The economic impact of city–county consolidations: a synthetic control approach


Although more rapid development is a primary motivation behind city–county consolidations, few empirical studies explore the impact of consolidation on economic development. No studies look at government consolidation in the United States using modern causal inference methods. We use the synthetic control method to examine the long-term impact of city–county consolidations on per capita income, population, and employment. The results from the three cases explored indicate that consolidation does not guarantee development and actually can have negative effects. Additionally, consolidation can deepen the urban-rural divide by accelerating the decline of rural populations relative to those of urban areas. The effects vary based upon the county, time horizon and development measure. The results are robust to placebo test simulations and counterfactuals constructed only from counties with earlier failed consolidation attempts. Our results highlight how public choice considerations surrounding the implementation of governmental consolidations are crucial to outcomes and can help inform any subsequent city–county consolidation attempts.

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  1. 1.

    Martin and Schiff (2011) list 39 successful city–county consolidations as of 2011. According to our analysis of the National Association of Counties’ information on “government structure,” that number is now 41. In terms of attempts at consolidation, a search of “city–county consolidation” in Newsbank yields over 11,000 results.

  2. 2.

    For a thorough treatment of the theoretical reasons for and against local government consolidations as well as an analysis of the case-study evidence, see Hall et al. (2018).

  3. 3.

    For readers unfamiliar with the use of nighttime luminosity to measure economic activity, the seminal paper is by Henderson et al. (2012). Digital satellite data exist from 1992 onwards and thus our three case studies are not suitable for the approach used by Egger et al. (2018). However, in the results section, we consider how consolidation affects the urban-rural divide by taking account of the rural-total population ratio.

  4. 4.

    SCM is a generalization of the difference-in-differences model (Abadie and Gardeazabal 2003; Abadie et al. 2010). While previous studies examine growth in consolidated counties to state-wide trends or comparison counties defined in ad hoc ways, the SCM systematically identifies a comparison case by weighting all counties in a state based upon a set of key predictor variables of development.

  5. 5.

    We are indebted to a referee for highlighting that point for us. When using the SCM, the donor pool is a group of similar regions from which the synthetic counterfactual is created but which did not receive the intervention.

  6. 6.

    Martin and McKenzie (1975) present a theoretical model wherein the benefits from consolidation will be captured by the bureaucracy of the newly consolidated government.

  7. 7.

    While we see our exercise as primarily empirical in this paper, we would highlight the deep and rich public choice tradition of Vincent and Elinor Ostrom with respect to the public choice issues surrounding consolidation and jurisdictional fragmentation (Ostrom et al. 1961; Ostrom 2010; Lowery 2013; Aligica 2015).

  8. 8.

    Our discussion here is only about the literature on the economic development or growth impacts of city–county consolidation. A large related literature exists that looks at the effect of government consolidation on governmental costs (Allers and Geertsema 2016; Blesse and Baskaran 2016; Fahey et al. 2016; Swianiewicz and Łukomska 2019), expenditures (Roesel 2017), and political power (Harjunen et al. 2019).

  9. 9.

    For additional details on the referendum, see Bacot (2004).

  10. 10.

    “Cooperative Endeavor Agreement Between the Lafayette City-Parish Consolidated Government and the Lafayette Parish School Board Concerning the Sharing of Costs Associated with the Alteration of District Boundaries”, see, accessed on May 20, 2019.

  11. 11.

    US Census Bureau,, accessed on May 20, 2019.

  12. 12.

    US Census Bureau,, accessed on May 20, 2019.

  13. 13.

    Another trade-off with donor pool selection is between having an appropriate comparison group and potential spillover effects (Gobillon and Magnac 2016). Areas adjacent to the treated region are natural controls, but could experience spillover effects from the treated region. For example, if consolidation leads to better economic development prospects, businesses and residents in surrounding counties could move to the newly consolidated jurisdiction. That effect would violate the SCM’s assumption that the shock affects the treated region only. Thus, following Pfeifer et al. (2018), who drop from the donor pool municipalities directly adjacent to their treated regions, in addition to our main results, we also conduct an analysis with counties adjacent to the consolidated counties excluded from the donor pool. The results are comparable.

  14. 14.

    Although the consolidated government was not fully functional until 1996, the year of the referendum (1992) is used as the treatment year because between 1992 and 1996, a transition to the consolidated government was underway.

  15. 15.

    Placebo tests using absolute difference are similar and available for all consolidation cases in Appendix 1.

  16. 16.

    For a detailed discussion of the politics and implementation of consolidation, see Bacot (2004).

  17. 17.

    Appendix Figs. 16 and 17 show the absolute difference placebo results.

  18. 18.

    We are indebted to a referee for highlighting this point for us.

  19. 19.

    The US Census defines rural areas as places with fewer than 2500 inhabitants.

  20. 20.

    In previous figures, the vertical line represented both the treatment year and the year of the consolidation referendum. However, with the rural to total population ratio available only every 10 years, the vertical line now represents the year of the consolidation referendum. The first treatment year for each case is 2000.


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We thank seminar participants at West Virginia University. The authors would also like to thank Kate Nesse, Brigid Tuck, Douglas Walker, and other participants at the 2018 Mid-Continent Regional Science Association meetings. Russell Sobel, Dwight Lee, John Garen, Randy Holcombe, J.R. Clark, Edward Lopez, and participants at the 2017 Southern Economic Association Conference. Justin Ross and graduate students at Indiana University provided important feedback. The authors received no specific funding for this research from any source. All three authors receive general research support for conference presentations and submission fees from the Center for Free Enterprise at West Virginia University.

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Appendix 1: Placebo tests with absolute difference

See Figs. 1314151617.

Fig. 13

Placebo tests of Lafayette Parish

Fig. 14

Placebo tests of Clarke County

Fig. 15

Placebo tests of Richmond County

Fig. 16

Placebo tests of Clarke County (11 County Donor Pool)

Fig. 17

Placebo tests of Richmond County (11 county donor pool)

Appendix 2: Urban–rural divide

See Fig. 18 and Tables 121314

Table 12 Rural population ratio predictor balances for Lafayette County
Table 13 Rural population ratio predictor balances for Clarke County
Table 14 Rural population ratio predictor balances for Richmond County
Fig. 18

Rural to total population ratio synthetic control analysis. (Top row: Lafayette Parish synthetic control analysis and its placebo test; middle row: Clarke County synthetic control analysis and its placebo tests; bottom row: Richmond County synthetic control method and its placebo tests)

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Hall, J.C., Matti, J. & Zhou, Y. The economic impact of city–county consolidations: a synthetic control approach. Public Choice 184, 43–77 (2020).

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  • Local governments
  • City–county consolidation
  • Development
  • Synthetic control method

JEL Classification

  • H11
  • H70
  • R11
  • R58