban Jaimovic, Theo Koutmeridis, Dong Li, James Powell, and especially Giovanni Mastrobuoni as well as the editor and anonymous referees for many helpful comments and suggestions. Most of all, however, I would like to thank Theo Diasakos for his help with this work. I am also grateful to Urmimala Sen for outstanding research assistance. Previous versions of this paper were presented at the Meetings of the Southern, Western, and Midwest Economics Associations. This research was supported by an NSF ADVANCE research grant. All mistakes are my own.

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  1. Department of Finance and Economics, Nova Southeastern University, 3301 College Ave., Fort Lauderdale, FL, 33314, USA

    Florence Neymotin

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Data Appendix: Variable Descriptions

Data Appendix: Variable Descriptions

Volunteering

Three types of volunteering were defined in this study. An individual volunteered either (1) generally, (2) for a religious organization, or (3) for any organization EXCEPT a religious organization. The main measure used for the analysis was the first definition, with a focus on the last two mostly in the instrumental variables regressions. The number of hours volunteered was used as part of a robustness check. The average level of volunteering in a community was the “rate” for that area. The area rate of volunteering was also separated into religious and non-religious variables. At certain points in the analysis, the area’s rate of volunteering 2 years prior to an individual volunteering was used as a time-lagged variable, as explained in the text.

Demographics

The demographic variables used in this research were: age measured in years, gender (male = 1, female = 0), Booleans for race (White, Black, American Indian, Hispanic, Asian), education Booleans by years and levels of education, income Booleans measured in categories, Booleans for marital structure (married with a spouse or without one, widowed, divorced, separated, never married), Booleans for the presence of children (0–2 years old, 3–5 years old, 6–13 years old, 14–17 years old), and the number of individuals living in the household,

Regarding employment status: an individual was either employed, unemployed, or not in the labor force. This measure was inserted as three Booleans in the individual volunteering regressions. As explained in the text, the employment regressions were run two ways. First, = 1 if the individual was employed, and = 0 if either unemployed or not in the labor force; Second, = 1 if the individual was employed or = 0 if unemployed and = . (omitted) if not in the labor force. Finally, the labor force participation regressions were run contrasting individuals who were in the labor force at all, employed or unemployed =1, and = 0 if not in the labor force.

CBSA-Level Variables

Variables relating to each Census Core Based Statistical Area were: The number of people living in the area, the fraction of the total area that is urban (versus rural), the fraction of people in the area of different (self-reported) racial backgrounds (Black, Indian, Asian, Pacific Islander, Hispanic), and average per-capita income. Additionally, income and total population were used in a linear, as well as in a squared format, and income was inflation-adjusted using the Consumer Price Index (CPI).

Table 6 Relationship with area effects
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Table 7 Model C OLS and IV regressions
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Neymotin, F. Individuals and Communities: the Importance of Neighbors Volunteering. J Labor Res 37, 149–178 (2016). https://doi.org/10.1007/s12122-016-9225-4

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