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Road maintenance over the local election cycle

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Abstract

Despite recognition that government officials have politically motivated incentives to pursue new infrastructure construction at the expense of infrastructure upkeep, no prior research directly addresses how political incentives affect road maintenance separate from road construction. This paper investigates how local political incentives affect local road maintenance using unique data on completed road maintenance projects and difference-in-differences which leverages exogenous timing of mayoral elections. Since residents complain about damaged roads and can also be frustrated by travel delays caused by road maintenance, it is theoretically ambiguous how elected officials manipulate road maintenance, assuming they do so for political purposes. We show local election cycles shift road maintenance timing and location. We provide evidence that maintenance follows different patterns in mayoral election years and that maintenance is shifted around sub-city geographic units based on those units’ political similarity of registered voters. A mayoral election costs $90,623 in additional road-related costs, translating to $28,093,182 per election for large cities.

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Notes

  1. A bridge recently collapsed here, shortly before President Joe Biden was expected to have a press conference on Infrastructure Week, making it a relevant context to examine this issue (CNN, 2022).

  2. Due to the multi-year nature of most construction projects, we cannot use road construction in our causal research design. Also, road construction is potentially less salient to voters compared to road maintenance if the new construction occurs in less populated places.

  3. See Figure C.2 for more detail.

  4. There are several reasons why road construction might be favored by politicians compared to road maintenance. One reason is that building new infrastructure is less complicated to complete and easier to finance. Second, new construction projects tend to be more popular with the public.

  5. There is a significant decrease in crashes when maintenance is shifted out of areas in mayoral election years. Areas that receive more maintenance right before the mayoral election see an increase in major injuries from crashes.

  6. Most cities, if they publicly provide data on road maintenance in the first place, only record what maintenance projects are currently in progress. Historical road maintenance data is rarely available.

  7. We summarize political competition for mayoral positions in large cities in the United States in Table C.2. Of 46 large cities, 14 have had no Republican mayors, like Pittsburgh, since 1960. The average number of Republican mayors is 1.82 and the standard deviation is 2.08. There are 4 cities that are 2 standard deviations above the average number of Republican mayors. The effects might be larger in cities with greater political competition.

  8. The road maintenance data for the city of Pittsburgh, Pennsylvania is publicly available via the Western Pennsylvania Regional Data Center (https://data.wprdc.org/dataset/paving-schedule). From 2009–2017, there are 5978 completed maintenance projects recorded. Since the start date is not observed, we use the ending date to assign projects to time periods.

  9. For instance, milling and overlay is the process of drilling down and then laying down a new road, whereas AC (asphalt concrete) overlay does not involve drilling down first. The drilling and pouring are likely more disruptive than pouring alone. The main analysis focuses on the heaviest types of maintenance and the results are not sensitive to the inclusion of types of maintenance that are less disruptive.

  10. For example, during the “lame-duck” period between November 2013 and January 2014, there were only 5 maintenance projects completed.

  11. These data are publicly available in every year of the sample from the Allegheny County Election Results website at https://www.alleghenycounty.us/elections/election-results.aspx.

  12. Glaeser and Shleifer (2005) show mayors act according to political incentives even in the absence of fierce political competition using several case studies. In Boston in the early 1900 s, James Curley was infamous for creating a non-competitive electoral environment by providing transfers to the type of people who supported him and not helping areas of the city where his political opposition lived. Even after Boston became less competitive as Curley’s opposition began to move to the suburbs because of these policies, Curley still engaged in politically motivated transfers and policies that favored his supporters.

  13. Since \({\bar{R}}_{w}\) depends on all the years of data, the mayor does not directly observe it. Nonetheless, it is a useful way to operationalize the idea that mayors have a sense of whether wards are similar.

  14. Furthermore, only four of these are heavy projects. It is highly unlikely that these have any impact on the results. December 2017 is dropped because the majority of these projects do not finish until after the sample period ends.

  15. Consider a road that crosses a ward boundary, ignoring for the time the role of political similarity. If it is the same street, road (lanes, traffic, age, etc.), and area (zoning, building, residents, etc.) characteristics are likely to be similar for road segments near the boundary on opposite sides. Some exceptions might be when the road is used as the boundary or when there is a river between wards. In the absence of political incentives mattering, road segments on either side of a ward boundary, where political similarity changes, would be equally likely to be maintained. A tell-tale sign that political incentives do matter, and how classic regression discontinuity approaches proceed, would be if a flexible regression line were fit through points on either side and the fitted lines were not equal at the ward boundary (eg. Lee, 2008).

  16. Our context has limitations that make us doubt using a traditional RD approach as an inferential analysis for our main estimates. One limitation is that the voters that politicians depend on move across ward boundaries. Another is the relatively rare occurrence of maintenance on the universe of local roads.

  17. The population of Pittsburgh-owned roads from the Western Pennsylvania Regional Data Center is used to format data this way which results in 2.936 million unique 1-meter segments.

  18. As is common in discontinuity-based approaches in which units right near the cutoff may be different than the population (e.g. Dague et al. 2017), segments within 10 ms of the border are dropped. This is done because often roads are used as ward boundaries as shown by Figure A.2. Also, if a 1-road meter segment has portions in both wards, dropping near segments removes this source of measurement error.

  19. The underlying treatment variable is continuous, which is a developing literature (Callaway et al., 2021) for difference-in-differences. Dichotimizing a continuous treatment variable is a standard approach for difference-in-differences.

  20. Unlike a typical context in which some geographic area enacts legislation, the post period is not indefinite, ending after the election is held.

  21. This is a straightforward implication of reducing unemployment.

  22. We are reticent to impose homogeneous effects of similarity during the 5 months of the IEP, because maintenance timing could be shifted.

  23. We do not claim that political similarity is randomly assigned, and this is not required to estimate a causal effect. Random assignment or treatment ignorability would be required for an average treatment effect estimand, but this assumption is too strong in this context. Instead, this strategy should be seen as seeing using exogenous election cycle timing, like prior literature in this area, to investigate how political incentives affect areas that differ in their political similarity under a parallel trends assumption.

  24. The month and year fixed effects are colinear with the binary variables in this specification and therefore cannot be estimated. Columns 2 and 4 remove the month and year FEs so all the interactions can be estimated. The results are not sensitive to which specification is used.

  25. We use further disaggregated categories in Figure A.5 and Figure A.6, but there are not enough projects for reliable inference in many cases. Similar conclusions emerge that the composition of maintenance is unaffected by political incentives.

  26. Randomization inference is commonly attributed to Fisher (1935). It is suggested as a fix by Bertrand et al. (2004). Applications include: Cunningham and Shah (2018) and Buchmueller et al. (2011).

  27. The non-binned scatterplots can be seen in Figure A.7. The conclusions are the same.

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Authors and Affiliations

  1. Department of Economics, Goucher College, 1021 Dulaney Valley Road, Baltimore, MD, 21204, USA

    Margaret Bock

  2. Dyson School of Applied Economics and Management, Cornell University, Ithaca, New York, 14850, USA

    Benjamin Blemings

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  1. Margaret Bock
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  2. Benjamin Blemings
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Bock, M., Blemings, B. Road maintenance over the local election cycle. Public Choice 198, 129–151 (2024). https://doi.org/10.1007/s11127-023-01115-3

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