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Special Interest Groups and Economic Growth in the United States

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Abstract

Using a direct measure of special interest group (SIG) strength from Thomas and Hrebenar, I analyze the effects of SIGs on economic growth across 48 contiguous US states. Thomas and Hrebenar categorize the strength of SIGs in each state into five categories: dominant, dominant/complementary, complementary, complementary/subordinate, and subordinate. I find a negative relationship between the SIG strength and economic growth supporting Olson. Holding everything else constant, the growth rate of median income over a decade is almost 12 percentage points lower in states in which SIGs are dominant than it is in states in which interest groups are complementary/subordinate. The results are robust to endogeneity between economic growth and SIG strength.

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Notes

  1. Shughart et al. [2003] analyze the relationship between SIG strength and income inequality.

  2. As there are not any states categorized as subordinate, I actually use four dummy variables.

  3. I would like to thank an anonymous referee for pointing this out.

  4. State convictions data are used to measure corruption in several studies such as Goel and Rich [1989], Fisman and Gatti [2002], Fredriksson et al. [2003], Glaeser and Saks [2006], Dincer [2008], and Apergis et al. [2010].

  5. As I have data from only two time periods, it is not possible to use dynamic panel data estimation. It is not possible to use fixed effects panel data estimation either. Owing to the dynamic nature of the model, state fixed effects are correlated with the error term [Caselli et al. 1996]. Following Barro [2000], I estimate the model with random effects panel data estimation as well. The results are very similar to the OLS estimation.

  6. I would like to thank an anonymous referee for pointing this out.

  7. Shughart et al. [2003] find that, ceteris paribus, the Gini coefficient is approximately 2.5 percentage points higher in states in which SIGs are dominant than it is in states in which they are complementary/subordinate. I estimate the standard linear model without Gini to find out if SIGs affect economic growth via income inequality as well. The magnitude of the estimated coefficients of Dominant, Dominant/Complementary, and Complementary/Subordinate all increase slightly identifying income inequality as a channel via which SIGs affect economic growth. The results are available on request.

  8. Glaeser and Saks [2006], using cross-state data, do not find a significant relationship between Corruption and growth. Nevertheless, they do not test the significance of Corruption2 in their model.

  9. Standardized coefficients are simply the coefficient estimates of a regression after standardizing all variables to have a mean of 0 and a SD of 1.

  10. The correlation coefficients between Thomas and Hrebenar [1999] measure and the Morehouse [1981] measure is higher than 0.6, whereas it is approximately 0.55 between Thomas and Hrebenar [1999] measure and the Sharkansky [1969] measure.

  11. The results are available on request.

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  1. Department of Economics, Illinois State University, Normal, 61790-4200, IL, USA

    Oguzhan Dincer

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Dincer, O. Special Interest Groups and Economic Growth in the United States. Eastern Econ J 38, 434–448 (2012). https://doi.org/10.1057/eej.2011.22

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