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Changing state interest group systems: replicating and extending the ESA model

Abstract

The ESA model adapted for interest group research by Gray and Lowery (1996) based on population ecology theory has become perhaps the most widely used framework for studying interest groups in the American states. Yet though replication is a hallmark of scientific research, and while it is important to verify a model’s fundamental assumptions before expanding on it, there has been little replication of the energy-stability-area (ESA) model with new, but comparable, data. Using new data on state interest groups from 2006 to 2017, I replicate their early analyses. While some of their findings do not hold up, many do, including the density dependence effect, which I further explore with new variables. I then extend ESA by exploring interactions between its component variables. Finally, I propose a new term for the model, the government’s capacity to support new policies as an influence on group formation and find it is empirically supported.

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Notes

  1. 1.

    The idea is that at lower, and even mid-range, values, GSP should have a positive linear effect, but at higher values, captured by the square of the variable, the effect should be negative. Thus the analysis captures a nonlinear effect for use in a linear model where the effect initially curves up, plateaus, and then starts to curve back down.

  2. 2.

    Interestingly, Gray and Lowery equate tests of Stability, as measured by the years since statehood, as a test of the hypothesis advanced long ago by Olson (1982), who claimed that interest groups increase in number over time until their demands become so numerous that they effectively paralyze the government. Since Olson’s hypothesis has not received much empirical support (see Unger and van Waarden 1999), I do not define Stability this way.

  3. 3.

    For information on the Institute, see http://www.followthemoney.org/.

  4. 4.

    Data and supporting information is at https://thomasholyoke.com/home/peer-reviewed-journal-articles/.

  5. 5.

    GSP data is at https://www.bea.gov/data/gdp/gdp-state. To prevent vanishingly small coefficients, it is divided by 100, so its mean is 3.16, its standard deviation is 3.92, and it ranges from 0.24 to 28.02.

  6. 6.

    Ranney Index for 2006 to 2012 is from Carl Klarner at https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:1902.1/22519. Later data is from Holbrook and Raja (2018); since there is no new data for 2017, the 2016 data is repeated. Mean is 0.86, standard deviation is 0.09, and it ranges from 0.66 to 0.99 where higher values mean greater party competition and lower values mean single-party dominance.

  7. 7.

    Total lobbying organizations ranges from 99 to 4,233 with a mean of 1,076 and standard deviation of 816. Since in some models lobbying organizations divided by GSP produced coefficients that were essentially 0, it was then divided by 100, so now mean = 533, S.D. = 299, ranging from 66 to 1,826.

  8. 8.

    As Gray and Lowery did, I also estimated individual models for each year, but since they do not produce different results, the results are not presented here (but are available on request).

  9. 9.

    The sectors are agriculture, communication, construction, general business (including manufacturing and retail), education, electronics, energy, entertainment, finance and insurance, health, law, leisure, real estate, social services, and transportation.

  10. 10.

    These results are available in the online appendix (https://thomasholyoke.com/home/peer-reviewed-journal-articles/).

  11. 11.

    From the U.S. Census Bureau. Results are in the online appendix (https://thomasholyoke.com/home/peer-reviewed-journal-articles/).

  12. 12.

    State population is from the Census Bureau. Because population numbers are very large relative to the dependent variable, the coefficients are essentially 0, so I divided it by 1,000,000. Its mean is now 6.17, the standard deviation is 6.78, and it ranges from 0.51 to 39.36.

  13. 13.

    From annual editions of Fiscal Survey of the States published by the National Association of State Budget Officers. It is divided by population for a per capita measure, and then multiplied by 1,000 to prevent very small coefficients. The mean is 2.61, the standard deviation is 1.70, and it ranges from 0.87 to 28.05.

  14. 14.

    From the Census Bureau and is divided by state population for a per capita measure. Because this made the measure very small, I multiplied it by 1 billion. The mean is 4028, its standard deviation is 2755, and ranges from 751 to 41,565.

  15. 15.

    The mean is 417, the standard deviation is 242, and it ranges from 47 to 1293.

  16. 16.

    Results are in the online appendix.

  17. 17.

    From Fiscal Survey of the States and standardized to year 2000 dollars. It is divided by population, but then multiplied by 10,000 to make the coefficients readable. Mean is 0.25, standard deviation is 0.17, and it ranges from 0.07 to 2.76.

  18. 18.

    Data comes from the National Institute for Education Statistics at the U.S. Department of Education. It is divided by population and multiplied by 100. Its mean is 0.60, standard deviation is 0.23, and ranges from 0.22 to 4.35.

  19. 19.

    See (Berry et al. 1998). The data is at https://rcfording.com/state-ideology-data/. Since they have not yet estimated 2017 data, I repeat the 2016 data. To produce more meaningful coefficients, the measure is divided by 100. The mean is 0.48, the standard deviation is 0.27, and it ranges from 0.03 to 0.97.

  20. 20.

    Results are in the online appendix.

  21. 21.

    Results are in the online appendix.

  22. 22.

    The coding was done by Holyoke (2019).

  23. 23.

    The associations dependent variable’s mean is 297, its standard deviation is 170, and it ranges from 38 to 950. For change in citizen / public interest groups, the mean is 0.08, standard deviation is 0.75, and ranges from − 0.93 to 14.58. It is also worth noting that these results are largely the same when simply using a one year lag of the number of citizen and public interest groups.

  24. 24.

    While research by Kattelman (2015) finds a connection between legislative professionalism and interest group growth, Berkman (2001) came to the opposite conclusion, arguing that there is density dependence here because very professionalized legislatures have little need for interest group information and this depresses group formation.

  25. 25.

    This comes from annual editions of the Book of the States.

  26. 26.

    At https://www.aei.org/wp-content/uploads/2017/09/Kallen-WP-Sept-217.pdf.

  27. 27.

    The eigenvalue of the factor is 2.53. Legislative professionalism loads at 0.64, state employees at 0.75, state revenue at 0.84, Democratic legislators at 0.53, governor’s party at 0.38, governor’s budget powers at 0.24, and state government ideology at 0.61. Its mean is 0.01, standard deviation is 0.98, and it ranges from − 1.48 to 4.98.

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Holyoke, T.T. Changing state interest group systems: replicating and extending the ESA model. Int Groups Adv 10, 264–285 (2021). https://doi.org/10.1057/s41309-021-00127-y

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Keyword

  • Interest groups
  • ESA
  • Lobbying
  • State politics
  • Mobilization
  • Population ecology