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Consolidated city–county governments and economic stability

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Abstract

With economic development as a primary aim of city–county consolidations, a theoretical and empirical literature explores the effect of consolidation on economic conditions. However, despite the economic development effects arguably being most relevant during economic crisis, no studies have focused on consolidation’s effects during recession. Using county-level data from 13 states across the United States, we consider how consolidation influences economic stability during and after the Great Recession. After controlling for demographic, economic, and geographic factors, including potential spillover effects, the results suggest that consolidation does not promote stability for either employment, the unemployment rate, per capita income, or number of business establishments. These null effects are not influenced by government sector employment or driven by results from any particular state. The paper’s findings caution against local governments pursuing city–county consolidation in hopes of greater economic stability.

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Notes

  1. For reviews of the local government consolidation literature, including the economic development effects, see Kim and Jurey (2013) and Hall et al. (2018).

  2. While there is debate on the precise number of consolidated city–county governments, the number is small relative to the over 3000 counties or county equivalents in the US. For example, Hall et al. (2020) puts the number at 41 as of 2019.

  3. Outside of the US, at least two papers consider government related variables beyond government sector employment. Mazzola et al. (2018) examine public-private capital investments while (Ezcurra and Rios 2019) focus on the effects of government quality.

  4. Although the results in Deng et al. (2021) illustrate the concept of spatial spillovers, it is important to note that city–county consolidations in China and the US function at different geographic levels. For a helpful explanation of the Chinese context, see Tang and Hewings (2017).

  5. Appendix A shows the results using a SDEM specification, and the results are similar in sign, magnitude and statistical significance to those presented in the main results.

  6. Rescaling by a factor of 100 is done to avoid meaningless zeros as coefficients in the regression tables. It has no influence on the results.

  7. The 2007–2013 time period aligns with Deller et al. (2017), and the coefficient of variation measure of stability is similar to the variance to mean ratio used in Deller and Watson (2016a, b), and Deller et al. (2017). These measures are cross-sectional, reflecting instability for the entire time period, not instability that differs by year.

  8. One concern with the analysis is using a dummy variable as an indicator for consolidation. While some consolidations occurred in the early to mid 2000s, others are from the 1970s or even earlier. The influence of consolidation on Great Recession economic stability may depend on when consolidation occurred. Therefore, we present in Appendix C the results including both the number of years since consolidation and the number of years squared in lieu of the consolidation dummy. The results remain similar in terms of statistical significance.

  9. https://www.nlc.org/resource/cities-101-consolidations/.

  10. There are four consolidation cases which occurred at or after the sample start period in 2007: Bibb County, GA; Echols County, GA; Webster County, GA; and Greeley County, KS. In the main analysis, these counties are included in the treatment. However, as a robustness check, we re-run the analysis but with these counties dropped. The results are similar and reported in Appendix D.

  11. The Herfindahl index is calculated as \(\sum _{s=1}^{S_{i}} (\frac{e_{si}}{e_{i}})^{2}\) where \(S_{i}\) is the total number of industries in county i, \(e_{si}\) is the total number of establishments of industry s within county i, and \(e_{i}\) is the total number of establishments within county i. Smaller numbers of the index reflect a more diversified local economy.

  12. LeSage and Pace (2014a) argue that the specification of the weight matrices should not have large impact on estimates and inferences.

  13. The one exception is with the rural–urban continuum code. Since the rural–urban continuum code is only updated every 10 years, we use figures from 2003, the closest reporting year to 2007.

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Authors and Affiliations

  1. Department of Economics, School of Business, Georgia Gwinnett College, 1000 University Center Lane, Lawrenceville, GA, 30043, USA

    Josh Matti

  2. Lutgert College of Business, Regional Economic Research Institute, Florida Gulf Coast University, 10501 FGCU Boulevard South, Fort Myers, FL, 33965, USA

    Amir B. Ferreira Neto

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  1. Josh Matti
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  2. Amir B. Ferreira Neto
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Correspondence to Josh Matti.

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Appendices

Spatial Durbin model estimation

See Table 7.

Table 7 Regression results for US county economic instability: spatial durbin error models
Full size table

Single state estimations

See Tables 8 and 9.

Table 8 Regression results for US county economic instability models for single states: SLX models
Full size table
Table 9 Regression results for US county economic instability models for single states: OLS models
Full size table

Time since consolidation

See Table 10.

Table 10 Regression results for US county economic instability: age of consolidated counties
Full size table

Consolidation during period of analysis

See Table 11.

Table 11 Regression results for US county economic instability: no consolidation during period of analysis
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Matti, J., Neto, A.B.F. Consolidated city–county governments and economic stability. Econ Gov 24, 263–286 (2023). https://doi.org/10.1007/s10101-022-00273-2

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