What is the Effect of Diaspora on Civil Society? [^1]

The worldwide proliferation of civil society organizations (CSOs), also known as nonprofit or voluntary organizations, has prompted the literature to announce that we are in the midst of a ”global associational revolution.” [^2] One explanation for such an unprecedented development is that world society, defined broadly as a set of rationalized and progressive ideas that became solidified in international institutions in the post-World War II period, has played a powerful role in enabling domestic associational activity. [^3] According to this neoinstitutional argument, CSOs are a product of liberal international influences that simultaneously provide legitimacy for domestic problems and a framework for how to solve them. [^4]

A common operationalization of such global forces is represented by a number of international nongovernmental organization memberships. However, this leaves out a broader spectrum of global stimuli such as international migration patterns. This is particularly relevant in the context of developing countries whose populations tend to emigrate in larger numbers and, hence, consist of a significant number of “external citizens.” [^5] This paper is an attempt to address this gap by focusing specifically on diaspora populations – individuals residing outside their countries of origin who maintain strong ties to their home countries – and its effects on domestic civil society in developing countries.

Diasporas embody components of world culture vis-à-vis their home countries in the form of remittances. This peculiar transfer of resources from host countries to home countries has significantly increased over the last two decades, both in absolute volume as well as relative to other sources of external resources. “According to the World Bank, global remittance flows totaled $706 billion in 2019, with $551 billion flowing to low and middle-income countries. In 2019, India was the top recipient of remittances in US dollar terms ($82bn), while Tonga was the largest recipient relative to the size of their economy (38% of GDP). The US was the top remittance sending country in 2018 ($68bn).” [^6] Also in 2019, remittance flows to low- and middle-income countries surpassed for the first time foreign direct investment and official development assistance received by these countries.

In line with the neoinstitutional approach, augmented to include diaspora philanthropy as a force of world society, the paper proposes the fallowing hypotheses:

H1: Developing countries that receive more diaspora remittances are likely to have a higher number of CSOs.

I am planning to operationalize my dependent variable, the size of the domestic nonprofit sector, as a count of CSOs in a given developing country. The longitudinal data is available via the Gale Group’s Encyclopedia of Associations, which contains information on more than 30,000 domestic CSOs around the world. [^7] I use founding dates to estimate the number of organizations in existence in prior years. The measure reflects the cumulative count of domestic CSOs previously founded. I recognize that this measure is limiting in that it doesn’t differentiate among organizations in terms of their size and impact. Working with budget size or sources of funds would provide for a more granular analysis, but at this point I am unsure if this information is even available.

As for my independent variable, I use diaspora philanthropy which I operationalize as remittances and measure as remittances as a percentage of GDP. I obtain the panel data from KNOMAD [^8], which includes information on remittance inflows for most countries since 1990.

In terms of additional variables, I control for democracy since I expect for diaspora donors to be motivated to donate more to their home countries if those countries are nondemocratic and donate less if they are democratic. I measure democracy by using the Polity IV database. [^9] It assigns countries a score from -10 to 10, from most autocratic to most democratic. I operationalize democracy by creating a dichotomous variable that takes the value of 1 if the Polity score is greater than or equal to 6 and 0 otherwise.

To account for the demographic and macroeconomic factors not directly tied to my theoretical argument but that may nonetheless affect the nonprofit sector size, I control for population and GDP. I obtain the population data from the World Bank and the GDP data from the Penn World Table [^10]. I measure population and GDP as the natural logs of country population and GDP per capita, respectively, to improve model fit.

I am planning to analyze the effect of remittances on the size of the CSO sector in developing countries by using OLS. My dataset includes 100 developing countries with three time points for each country, bringing the total number of observations to 300. I am planning to use bootstrap resampling to get more data that approaches the parent distribution.

While not directly related to the methods section, it is worth noting that remittance inflows are measured at the aggregate/country level rather than at the CSO/community level. Based on the literature review, I am making as assumption that at least some remittances go to CSOs but it is also possible that some are delivered directly to family members. My theory section will have to compensate for this weakness of my model.


[^1] According to Salamon et el., social institutions that operate outside the confines of the market and the state are “known variously as the “nonprofit,” the “voluntary,” the “civil society,” the “third,” or the “independent” sector … [and can refer to] a sometimes bewildering array of entities—hospitals, universities, social clubs, professional organizations, day care centers, environmental groups, family counseling agencies, sports clubs, job training centers, human rights organizations, and many more.” Salamon et al., 1999, pp. 3
[^2] Salamon et al., 1999, pp.4
[^3] Bromley, P. et al., 2018, pp. 529-532
[^4] Schofer, E. and Longhofer, W., 2011: pp. 548-552
[^5] Bauböck, R., 2003, pp. 711-718
[^6] IOM UN Migration (2020): COVID-19 Analytical Snapshot #53: International Remittances UPDATE
[^7] The trial data that I have access to has altogether 10,000 observations.
[^8] The Global Knowledge Partnership on Migration and Development (KNOMAD) is a World Bank-based platform for synthesizing and generating knowledge and policy expertise around migration and development issues.
[^9] Marshall, M. et al., 2013
[^10] Feenstra, R. C. et al., 2015, pp. 3150–3182

References

Bauböck, Rainer (2003): “Towards a Political Theory of Migrant Transnationalism,” The International Migration Review, Vol. 37, No. 3, pp. 700-723

Bromley, Patricia, Evan Schofer and Wesley Longhofer (2018): “Organizing for Education: A Cross-National, Longitudinal Study of Civil Society Organizations and Education Outcomes,” VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, Vol. 29, No. 3, pp. 526-540

Feenstra, R. C., Inklaar, R., & Timmer, M. P. (2015): “The next generation of the Penn World Table,” The American Economic Review, Vol. 105, No. 10, pp. 3150–3182

IOM UN Migration (2020): COVID-19 Analytical Snapshot #53: International Remittances UPDATE, available at: https://www.iom.int/sites/g/files/tmzbdl486/files/documents/covid-19_analytical_snapshot_53_-_international_remittances_update.pdf

Marshall, M., Jaggers, K., & Gurr, T. R. (2013): Polity IV Project: Regime Transitions and Characteristics, 1800–2007; Center for Systemic Peace, Detroit, MI

Salamon, L. M. et al (1999): Global Civil Society. Dimensions of the Nonprofit Sector, Published by Johns Hopkins Center for Civil Society Studies, Baltimore, MD

Schofer, Evan and Longhofer, Wesley (2011): “The Structural Sources of Association,” American Journal of Sociology, Vol. 117, No. 2, pp. 539-585